The AI Craze Archives - Page 2 of 3 - upGrad Campus

Data Science vs. Machine Learning: Which Course is Right for You?

Table of Contents:

Machine learning and data science are two fields that are making significant waves in today’s data-driven landscape. While still in their infancy, both of these domains are central for professionals aspiring to leverage the power of data. The future of both these fields is extremely prosperous, with advancements and breakthroughs happening on a daily basis. 

However, even though both these fall under the same umbrella, it is important to remember that they have distinct focuses and applications. Pursuing a data science course or a machine learning course will lead to two distinct career outcomes. This makes it essential to understand the differences between the two domains so that you can select the right path based on your personal interests and career aspirations. 

In this blog, we’ll discover what both these courses cover so that you can make an informed decision based on their unique needs.

Difference Between Data Science Courses and Machine Learning Courses

Data science courses equip students with the ability to manipulate huge swaths of collected consumer data. Students are taught how to operationalize and investigate data by processing it. This is an important component to master for future data scientists, as it helps them derive valuable insights into emerging trends and hidden patterns which can be used to extract some meaning. 

Machine learning courses fall under the branch of artificial intelligence. Conversely, machine learning courses are devoted towards understanding and formulating different ways of utilizing Big Data. Students are taught how to enhance a system’s overall operational performance as well as make future predictions. 


Important Skills Developed in Data Science Courses 


Important Skills Developed in Data Science Courses

Data science courses in India are usually offered with opportunities for volunteers and placements at the end so that students can supplement their theoretical learning with real-world experience. This is quite helpful as it allows students to partake in experiential learning outside the classroom. 

Some of the key skills that are honed during these data analyst courses with placement are:

  • Hypothesis testing and data visualization
  • Coding Skills such as Python/R
  • Data wrangling
  • General domain expertise and problem-solving skills
  • Methods of data collection and how to process this data
  • Introduction to statistical analysis

Career Opportunities in Data Science 

Data Science is a field that has yet to reach its full potential, with businesses still grappling with understanding the sheer scope and power of this tool. As companies shift towards a more data-driven approach to corporate decision-making, data scientists will be in increasingly high demand.

There are multiple industries that data scientists can work in, such as finance, marketing, e-commerce, and healthcare, just to name a few. Additionally, they can take up various different projections according to their specializations, such as fraud detection, customer segmentation, healthcare analytics, and optimizing supply chains. Thus, data science courses offer a wide array of career opportunities for data scientists. 

Key Skills Developed in Machine Learning Courses 

Key Skills Developed in Machine Learning Courses

Similar to data science courses, machine learning courses also teach students some unique and incredibly helpful skills that they can harness to excel in their careers. Some examples of the key skills they stand to develop in machine learning courses are:

  • Programming skills such as Java, SQL, Python
  • Prototyping
  • Data modelling
  • How to write algorithms for supervised, unsupervised, and reinforcement learning
  • Introduction data processing required for model training
  • Skill in feature engineering
  • Model evaluation and optimization
  • General implementation and real-world applications. 

Career Opportunities in Machine Learning 

Machine Learning courses produce ML engineers and researchers who are highly sought after by startups, tech giants, and research institutions alike. Machine Learning engineers can leverage their skills in numerous projects, such as building recommendation systems, natural language processing, image recognition, and autonomous vehicle systems. 

Machine learning and data science are both incredibly valuable fields with tremendous growth potential. Pursuing either course will guarantee a promising and fulfilling career. The choice will ultimately depend on your strengths, interests and career aspirations.

Head over to upGrad Campus and enter the world of Data Science or AI. We offer courses in both these fields so that no student is left behind. Speak to our representative to understand more about our machine learning course fees, data science course fees and duration, eligibility criteria, and prerequisites. With upGrad Campus, gain mastery over all the skills you require to be an industry leader in data science or machine learning!

Top 5 Entry-Level Machine Learning Job Roles for Freshers

Table of Contents

In the era of rapid technological advancement, Machine Learning has emerged as a driving force, reshaping industries and revolutionising the way we interact with data. For fresh graduates and aspiring tech enthusiasts, the world of Machine Learning offers an array of opportunities to dive into.

This comprehensive blog serves as a guide, introducing the top five entry-level Machine Learning Job Roles that provide a springboard for enthusiastic freshers to venture into the field of Machine Learning.

Overview of Entry-Level Positions in the Machine Learning Field

Overview - Entry Level Positions In Machine Learning Field

The landscape of machine learning job roles spans a wide spectrum, catering to a diverse set of skills, interests, and academic backgrounds. The following five entry-level positions capture the diversity of options available within the field of Machine Learning:

1. Machine Learning Engineer</h3

Machine Learning Engineers stand at the intersection of Data Science and Software Engineering, crafting and deploying Machine Learning models and systems. Their role involves transforming raw data into actionable insights, serving as a vital bridge between Data Scientists and Software Engineers. 



  • Should be able to implement the best image processing techniques as required.
  • Knowledge of Edge detection, object detection, image classification, image segmentation with the help of OpenCV & Python is a must.
  • Working knowledge of Python Web Frameworks such as Django, FastAPI.
  • Knowledge of libraries such as TensorFlow, Keras, Matplotlib, and Scikit-Learn.

The toolbox of a Machine Learning Engineer often includes indispensable tools like TensorFlow, PyTorch, and Scikit-learn.

2. Data Scientist

A Data Scientist is a professional who uses a combination of skills in data analysis, statistics, programming, and machine learning to extract valuable insights and knowledge from large and complex datasets. Data Scientists play a crucial role in various industries by utilising their expertise to interpret data, discover patterns, and make predictions or informed decisions.



  • Proficiency in programming languages like Python or R is a must-have skill for Data Scientists.
  • A strong understanding of statistics is vital, it includes knowledge of concepts like hypothesis testing, regression analysis, and probability distributions.
  • Familiarity with machine learning algorithms and techniques. They need to know how to select appropriate algorithms, preprocess data, train models, and evaluate their performance.
  • One of the primary responsibilities is to analyse and model data, this involves cleaning and preprocessing raw data, conducting exploratory data analysis (EDA) to understand patterns and relationships, and building predictive models using machine learning algorithms.

For tools, libraries like Pandas and Matplotlib are essential to weave stories from data.

3. AI Research Assistant

AI Research Assistants operate behind the scenes, playing a pivotal role in advancing AI through research projects and experiments. They design experiments, collect and analyse data, and fine-tune algorithms. 



  • Proficiency in programming languages such as Python, Java, or C++ is essential, they need to write code to implement and experiment with different algorithms, models, and solutions.
  • A solid understanding of mathematics, particularly linear algebra, calculus, and probability theory, is crucial for AI research.
  • AI research often involves tackling complex problems, and should have strong problem-solving skills and the ability to think critically.
  • One of the primary responsibilities is to contribute to the design and execution of experiments, it involves planning and setting up experiments to test hypotheses, algorithms, or models related to AI. 
  • Tasked with staying updated on the latest developments and breakthroughs in the field of AI, they conduct thorough literature reviews to understand existing research, identify gaps, and explore potential research directions.

An AI Research Assistant should be familiar with tools like Python and its libraries like TensorFlow or PyTorch, Git for version control, and libraries like NLTK and spaCy for natural language processing. 

