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Artificial Intelligence

With the use of concepts such as ML, Python, and Predictive Analysis, we dive into the domain of Artificial Intelligence.


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10 live projects + 1 capstone project

36+ hours of video content access

Weekly tasks and reports

Certificate of completion


Learn Artificial Intelligence today

Artificial Intelligence is a developing and highly sought-after domain to specialise in. Build the foundation, enhance your proficiency in its fundamental topics, and learn to make the smartest computer systems, equipped with AI.


Predictive Analysis

Artificial Neural Networks

Association Rules


Course Curriculum

Discover our comprehensive Artificial Intelligence course curriculum, designed to provide in-depth knowledge and practical skills



  1. ML Fundamentals
  2. ML Common Use Cases
  3. Understanding Supervised and Unsupervised Learning Techniques


  1. Similarity Metrics
  2. Distance Measure Types: Euclidean, Cosine Measures
  3. Creating predictive models
  4. Understanding K-Means Clustering
  5. Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
  6. Case study

Implementing Association Rule Mining

  1. What is Association Rules & its use cases?
  2. What is Recommendation Engine & it’s working?
  3. Recommendation Use-case
  4. Case study

Decision Tree Classifier

  1. How to build Decision trees
  2. What is Classification and its use cases?
  3. What is Decision Tree?
  4. Algorithm for Decision Tree Induction
  5. Creating a Decision Tree
  6. Confusion Matrix
  7. Case study

Random Forest Classifier

  1. What is Random Forests
  2. Features of Random Forest
  3. Out of Box Error Estimate and Variable Importance
  4. Case study

Support Vector Machines

  1. Case Study
  2. Introduction to SVMs
  3. SVM History
  4. Vectors Overview
  5. Decision Surfaces
  6. Linear SVMs
  7. The Kernel Trick
  8. Non-Linear SVMs
  9. The Kernel SVM

Feature Selection and Pre-processing

  1. How to select the right data
  2. Which are the best features to use
  3. Additional feature selection techniques
  4. A feature selection case study
  5. Preprocessing
  6. Preprocessing Scaling Techniques
  7. How to preprocess your data
  8. How to scale your data
  9. Feature Scaling Final Project

Introduction to Artificial Neural Networks

  1. The Detailed ANN
  2. The Activation Functions
  3. How do ANNs work & learn
  4. Gradient Descent
  5. Stochastic Gradient Descent
  6. Backpropogation
  7. Understand limitations of a Single Perceptron
  8. Understand Neural Networks in Detail
  9. Illustrate Multi-Layer Perceptron
  10. Backpropagation – Learning Algorithm
  11. Understand Backpropagation – Using Neural Network Example
  12. MLP Digit-Classifier using TensorFlow
  13. Building a multi-layered perceptron for classification
  14. Why Deep Networks
  15. Why Deep Networks give better accuracy?
  16. Use-Case Implementation
  17. Understand How Deep Network Works?
  18. How Backpropagation Works?
  19. Illustrate Forward pass, Backward pass
  20. Different variants of Gradient Descent

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Teachnook Certificate

Upon successfully completing this course, you will receive a certificate of completion that helps potential employers assess your proficiency.

Pricing Plans

Experience the premium features at an affordable price. Get industrial experience, 10+ live working projects and mentorship from top 1 percentile mentors and much more. Choose the plan that suits your needs and take your practical and outcome based learning to the next level. Join today and lead tomorrow.


Explore our FAQ section for quick answers to common questions. Can't find what you're looking for? Contact us for assistance.

Are there any necessary prerequisites for this course?

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No, there are no specific prerequisites for this course. The course is structured in a manner that covers the very basics of the topics as well. So if you’re a complete beginner, this is a great course to start with.

What are the requirements for the classes?

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You’ll need a secure and stable internet connection on a well functioning device such as a laptop or mobile phone. We also recommend keeping a notepad and pen/pencil alongside to jot down notes.

Who do I contact for more information regarding the course?

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You can get in touch with us via email - or call us at the phone number provided in the ‘Contact Us’ section.

What are the possible career options for Artificial Intelligence?

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Some options are - Data Analyst, Natural Language Processing, AI Engineer, AI Researcher and User Experience.