Complete Guide to Machine Learning and Artificial Intelligence

Solve Real-World Problems with Machine Learning and AI

(Inclusive of all applicable taxes)
Price on Request 

Make a life-changing career decision now!

Career opportunities in AI and ML are growing rapidly as the world is becoming more and more dependent on data. The data-driven operations, adopted by almost every industry, will increase the demand for well-trained AI and ML professionals.

    • Potential Job Opportunities

      22,000+ jobs listed across various industries

    • Earning Potential

      Potential to earn an average salary of ₹ 5 LPA to ₹ 10 LPA

    • Employment Sector

      IT, Healthcare, Manufacturing, Retail, Marketing, Finance and more

    Potential Career Path
    Data Analyst
    Data Engineer
    Data Scientist

    Course Details

    • Commencement Date
    • Duration 658 hours
    • Language English
    • Eligibility Criteria Diploma completed or pursuing Bachelor’s degree in Engineering/Technology/Statistics/Mathematics/Computer Science
      Minimum Age: 18 Years
    • Assessment
      • Multiple intermediate assessments and one final assessment leading to certification.
      • Final assessment date will be announced, 1 month in advance.
      • NA
      • NA
    • Total Credits NA
      Lecture Credits NA
      Practical Credits NA
    • Course Format Online (Self-paced + Live Lectures + Hands-on Learning)

    What you will learn in the course

    Differentiation between supervised and unsupervised machine learning methods
    Supervised learning algorithms, including classification and regression
    Unsupervised learning algorithms, including clustering and dimensionality reduction
    Statistical modeling related to machine learning and its comparison
    Using state-of-the-art Deep Learning frameworks such as Google’s TensorFlow
    Applications of Deep Learning in image recognition, NLP, and so on

    Course Summary

    According to recently updated International Data Corporation (IDC) Worldwide Artificial Intelligence Systems Spending Guide, the spending on AI systems will reach USD 282 billion by 2027. Globally, vendors of consumer devices such as phones, speakers, displays, wearables, and the like, are competing and investing billions to make their devices feature-rich, more powerful, connected and affordable. Professionals who are pursuing a career in Machine Learning and Artificial Intelligence will be progressively in demand as companies continue to adopt AI and ML innovations in the coming decade.

    The objective of the programme is to enable the learners to acquire knowledge on Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Computer Vision and different algorithms to build custom ML models, facilitate better understanding of conversational experiences and create better customer experience.

    Read more


    Course Syllabus

    The course syllabus will be delivered through a combination of learning resources, live lectures and community-based interactions.

    • Introduction to Artificial Intelligence and Machine Learning
    • Types of Machine Learning
    • Trends in Artificial Intelligence
    • Importance of Python
    • Introduction to Python and comparison with other programming languages
    • Python basics
    • Functions
    • Object-Oriented Programming System (OOPS)
    • Basic libraries for Data Science
    • Introduction
    • Types of data and ways to measure data
    • Basics of Probability
    • Basics of Statistics
    • Basics of Calculus and Linear Algebra
    • Preprocessing data in Python
    • Dealing with missing values in Python
    • Data formatting in Python
    • Handling imbalanced data
    • Handling outliers
    • One-hot encoding and label encoding
    • Feature engineering and selection
    • Linear regression
    • Logistic regression
    • Polynomial regression
    • Support vector regression
    • Decision tree regression
    • Random forest regression
    • Linear models
    • Non-linear models
    • Model evaluation
    • K-means clustering
    • Density-based clustering
    • Distribution model-based clustering
    • Hierarchical clustering
    • Fuzzy clustering
    • Apriori algorithm
    • Dimensionality reduction
    • Hyperparameter tuning
    • Introduction to Deep Learning
    • Introduction to Natural Language Processing
    • Image processing
    • Recommendation systems

    Meet the Mentor(s)

    Connect with Us

    If you want to know more about our products or have any other queries


    1. What is the application process for the programme? +
      • Candidates must apply on OR before the application closure date.
      • Verification of candidate details will be done by Academic Partner.
      • If the application is approved, candidates will receive an approval notification via email.
      • In case the application gets rejected; candidates will not be able to enrol for the course.
      • If there is any information missing in the application form, candidates would be asked to edit/fill the application form with missed out information and resubmit.
      • Approved candidates will receive the Activation Code from their respective institutions to activate the course.
      • The learning programme will be added to learner’s dashboard subject to successful payment and activation.