4. Junior Data Analyst

Junior Data Analysts are the detectives of the data world, deciphering patterns and insights that guide strategic decision-making. They excel in data cleaning, transformation, and visualisation techniques. 



  • Proficiency in data analysis techniques using tools like Excel, SQL, and data visualisation libraries (such as Tableau, Power BI, or matplotlib). 
  • Strong critical thinking skills are essential for interpreting data, identifying patterns, and drawing meaningful conclusions.
  • Precision is key in Data Analysis, you need to pay attention to details, spot discrepancies or errors in datasets, and ensure accuracy in your work.

Junior Data Analysts are responsible for cleaning, preprocessing, and transforming raw data into a usable format. They analyse data to identify trends, patterns, and insights that contribute to informed decision-making.

5. Machine Learning Intern

Machine Learning Internships serve as gateways for budding machine learning enthusiasts to gain hands-on experience. These internships provide opportunities to translate theoretical knowledge into practical applications. 



  • ML interns should be comfortable with coding, debugging, and implementing machine learning algorithms and models.
  • Familiarity with data preprocessing, cleaning, and analysis techniques using libraries like pandas and NumPy, come in handy.
  • Basic understanding of Machine Learning concepts, algorithms, and techniques, should know how to build and train simple models, evaluate their performance, and understand the underlying principles. 
  • Contribute to data collection from various sources and assist in preprocessing tasks, including cleaning, formatting, and transforming raw data into a suitable format for model training.
  • Participate in building and training Machine Learning models under the guidance of experienced professionals, assist in selecting features, experimenting with different algorithms, and evaluating model performance using appropriate metrics.

Machine Learning Interns should be familiar with tools like Python for coding and libraries like scikit-learn, TensorFlow, or PyTorch for building models. Knowledge of Jupyter Notebooks, pandas, and Data Visualisation tools aids in experimentation, analysis, and presentation of results.

Common Skills and Educational Backgrounds for Freshers

Common Skills and Educational Backgrounds For Freshers

While the precise requirements may vary across roles, certain skills and educational backgrounds remain universally valuable. A robust grasp of programming languages like Python, complemented by familiarity with machine learning algorithms, serves as the foundation. Proficiency in data manipulation and analysis techniques is essential.

An academic background in Computer Science, Data Science, Mathematics, or related fields equips individuals with the necessary tools to excel in these roles.

Trends and Future For Entry Level ML Careers

As the field of Machine Learning evolves, new trends are shaping the landscape of entry-level job roles. The integration of Machine Learning across diverse industries, coupled with the surging demand for automation, is creating a surge of opportunities for freshers.

The emphasis on ethical considerations in AI and Machine Learning is also growing, resulting in the rise of roles centred around AI ethics and fairness. Staying attuned to these trends ensures that individuals remain competitive in this dynamic field.


In a world where data-driven insights drive innovation, entry-level Machine Learning roles offer a gateway to a thrilling and fulfilling career. Whether your passion lies in constructing intricate models or unravelling insights from data, these roles cater to diverse interests. By honing essential skills, pursuing relevant educational backgrounds, and staying abreast of industry trends, freshers can set themselves on a trajectory toward a rewarding journey in the captivating realm of Machine Learning.

If you want proficiency in Machine Learning then upGrad Campus online Machine Learning Certification Course is a comprehensive program designed to learn all in-demand skills in machine learning.

Through interactive modules, real-world projects, and industry insights, participants gain hands-on experience in Python programming, data preprocessing, model building, and deployment. The course provides a solid foundation in algorithms like regression, clustering, and neural networks. With upGrad Campus’s expert guidance, students acquire the expertise needed to excel in the evolving field of Machine Learning and secure promising career opportunities.

AI Jobs in Financial Services: Exploring Lucrative Opportunities for Junior Hires

Table of Contents


In recent years, the integration of Artificial Intelligence (AI) in the financial services industry has not only transformed traditional business models but also created a surge in demand for skilled AI professionals. The allure of high-paying positions in this domain has attracted both seasoned experts and junior talent, with some reports suggesting that junior hires are now being offered salaries of 350,000 USD.

This blog delves into the reasons behind the substantial compensation packages for junior AI professionals in financial services, the skills required to thrive in such roles, and the potential future trends in this dynamic field.

The AI Revolution in Financial Services

The AI Revolution in Financial Services

The financial services industry has undergone a shift, largely driven by AI technologies. From algorithmic trading and fraud detection to credit assessment and customer service automation, AI has proven its potential to revolutionise traditional banking and investment practices. This transformation has led to an increased demand for AI experts who can design, develop, and deploy sophisticated solutions that not only streamline operations but also improve decision-making processes.

As financial institutions recognise the competitive advantage AI brings, they are willing to invest significantly in talent acquisition to stay ahead in the game.

The Junior Hire Compensation Landscape

The Junior Hire Compensation Landscape

The eye-catching figure of 350,000 USD for junior hires in AI roles within financial services is indeed attention-grabbing. However, it’s crucial to understand the factors contributing to this seemingly extravagant compensation. One key factor is the shortage of skilled AI professionals in the job market.

As the demand for AI experts far exceeds the supply, organisations are faced with the challenge of attracting and retaining top talent. To this end, they are resorting to lucrative compensation packages to entice candidates with the right skills and potential to drive innovation and growth.

Moreover, the complexity and sensitivity of financial data require a high level of expertise to ensure the responsible and ethical use of AI. Junior hires are expected to work alongside senior professionals, and their contributions directly impact the organisation’s success. Therefore, the hefty compensation is often a reflection of the responsibilities they shoulder and the level of expertise they are required to bring to the table.

Essential Skills for Junior AI Professionals in Finance

Essential Skills for Junior AI Professionals

1. Technical Proficiency: A strong foundation in machine learning, deep learning, natural language processing, and data analysis is essential for success in AI roles. Junior hires need to be proficient in programming languages like Python and have experience with frameworks such as TensorFlow and PyTorch.

2. Domain Knowledge: Understanding the intricacies of the financial services sector is critical. Junior AI professionals must comprehend the nuances of financial markets, risk management, compliance regulations, and customer behaviours to develop effective AI solutions.

3. Problem-Solving: Financial institutions often face complex challenges that require creative problem-solving. Junior hires should be adept at breaking down problems, devising innovative solutions, and implementing AI-driven strategies to address them.

4. Collaboration: Working in cross-functional teams is common in financial services. Junior AI professionals need to collaborate with experts from various fields, such as finance, compliance, and legal, to ensure the successful integration of AI solutions.

5. Ethical Awareness: Given the sensitivity of financial data, ethical considerations are paramount. Junior hires must understand the ethical implications of AI in financial services and ensure compliance with industry regulations.

Exploring Innovative AI Projects in the Realm of Financial Services

Exploring Innovative AI Projects

Before we delve into how freshers can go about acquiring the above skills, let’s take a look at the current financial landscape integrating AI in their everyday functions:

1. AI-driven Fraud Detection: Real-time analysis of transactions to identify unusual patterns and potential fraud, enhancing financial security.

2. Algorithmic Trading Automation: Utilising AI to execute trades based on predefined strategies, reducing human biases and improving investment outcomes.

3. Enhanced Credit Scoring: Incorporating alternative data sources and machine learning models for accurate credit assessments, expanding access to financial services.

4. Personalised Robo-Advisors: AI-powered platforms offering tailored investment advice and portfolio management, making investing more accessible and cost-effective.

5. Customer Service Chatbots: Using natural language processing to provide 24/7 support, quick responses, and improved customer interactions.

Real-world Examples of AI Implementations

Real-world Examples of AI Implementations

Artificial Intelligence (AI) has made significant strides in reshaping India’s financial services landscape, driving efficiency, improving customer experiences, and enabling innovative solutions. Here are noteworthy real-world examples of AI implementations in financial services across India:

1. ICICI Bank:

AI-Powered Chatbot: ICICI Bank’s chatbot, “iPal,” uses natural language processing to assist customers with queries, provide account information, and even process transactions, enhancing customer engagement.

Voice Recognition: The bank employs AI-driven voice recognition for secure and convenient customer authentication during phone banking interactions.

2. HDFC Bank:

Trade Assist: HDFC Bank utilises AI-powered platforms to offer personalised trading advice to customers, aiding them in making informed investment decisions.

EVA: The bank’s AI-based platform, Electronic Virtual Assistant (EVA), assists customers with tasks such as balance inquiries, fund transfers, and bill payments through voice and text interactions.

3. Paytm:

Fraud Prevention: Paytm employs AI algorithms to detect and prevent fraudulent activities, ensuring secure transactions for millions of users.

Personalised Financial Services: The platform uses AI to analyse user data and offer tailored financial products, such as investment options and insurance plans, based on individual preferences.

Future AI Trends and Opportunities

The landscape of AI in financial services is continually evolving, opening up exciting prospects for junior professionals entering the field, such as:

1. Explainable AI: As AI systems become more sophisticated, the need for transparency and interpretability grows. Junior hires who can develop AI models that offer clear explanations for their decisions will be in high demand.

2. Regulatory Compliance: The financial sector is heavily regulated, and AI solutions must adhere to these regulations. Junior professionals with expertise in developing compliant AI systems will have a competitive edge.

3. Personalised Customer Experience: AI is being leveraged to provide personalised financial advice and services. Junior hires who can create AI-driven solutions that cater to individual customer needs will find themselves at the forefront of innovation.

4. Risk Assessment: AI can enhance risk assessment models, leading to more accurate predictions. Junior professionals who can contribute to developing robust risk assessment algorithms will be highly sought after.

5. Cybersecurity: With the integration of AI comes the risk of cyber threats. Junior AI experts with a focus on cybersecurity will be integral in safeguarding financial institutions from potential breaches.

AI Certification and Training Programs

AI Certification Course

As the world advances into the digital era, the demand for professionals skilled in Artificial Intelligence (AI) is soaring. Recognising this need, upGrad Campus AI Certification Course is designed to equip students with essential AI knowledge and practical skills.

The upGrad Campus AI Certification Course is a comprehensive learning journey that demystifies the complexities of AI and provides students with training programs and a solid foundation. The course caters to both beginners and those with a basic understanding of AI, making it accessible to a wide range of students.


Conclusion for AI Certification Course

The allure of high-paying AI jobs in financial services is a testament to the industry’s recognition of the pivotal role AI plays in shaping its future. Junior hires stand to benefit not only from the attractive compensation packages but also from the opportunity to contribute to transformative projects, collaborate with seasoned professionals, and influence the trajectory of an evolving field.

As AI continues to reshape the financial landscape, aspiring junior AI professionals can look forward to a rewarding career that combines technical prowess with financial acumen, ethical considerations, and innovative problem-solving.

The Future of Advertising: Transforming Advertising with Generative AI

Table of Contents


The advertising landscape is undergoing a profound transformation, driven by technological advancements that have reshaped the way brands interact with consumers. One such technology that is set to revolutionise advertising is Generative Artificial Intelligence (AI) in advertising with the market size expected to reach $15.7 trillion by 2030. Emerging as a game-changer, this technology has the power to transform advertising campaigns by offering innovative ways to engage customers and deliver personalised experiences unlike traditional advertising methods.

This blog delves into the ways generative AI has transformed marketing and advertising.

Understanding Generative AI

Understanding Generative AI

Generative AI is a class of artificial intelligence that enables machines to create content autonomously, imitating human creativity and intuition. Unlike traditional AI systems, which are trained on existing data to perform specific tasks, generative AI relies on deep learning models to generate original content.

This breakthrough technology has sparked immense interest across various industries, and advertising is no exception.

Impacts of Generative AI in advertising

Impacts of Generative AI in Advertising

1. Personalised Advertising

One of the most significant impacts of generative AI in advertising is the ability to create highly personalised content. By analysing vast amounts of consumer data, AI algorithms can understand individual preferences and behaviours, allowing brands to tailor advertisements with increased personalization, improved engagements, reduced costs and framing new creative possibilities.
Here are some examples of how generative AI is being used in personalised advertising today:

  • Google Ads: Google Ads uses generative AI to create personalised ad copy based on search terms of individual users.
  • Facebook: Facebook or Meta uses generative AI to create personalised ad images based on interests of individual users.
  • Amazon: Amazon uses generative AI to create personalised product recommendations based on past purchase history of individual users.

2. Efficient Content Creation

Traditional content creation processes are often time-consuming and resource-intensive. Generative AI streamlines the process by automatically generating various advertising materials, such as images, videos, and even copy. Here are some examples of efficient content creation today:

  • Midjourney: Midjourney uses generative AI to create a variety of personalised content including blog posts, social media posts, and even email campaigns for brands.
  • Copysmith: Copysmith uses generative AI to create high-quality content, such as ad copy, product descriptions, and marketing emails.
  • Creatopy: Creatopy uses generative AI to create images, videos, and even infographics.

3. Hyper-Relevant Advertisements

Generative AI continuously analyses user interactions and feedback, ensuring that advertisements are always relevant and up-to-date. Using the latest consumer sentiments, AI-powered ads can adapt in real-time and allow brands to stay relevant in an ever-changing market.
Here are some of the challenges that need to be addressed in order to realise the potential of generative AI in hyper-relevant advertisements:

  • Bias: Generative AI models can be biased, leading to the creation of discriminatory or offensive ads that need to be addressed on priority.
  • Privacy: Advertisers need to be transparent and address the privacy concerns of users while using Generative AI models.
  • Regulation: Governments are starting to regulate the use of generative AI in advertising ensuring ethical and responsible use.

Seamless Integration with AR and VR

Seamless Integration with AR and VR

Augmented Reality (AR) and Virtual Reality (VR) have already shown tremendous potential in the advertising space, providing immersive experiences for consumers.

Here are some specific examples of how generative AI could be used to seamlessly integrate with AR and VR:

  • Creating realistic avatars: This could be used for training, education, or even just for entertainment.
  • Creating dynamic virtual environments: This could be used for games, simulations, or even just for exploring a whole new world.
  • Generating personalised content: This could include personalization like product recommendations, educational content, or even entertainment experiences.

Ethical Considerations and Privacy Concerns

Ethical Considerations and Privacy Concerns

Generative AI is a powerful technology with the potential to be used for good or for bad. Striking a balance between creative freedom and responsible use of AI-generated content will be crucial in maintaining consumer trust and safeguarding against potential misuse.

Some of the privacy concerns that need to be taken into account when using generative AI include:

  • Data collection: Generative AI models require a large amount of data to train, which raises privacy concerns.
  • Data ownership: The ownership of data used to train generative AI models is a complex issue.
  • Data security: Generative AI models can be vulnerable to cyberattacks. So, how to secure data and protect it from unauthorised access while using generative AI is an important issue to be addressed.



Q. Will Generative AI replace human creativity in advertising?

A. Generative AI complements human creativity rather than replacing it. While AI can automate content generation and provide valuable insights, human creativity remains essential in shaping advertising strategies, crafting unique brand stories, and connecting emotionally with consumers.

Q. Give a real-world example of Generative AI in Advertising.

A. Nike, a global leader in athletic footwear and apparel, has been at the forefront of incorporating innovative technologies into its marketing campaigns. In 2019, Nike launched the “Create with Air” campaign, which showcased the power of Generative AI in the world of advertising.

Q. Can Generative AI create different types of advertising materials?

A. Yes, Generative AI is versatile and can create various advertising materials, such as images, videos, and even copy. Its ability to generate diverse content enables marketers to maintain a consistent brand presence across multiple platforms and channels.


Conclusion For Artificial Intelligence Course

Generative AI is revolutionising the marketing and advertising industry, paving the way for a more personalised, efficient, and relevant approach to marketing. Moving into the future, embracing generative AI’s potential while remaining vigilant about its implications will be vital in transforming advertising into a more engaging, user-centric, and innovative realm.

If you want to excel in the fields of Artificial Intelligence Generative AI, then upGrad Campus online certification courses are the perfect platform for you to gain the relevant skills.

Harnessing the Potential of Artificial Intelligence in Marketing Campaigns

Table of Contents

In today’s digital landscape, every single second, users create and consume an infinite flow of data and information: using phones to update social media status, submitting reviews of products, or asking Alexa to play a specific song or to find the best deal of an item.

These pieces of data can be quite valuable for businesses as it helps them to learn more about current and potential customers – what they like and dislike, what their interests are and what they do. These kinds of information help organisations learn more about their existing and potential consumers, including what they like and dislike, their hobbies, and what they do. However, a key challenge remains how to extract insights from this constant stream of data that is growing so rapidly?

A possible answer is ‘artificial intelligence’ (AI).

In this blog post, we will explore the various ways in which generative artificial intelligence can be harnessed to elevate marketing campaigns to new heights.

What Makes “Artificial Intelligence” so Intelligent

What Makes “Artificial Intelligence” so Intelligent

There are two key elements that make AI well-suited to deal with the vast volume and variety of data.

First, AI can make sense of the large amount of non-numerical data, such as everyday language consumers use in written (social media posts, product reviews) or in spoken content (videos, real-time conversations). This is ‘natural language processing’, aka NLP, and allows AI to understand what was said, how it was said and the context in which the words or phrases were used.

For example, AI can understand whether a review post discussed apple (the fruit) or Apple (the tech firm) and also discern the sentiment and emotions expressed by the creator of the review.

Second, AI uses a process called ‘machine learning’ which enables AI to learn by itself. Through remembering and storing everything it has analysed previously, AI continuously improves. The more data and information, the ‘smarter’ AI gets, meaning the more accurate and insightful its analyses become.

How AI Can Benefit Marketing Campaigns

How AI Can Benefit Marketing Campaigns

There are many ways AI can help to elevate marketing campaigns to new heights.

Building rich customer profiles

With AI, it is possible to understand customers’ emerging wants and needs in real time — as consumers express them online — and build and continuously update these profiles.

For example, E-commerce platforms leverage AI to collect and analyse customer data from various sources, including customer preferences, price points of items purchased, reviews provided, items in cart, etc. AI algorithms segment customers based on behaviour, analyse sentiment, and provide personalised recommendations.

Enhanced audience targeting

AI can significantly improve audience targeting by enabling marketers to create more accurate buyer personas. This allows marketers to deliver highly targeted and personalised messages to the right people at the right time, maximising the effectiveness of their campaigns.

For example, AI can identify the most valuable segments based on purchase intent and provide offers, discounts or other marketing communications that are most likely to resonate and convert leads into sales.

Chatbots and virtual assistants

Chatbots and virtual assistants powered by AI have become increasingly popular in recent years. These intelligent bots can engage with customers in real-time, answer their queries, and provide personalised recommendations.

By incorporating chatbots into marketing campaigns, businesses can enhance customer service, increase engagement, and even drive sales.

Content creation and optimisation

AI can assist marketers in creating and optimising content. NLP algorithms can analyse existing content and identify patterns that resonate with the target audience. This information can be used to create compelling copy, headlines, and calls-to-action.

AI can also optimise content by analysing user engagement metrics and recommending changes to improve its performance. This helps marketers create content that is more likely to resonate with their audience and drive desired actions.

Social media monitoring and sentiment analysis

AI can play a vital role in monitoring social media platforms and analysing sentiment around a brand or product. By analysing user-generated content, AI algorithms can identify trends, sentiments, and influencers in real-time. This information can help marketers understand public opinion, gauge the effectiveness of their campaigns, and make timely adjustments to their strategies.



Q: Are there any challenges of implementing AI in marketing campaigns?

A: Yes, there are challenges to consider when implementing AI in marketing campaigns. Data quality and privacy concerns must be addressed to ensure accurate insights and protect customer information. It is crucial to have skilled professionals who can manage and interpret AI-generated data. Additionally, transparency in AI algorithms and ethical considerations surrounding AI use are important for maintaining trust with customers.

Q: What are some examples of successful AI-driven marketing campaigns?

A: Several companies have implemented successful AI-driven marketing campaigns. For instance, Netflix utilises AI algorithms to recommend personalised content to its users, resulting in increased viewer engagement. Amazon’s recommendation system uses AI to suggest products based on user behaviour and purchase history, leading to higher conversion rates. These examples showcase the power of AI in delivering tailored marketing experiences.

Q: What are the benefits of using AI-powered chatbots in marketing campaigns?

A: AI-powered chatbots offer numerous benefits in marketing campaigns. They provide instant customer support, answer inquiries 24/7, and assist with product recommendations. Chatbots can also automate lead generation, offer personalised promotions, and gather valuable customer feedback. Their efficiency, scalability, and ability to handle multiple conversations simultaneously make them invaluable tools for enhancing customer experience.



Artificial intelligence has immense potential to transform marketing campaigns and revolutionise the way businesses engage with their target audience. However, it is crucial to remember that AI is a tool, and its effectiveness depends on the quality of data and the human expertise behind it. To truly harness the potential of artificial intelligence in marketing, businesses should combine it with human creativity, insights, and strategy to create compelling and impactful campaigns that resonate with their audience.

If you also want to learn the dynamic world of Artificial Intelligence, then upGrad Campus artificial intelligence course will be the best option for you.

Unlock the Power of Generative Artificial Intelligence with upGrad Campus Online Certification Course! Learn from industry experts, explore cutting-edge technologies, and gain hands-on experience through practical projects. Boost your career prospects with a comprehensive curriculum that covers machine learning, deep learning, and data analytics.

Enrol now and embrace the future of AI!

Disclaimer: The success of job placement / interview opportunity depends on various factors including but not limited to the individual’s qualifications, experience, and efforts in seeking employment. Our organization makes no guarantees or representations regarding the level or timing of job placement / interview opportunity. Relevant terms and conditions will apply for any guarantee provided by upGrad.

The Role of Linguistics in AI

Table of Contents

Introduction to the Linguistic Role in AI

Introduction to the Linguistic Role in AI

Artificial Intelligence (AI) has revolutionised the modern world. From automated systems to self-driving cars, we can see the footprints of AI in most industries and subsectors. As AI grows more and more powerful, it gets deeply integrated into all aspects of our lives.

The role of linguistics in AI is one such aspect which has become significant in recent years. Linguistics are leveraged to develop natural language processing (NLP) algorithms that allow our machines and systems to comprehend, engage, and generate human language effectively. Because of how valuable this technology is becoming, companies are always searching for skilled professionals to hire. Artificial intelligence courses in India have recognised the potential benefits of a career in this field, and hence have started AI certification programs for freshers that specialise in linguistics.

This article will explore the significance of linguistics in AI, the intricacies of the field, and why they’re important in today’s world. Upskill with artificial intelligence courses and lead the revolution into this transformative new technology.

Natural Language Processing – Foundation of Language-Based AI

Natural Language Processing - Foundation of Language-Based AI

Natural Language Processing (NLP), is a branch of AI that focuses on helping computers comprehend, interpret, and produce human language. NLP essentially forms the core of Language-based AI. Recent developments in Deep Learning have now made it possible for NLP models to extract meaning, feelings, and entities by processing enormous quantities of unstructured data.

Numerous language-based AI applications have been created thanks to NLP – think Google Assistant, Apple’s Siri, and Amazon’s Alexa. Virtual Assistants use NLP to understand and carry out requests. Be it delivering information or even just playing a song!

Speech Recognition – Turning Spoken Language into Text

Speech Recognition - Turning Spoken Language into Text

The use of linguistic principles in AI has led to substantial breakthroughs in speech recognition technologies. These technologies enable seamless communication between humans and machines by translating spoken words into written text. Major industries are now driving towards AI Based softwares and technologies.

For example: AI-driven speech recognition has had a significant impact on various industries, like medical transcribing services. Doctors can record patient information verbally, into a system that will note it down, saving time and lowering the possibility of an error. Additionally, voice assistants like Siri and Google Assistant use speech recognition to understand user requests, improving the usability and intuitiveness of our interactions with the technology.

Language Translation: Bridging the Gap Between Languages

Language Translation: Bridging the Gap Between Languages

Another impressive way that linguistics are used in AI is in Language translation . Advanced algorithms are used by Machine Translation (MT) systems to automatically convert text or speech from one language to another.

AI-powered translation services can assist in bridging language gaps. Users can translate individual words or whole web pages into other languages using online tools like Google Translate. People from various linguistic origins can now interact and work together successfully thanks to this transformation of cross-cultural communication. Further, these AI tools are critical to industries like Tourism, where real-time translation is required.

The Linguistic Edge: Opportunities with AI Certification

The Linguistic Edge: Opportunities with AI Certification

Artificial Intelligence (AI) has emerged as a transformative force across various industries, and its impact continues to grow rapidly. As a result, the importance of linguistic expertise after completing an Artificial Intelligence course cannot be overstated.

Moreover, AI language models are revolutionising translation services, content generation, and sentiment analysis, among other applications. The demand for professionals with expertise in linguistic roles within AI is growing massively, offering immense opportunities for freshers in the fields like Healthcare, Finance and many more. By enrolling in an Artificial Intelligence and Machine Learning Program, students can land jobs in a very in-demand sector.

Related : The Future of AI: Trends and Technologies Covered in Artificial Intelligence Courses





As India gears up for the AI revolution, linguistics is one of the central technologies that is emerging as incredibly important and in-demand. A career in AI linguistics is going to be prosperous, but also highly competitive. Hence, by obtaining an AI certification from upGrad Campus, freshers will be able to upskill themselves and get in-depth knowledge of elements such as sentiment analysis, content development, virtual assistants, translation services, and chatbots. 


Our programs rank as some of the best artificial intelligence courses in India due to our excellent faculty, numerous resources and placement opportunities. Enroll in one of the most comprehensive AI courses online with upGrad Campus and be at the forefront of this exciting new field.


Frequently Asked Questions (FAQs)

1. Is Linguistics useful in Machine Learning?

A: Of course! Linguistics is incredibly important for ML, particularly when examined through the lens of NLP. It provides students with the ground level understanding of language structure, semantics, and grammar which is key for training machine learning models. It also plays a fundamental role in operations such as text analysis, speech recognition, and sentiment analysis.


2. What Language is Used in Artificial Intelligence?

A: When it comes to AI, several programming languages are typically used. This can vary as per the application, the system and developer’s preferences. Some popular programming languages for AI include Python, R, Java, and C++. The predominant language used however is Python, due to its exhaustive libraries and frameworks.


3. What are Linguistic Features in Machine Learning?

A: Linguistic features are details that are extracted from text or speech data. This is then used to train machine learning models. They include things such as word frequency, sentence structure, and sentiment analysis. 


4. Why is Linguistics Related to Science?

A: Linguistics is related to science as it is a field that systematically and scientifically studies language and all its various components and intricacies. It helps us understand a language’s structure, the evolution, and common day usage- making it essential for advancing technology like ML and NLP. 

Related : Opportunities and Challenges in the field of AI and ML for Freshers

Impact of ChatGPT and other AI tools in the workplace

Table of Contents

If you are a student or a fresher you might have entered a conversation about 

“Is AI going to take over human intelligence?” or “Will AI lead to mass unemployment in the marketing sector?”. Today, evolution in AI is a matter of concern for many working professionals or students choosing their career path. Especially after the introduction of ChatGPT 3.5.

In recent years, Artificial Intelligence has developed significantly shaping various industries and workplaces. AI has become a big subject in the tech industry as now it is involved in doing a lot of work which previously needed human intelligence.

AI offers a helping hand everywhere – from basic tasks like turning off the lights to more complex functions like developing apps and websites or security-led programming. This has led to speculation that AI tools will eventually replace developers, making programming a thing of the past.

The answer to these questions lies deep into understanding how AI works and how AI is going to be placed in the upcoming market. In this blog, we will explore the impact of AI on the workplace and how artificial intelligence certification can prepare developers for the future.

Most Popular AI Tools

Artificial intelligence or AI tools are introduced to make working easier and efficient for the workforce and to adroitly perform tasks which were tough and time consuming when done manually.

Gradually, these tools are undergoing massive improvements and are in great need in the current market, leading to a rise in demand for artificial intelligence courses. Many AI tools like Siri, Alexa, Grammarly, Dall-E, Jasper came in the market and became an indispensable part of everyday work. The development of ChatGPT was part of a larger effort to improve AI’s ability to process and understand natural language.

Prior to the introduction of ChatGPT, Natural Language Processing (or NLP) models were limited in their ability to understand and respond to complex natural language inputs. ChatGPT’s development represented a significant advancement in NLP, as it could generate coherent and natural language responses to a wide variety of input prompts. Its ability to generate human-like responses has made ChatGPT a valuable tool in improving the customer experience and streamlining communication between humans and machines. 

However, in today’s market, ChatGPT is standing as a great question mark on its impact on the human workforce, leading to more students signing up for artificial intelligence courses. Despite its many capabilities and a great working potential, it is hard to definitively say that it can ever replace developers. Let us see why.

Can AI tools Replace Developers?

Can AI tools Replace Developers?

Firstly, it’s important to understand what AI tools are and what they can do.

AI tools are software programs that use machine learning algorithms to automate tasks that would normally require human intelligence.

For example, some AI tools can generate code based on natural language descriptions of what the code should do, while others can identify and fix errors in code.While AI tools can automate some aspects of software development, they cannot replace developers entirely.

Here are some reasons why:

Creativity and Problem-Solving Skills: One of the most important skills that developers possess is creativity. They can come up with innovative solutions to problems that AI tools may not be able to handle. 

Developers can also understand the context of a problem and are able to apply their knowledge to find the best solution. AI tools, on the other hand, are limited by their algorithms and cannot think outside the box.

Flexibility: Developers need to be able to adapt to new situations and learn new skills quickly. They need to be able to work with new technologies and programming languages as they emerge. While AI tools can be trained on new data, they cannot adapt to new situations as quickly as humans.

Communication and Collaboration: Developers often work in teams, collaborating with designers, project managers, and other developers. They need to be able to communicate effectively and work together to achieve a common goal. While AI tools can assist in some aspects of software development, they cannot replace the human element of communication and collaboration. So, if you are going for artificial intelligence certification do remember to master soft skills with hard skills. 

Ethics and Responsibility: Developers are responsible for ensuring that their code is ethical and does not harm users. They need to be aware of the social and ethical implications of their work. AI tools, however, are not capable of making ethical decisions or understanding the consequences of their actions.

AI tools can assist in some aspects of software development. For example, they can automate repetitive tasks such as testing and debugging, freeing up developers to focus on more creative work. They can also help identify security vulnerabilities and suggest improvements to code.
For a better understanding, let’s check the uses and limitations of ChatGPT.

Highlights and Challenges of ChatGPT

Highlights and Challenges of ChatGPT

ChatGPT, like any other AI tool, is useful in many aspects but comes with its own sets of limitations. Here are some of the key uses and limitations of ChatGPT:

Uses of ChatGPT

  1. Chatbots and virtual assistants: ChatGPT is commonly used to develop chatbots and virtual assistants that can engage in natural language conversations with humans.
  2. Customer service: ChatGPT can be used to provide automated customer service by responding to common inquiries and providing helpful information.
  3. Language translation: ChatGPT can be used to translate text from one language to another, making it useful for communication across different languages.
  4. Personalization: ChatGPT can be used to personalise the user experience by understanding user preferences and tailoring responses accordingly.

Limitations of ChatGPT

  1. Lack of context: ChatGPT may sometimes lack the contextual understanding needed to provide accurate responses. This may lead to a non-relevant or unsatisfactory response.
  2. Biases: ChatGPT may exhibit biases in its responses due to biases in the training data or the way the model was developed.
  3. Limited understanding of the world: ChatGPT has limited understanding of the world outside of the data it was trained on. This can lead to a lack of understanding of certain concepts or events.
  4. Conditioned to training data: ChatGPT purely works on training data and continuously works on self-improvements. But it significantly lacks common sense and without sufficient and diverse data, its performance may be limited.



Overall, developers are creative problem solvers who can think outside the box, understand the context of software development, adapt to new technologies, collaborate effectively with others, and consider the ethical implications of their work. Until ChatGPT or any other AI tool doesn’t imbibe these traits, it is safe to say that human software developers will still be in need. However, companies will always be on the lookout for professionals who can work hand-in-hand with AI.

If you want to be this professional, then you should go for the upGrad Campus artificial intelligence certification. This course helps developers stay up-to-date with the latest AI technologies and prepare for the future. Be ready for the future, act today by talking to our experts.

Chatbots and Conversational AI: Improving Customer and Business Service

Table of Contents

Being a good friend who replies late or ignores messages may not affect your identity much, but have you ever thought what if a business does the same? It can cost you a good amount of clients.

But now business has evolved a lot in this field with the introduction of Conversational AI, Chatbots and Virtual assistance. Which makes Artificial Intelligence Course a high opportunity field in today’s market. Conversational Chatbots have significantly reduced the time a business takes to respond to their customer. It has also changed the way businesses manage their internal processes.

Moreover, Conversational chatbots have taken a supreme position after the introduction of video chatbots, that use visual and audio technology to answer the queries of the customers. By leveraging AI, businesses are now able to maintain 24/7 customer support, streamline internal processes, and improve overall efficiency at a very low cost.

Let’s dig a little deeper to know how Conversational AI can improve customer and business service, and the benefits they put out to us.

What are Customer Chatbot Services and Video Chatbots?

What are Customer Chatbot Services and Video Chatbots?

Chatbot services are AI powered computer programs developed to mimic human interaction with the help of machine and data-based learning. These are programmed to extract answers from different articles and data present on the internet.

Besides commonly used Chatbots, the introduction of video chatbots have proved to be a massive hit. These chatbots work on audio linguistics technology and are capable of answering questions by taking audio instructions. The video graphic representation makes it more indulging and mimics real conversations perfectly.

Moreover, these services can be integrated in various platforms like websites, social media accounts or messaging apps like WhatsApp business and Instagram business profiles to make customer service convenient and accessible at any time. These chatbots are based on psychology for product management to work on self-improvisations and update themselves through client’s conversation and queries and provide more personalised assistance over time.

With a long list of benefits these chatbots have brought a big revolution for business customer services. Let’s see how.

Impact of Chatbot customer service on business

Impact of Chatbot customer service on business

Chatbots have significantly received a warm welcome by the existing businesses and the employees. Let us explore how AI and conversational AI can improve customer and business service, and the benefits they bring to the table.

  • Round-the-clock support

One of the most significant benefits of conversational AI is its ability to provide round-the-clock customer service support. Customers can easily interact with chatbots to get answers to frequently asked questions, place orders, and resolve issues. This can save time for both customers and businesses, and improve overall customer satisfaction.

Additionally, chatbots can collect data and analyse customer behaviour, providing valuable insights for businesses to improve their products and services.

  • Streamlining Internal Processes with Virtual Assistants

Conversational AI can also be used to streamline internal processes and improve employee productivity.

For example, virtual assistants can help employees with tasks such as scheduling meetings, managing emails, and accessing important information.

This can save time and increase efficiency, allowing employees to focus on more strategic tasks.

  • Building Chatbots and Virtual Assistants

To build a conversational AI, individuals can pursue an artificial intelligence certification or take an artificial intelligence course. These courses can provide an understanding of the fundamentals of AI, machine learning, natural language processing, and more which can be useful for building chatbots and virtual assistants.

Additionally, there are several AI platforms available that make it easy to create and deploy chatbots without any coding experience. AI and Conversational chatbots have become so commonly used and hence is a vast growing opportunity field.

Let’s check how common they are and with how many conversational AI and Chatbots you are familiar with.

Popular Conversational AIs

Popular Conversational AI

Conversational AI is no more about giving pre- built answers to questions asked in a certain language or manner but it has elevated itself in understanding commands in many commonly spoken languages and providing personalised responses.

There are mainly three types of conversational AI: –

AI chatbots: – These are most used chatbots in small businesses and e-commerce websites and social media and messaging apps popularly found in WhatsApp Business, Facebook, Instagram. These chatbots are very reliable for providing virtual customer assistance and are easy to install and use in your business.

Voice-activated Bots: – These bots use Interactive Voice Response technology which allows users to interact with computerised systems using voice command inputs or touch tone keypad inputs. They are mostly used to handle high volume of incoming calls and redirect it to the authorised person. Some popular examples are Ameliai and AVA.

Interactive voice assistance: – These are voice enabled devices which are programmed to mimic human conversations in many different and now even regional languages. They allow users to communicate in simple and generic language without giving any specific command.

Amazon Alexa, Google Assistant, Apple Siri, and Microsoft Cortana are most common and successful examples of Interactive Voice Assistance.

Video bots: – These chatbots use video content as means of communication instead of regular text based conversations. These are programmed on the basis of NLP which is Natural Language Processing and machine learning algorithms.

Most common and popular video bots are Meena by Google, Blender Bot by Facebook, Tay by Microsoft.



Conversational AI has a strong grasp in today’s market and hence has a great career opportunity.

If you have interest in this field and you want to pursue a course in Artificial Intelligence Course then go for upGrad campus online Artificial Intelligence certification course that provides you the complete package with Live Sessions, Mentor Support and Placement Assistance.

How Artificial Intelligence and Machine Learning is Going to Impact your Career?

Table of Contents

  1. What is Artificial Intelligence and Machine learning
  2. Advantages of Artificial Intelligence and Machine learning for industries and job seekers
  3. Types of AI & ML Jobs
  4. Certification Program in Artificial Intelligence and Machine learning by upGrad Campus
  5. Conclusion

Artificial Intelligence (AI) and Machine Learning (ML) are no longer the stuff of science fiction movies – they are here and making an impact on the world we live in. AI & ML jobs are becoming more demanding but due to the shortage of institutions providing quality skills, only a handful of candidates are switching to this field. Hence the competition is too less and chances of success are higher.


Are you curious about this exciting, rapidly-growing field? Do you want to make a career out of it? Or even pick up some supplementary skills to boost your existing career? In any case, understanding the different types of AI & ML jobs available in the market is an important first step.


Whether you’re an experienced professional or a recent graduate, this guide will provide helpful insight into the current trends in AI & ML jobs – what salaries you can expect, what companies are hiring, and where your skills could be most in-demand. Let’s take a look at what we need to know to make sure we’re up-to-date with our job market research.

What is Artificial Intelligence and Machine Learning?

What is Artificial Intelligence & Machine Learning

AI and ML (or Machine Learning) can be thought of as two sides of the same coin. AI is about using algorithmic models to perform tasks automatically, while ML focuses on teaching machines to solve problems by analysing huge datasets – like finding patterns or recognizing objects. In other words, AI works like your digital assistant, while ML helps you turn that data into insights that can be used to make better decisions.


AI & ML jobs are some of the most in-demand tech jobs in the market today. From working on the strategy and development of AI systems to applying ML techniques to analyse data, there are lots of opportunities for professionals with the right skillset. There are also specialised roles for building and engineering AI models, such as Machine Learning Engineers and Data Scientists – so if you’re looking for a lucrative career in tech, now might be a great time to explore these roles.

Advantages of Artificial Intelligence and Machine Learning for Industries & Job Seekers

Advantages of Artificial Intelligence and Machine Learning for Industries and Job Seekers

The future of industries and job seekers stands poised to be shaped by innovation in Machine Learning and Artificial Intelligence (AI) . AI and ML are powerful tools that enable faster decision-making and allow businesses to respond more quickly to market changes. With these powerful technologies reshaping the industry, job seekers must be aware of the advantages AI and ML bring to the workplace. AI and ML offer the potential of increased efficiency and productivity to businesses. By incorporating AI into existing processes, these tools can cut costs, free up time for other tasks by automating repetitive tasks, and even increase customer satisfaction. Additionally, AI-driven automation tools can improve customer service. For example, Amazon’s suggested items based on past purchase history and its voice-activated Alexa respond to customer requests, allowing shoppers to save time and resources. AI and ML also offer better decision-making capabilities and predictive insights. 


By analysing large amounts of data, AI and ML algorithms can help CEOs make more informed decisions. This data can also help forecast future trends and inform new strategies that could prove to be beneficial to the business. For example, Uber’s UberEats service uses ML to predict customer demand in areas they are less familiar with, helping them to better manage their supply and demand and improve customer service. The combination of AI and ML can also offer job seekers an edge by helping them better target the right job opportunities. 


By using AI-driven job search platforms such as Monster or Indeed, jobseekers can effectively create powerful search terms that allow them to target specific jobs. Additionally, intelligent algorithms can help jobseekers assess the job opening, customise their message, and modify their uploading resume to match the search criteria. AI and ML, when used wisely, can be a powerful enabler of job opportunities and increase the efficiency and productivity of industries. By understanding and taking advantage of these valuable technologies, businesses and job seekers can create beneficial outcomes for their future and the future of the economy.

Types of AI & ML Jobs

AI & ML jobs are some of the hottest trends in tech right now, and there’s no shortage of options to choose from. Understanding the different types of roles available can help you decide which kind of job might be best suited to your skills and interests.Let’s start with AI Specialists, who work to design and build AI systems to automate processes or tasks. They often also develop algorithms, usually in Python or Java, for use with Machine Learning or Artificial Intelligence systems.


Data Analysts are responsible for searching data sets for patterns and insights using a variety of tools and techniques, from predictive modelling to natural language processing. They’ll then interpret the results and make recommendations based on their findings.


Then there are Machine Learning Algorithm engineers, who use computer programming languages such as C++ or Python to create algorithms that allow machines to learn from data—think facial recognition systems or autonomous vehicles.


Finally, there are also roles for AI & ML Architects—the people responsible for translating business objectives into AI & ML solutions using the latest technologies. They need an in-depth understanding of algorithms and software engineering principles, as well as a good knowledge of cloud computing platforms like Amazon Web Services or Microsoft Azure.

Certification Program in Artificial Intelligence & Machine Learning by upGrad Campus


Certification Program in Artificial Intelligence and Machine Learning upGrad Campus

Are you ready to jump into the world of AI & ML? If you’re looking to break into the field, a certification program in Artificial Intelligence and Machine Learning (AI ML certification) by upGrad Campus could be an excellent starting point. It’s one of the few courses available that offers comprehensive training in both Artificial Intelligence as well as Machine Learning.


The Artificial Intelligence and Machine Learning Course  combines theoretical knowledge with real-world application and trains participants on cutting-edge skills like Artificial Intelligence automation, Python, Kera, Kaggle and more such languages, multi-agent systems, reinforcement learning, natural language processing, deep learning and more. It also provides support from mentors and industry experts to ensure students can implement their learning to solve real-world problems.

With an AI ML certification from the upGrad Campus, you can be confident that you’re equipped with the right skills and experience to help you succeed in this competitive market. What’s more, it will also open up exciting opportunities with some of the leading tech companies in the field of Artificial Intelligence & Machine Learning.




AI and ML are here to stay and the job market is in high demand for those who have completed AI ML certification and are educated around the technology. Whether you’re looking for a career in a specific field, or want to expand your skill set to include AI and ML, understanding the different types of jobs available is crucial for success.


From Machine Learning engineers creating powerful algorithms to business analysts using AI-powered tools to make better decisions, there is a variety of options for those with the right skillset. Understanding the different roles and responsibilities within these jobs, as well as staying up-to-date with the latest market trends, will help you better position yourself for a rewarding career in the most advanced, interesting, and lucrative fields of the 21st century.

Introduction to Machine Learning

Table of Contents

Traffic Alerts on Google Map, Netflix Recommendation Engine and now ChatGPT. These are just a few applications and uses of Machine Learning. 

Whether you’ve noticed it or not, Machine Learning is becoming a part of our daily lives. And with every new intelligent application cropping up, it’s a good idea to know the importance of Machine Learning and the Machine Learning career opportunities available to you.

In this article, we are going to answer all your queries starting from the most fundamental one – What is Machine Learning?

What is Machine Learning?

As the name suggests, Machine Learning is the science of equipping machines (or computers) to learn on their own. The official Machine Learning definition is that it is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” with data, without explicitly being programmed.

Now if you cannot explicitly program or tell a computer what it is supposed to do, then how does Machine Learning work?

How does Machine Learning work?

In one word? Data. 

With Machine Learning techniques, you feed the computer system enough data so that it can pick on patterns and come up with meaningful insights.

(Side note: This reliance on data is what makes Data Science and Machine Learning related fields.)

Let us understand this with an example. Suppose you want to build a system that identifies whether a given animal is a dog or a cat. Here are the steps involved in the same:

Step 1) Define the problem: Your first step, like in any problem-solving case, is to define what the problem is. Our problem is to be able to distinguish between cats and dogs.

Step 2) Collect data: In this step, we will collect a large amount of images containing dogs and cats and label them appropriately.

Step 3) Prep the data: Next, we will process the images so that they are understandable by the system. For e.g., we could resize the images to be in the same dimension.

Step 4) Select a Machine Learning model: We will identify which Machine Learning model is best suited to our problems. For image classification, it is usually Convolutional Neural Networks (CNNs).

Step 5) Train the model: With the existing dataset, train the model to identify one set of images by tuning the parameters (e.g. if the image has 4 legs, 2 ears, 2 eyes, whiskers, it’s an animal).

Step 6) Evaluate the model: Feed the model another set of images to check for its accuracy.

Step 7) Fine-tune the model: Make further adjustments in the model to improve performance. For example, show the machine how to classify the different types of fur coats of dogs and cats.

Step 8) Make predictions: Test the model on completely, new unseen images and see if it has learnt enough to make predictions.

Step 9) Deploy the model: Integrate the model with a real-world application like an app or a website.

Step 10) Monitor and maintain: Continue to monitor the model functioning and make updates as needed.

Sounds simple enough right? But it needs the technical skills of a Machine Learning engineer to get this in place. And more importantly, there are variations in the above steps based on the type of Machine Learning model.

Types of Machine Learning

The three types of Machine Learning models are:

  1. Supervised Machine Learning

    Supervised learning algorithms are trained to make predictions on unseen data based on labelled data. Think of supervised learning as your 3rd standard teacher who shows you different charts to explain what things are.

    The image classification model that we explained above is a perfect example of supervised machine learning. 

  2. Unsupervised Machine Learning

    In unsupervised learning, the algorithms are trained to uncover patterns on unlabelled data. Think of an unsupervised machine learning model as exploring a new city without a map or a guide to help you. But only by recognising what seems familiar (like a restaurant), you can figure out the context of unfamiliar things.

    For example, in fraud detection, the algorithm views various transactions and groups them. Whenever an unexpected transaction is seen, it identifies it as an anomaly and flags it to your bank.

  3. Reinforcement Machine Learning

    As the name suggests, reinforcement learning is all about positive and negative reinforcement or in other words – rewards and penalties. An example would be teaching a robot to play a game. Every time the robot makes a correct move, it wins points and every time it makes the wrong move, it loses points.

Common Machine Learning Algorithms

Common Machine Learning Algorithms

When you have to teach a machine learning model how to solve a problem, you don’t have to reinvent the wheel. There are already many machine learning algorithms that exist for the same.

Here is a small introduction to machine learning algorithms that are most widely used:

  • Neural Networks

    A neural network is based on the way our brain works – in that, our brain’s final decision is based on the smaller decision of each neuron which gets passed to the next neuron, and so forth. For example, before we eat something, there is a neuron that checks if our stomach feels empty which passes this decision to the next neuron that decides if we want to eat or not and passes this decision to the next neuron that decides whether we want to eat an apple or not and so forth.

    Likewise, a neural network comprises a series of binary decision makers that come up with a consolidated output. A simple example would be a facial recognition system which looks at an image, divides it into many pixels and considers every pixel match before it finally decides whether the face matches the image or not.

  • Linear Regression

    Linear regression algorithm tries to determine a relationship between two entities in order to make predictions. For example, let us say a publisher wants to understand whether or not to invest in a sequel of a book. The publisher will use a linear regression algorithm to see how the sales have been of the same author’s previous novels, and based on that the possible sales of the next novel. 

  • Logistic Regression

    Similar to linear regression in that logistic regression is also used in making predictions, but the outputs are binary – a plain yes/no. The simplest example would be for banking systems to understand if a person is likely to pay their loans based on their credit history and the payment history of other customers with similar characteristics. Based on the outcome, the bank will approve or reject the loan.

  • Clustering

    As the name suggests, clustering analyses various data points to find commonalities and groups entities together. A simple example would be for a store to go through the purchase history, age, gender and other characteristics of their customers, and come up with new customer segments.

  • Decision Trees

    Decision Trees mimic human thinking by dividing a major decision into a series of tiny decisions. For example, let us say there is an automated game in which there is a non-playable character (NPC) that is controlled by the computer. Now this NPC may decide to shoot an actual player or not and may consider several factors. Does it have enough ammunition? Will it alert other players of its location? Based on these decisions, the final decision will be taken.



With these Machine Learning examples, we hope it is clear that the applications of Machine Learning are plenty and only growing further. You can start your career in this lucrative field by taking a Machine Learning course online. But make sure to take a course that gives you sufficient hands-on experience so that you can build Machine learning models for real-world problems.

Which Machine Learning algorithm or example fascinates you the most? Let us know in the comments below.

*The placement figures represent the number of students placed by upGrad. Past record is no guarantee of future job prospects. The success of job placement/interview opportunity depends on various factors including but not limited to the individual's performance in the program, the placement eligibility criteria, qualifications, experience, and efforts in seeking employment. Our organization makes no guarantees or representations regarding the level or timing of job placement/interview opportunity. Relevant terms and conditions will apply for any guarantee provided by upGrad.