Artificial Intelligence (AI) and Machine Learning (ML) Foundation Course

32 Hours | 4 Hours Instruction/day | 8 weeks

The AI & ML foundation course is perfect for beginners who want to explore and take a step forward in this domain. The training offered is a perfect blend of theoretical concepts and practical learning with real-time AI & ML projects helping you gain the in-demand skills of the market

Course Description

This Artificial Intelligence & Machine Learning course will help you learn the fundamentals of Machine Learning and Deep Learning. You will also get to know how to model neural networks, and implement AI projects based on Deep Learning using TensorFlow and Keras. You will get a complete understanding of Convolutional Neural Networks, Recurrent Neural Networks, and other Deep Architectures along with their uses in complex raw data using TensorFlow. 

This course has been designed by handpicked industry experts to develop your expertise through case-studies, practice modules, and real-time projects.

  • Introduction to Artificial Intelligence and Machine Learning

  • Fundamentals of Deep Learning techniques

  • Learning TensorFlow and its usage

  • How to simplify Machine Learning using Keras

  • What is Deep Learning

  • Understanding key parameters in a neural network's architecture

  • Convolution Neural Networks and their applications

  • Recurrent Neural Networks and their applications

  • Real world projects

Course Outcome

The course will give you a holistic understanding of AI, Machine Learning & Deep Learning. You will learn about Deep Learning Algorithms which have been most impactful in taking the AI field forward. At the end of the course, you will be ready to apply these concepts at work to handle Classification & Regression problems.

On successful completion of the training, you become eligible for the certification exam. The certification exam tests your knowledge and understanding of the subject. Upon clearing this certification exam you will be awarded a GreyCampus - AI and Machine Learning Developer that helps to validate your knowledge and establish your credibility in the market.

AI and ML Sample Certificate

Course Components

  • 32 hours of Instructor-led live-online AI & ML training

  • 1-year access to audio-video lectures

  • One-on-One Expert & Certified Trainers Support

  • Complimentary access to Python self-learning course

  • Jupyter Notebooks and other learning aids

  • Case studies and real-world scenario-based sessions

  • Assessments and Quizzes

  • Other downloadables for your learning

  • Learning assistance and support

Course Benefits

Live Sessions

Live and interactive instructor sessions

Top Instructors

Learn from the practicing experts in the Industry.

Hands-on Labs

Sessions include hands-on projects in live labs.


1-year access to audio video lectures

Instructor Support

Ongoing support from a panel of instructors to aid your learning 


Experience end-to-end project completion in the program


The training modules are structured to help working professionals who aspire to upgrade their skills without compromising on their personal or professional commitments. Giving you the flexibility and convenience to attend the training from anywhere; all you need is a high-speed internet connection.

The session is scheduled once every week (either a weekday/ weekend) for eight weeks with 4 hours of interactive, hands-on training. Check the schedule below to register for a batch of your choice.


A sound understanding of programming languages such as Python will be beneficial, however, not mandatory.

This course is highly recommended for:

  • IT Professionals who aspire to be a Data Scientist or an expert in AI & ML

  • Project Managers, Solution Architect, IT Architects who wish to gain expertise in Deep Learning Algorithms

  • Business Analysts and Research analysts

  • Any technology enthusiast who has decent programming skills

  • Freshers who want to step into Data Science as a career


In this AI & ML hands-on training, you will be working on several projects and assignments that help you implement the learnings in your class. You will also work on a full-length real-world project:

Natural Language Processing Use case:

  • How do customers feel about a Product?
    1. Use a Deep Neural Network (DNN) to do a Sentiment Analysis on Amazon Reviews dataset.

Computer Vision Use case:

  • What is there in a picture?
    1. Using Convolutional Neural Network, classify images from CIFAR-10 dataset. Use different techniques learned in the program to improve the results.

These projects will help you gain confidence to implement AI solutions and hit the ground running with AI initiatives immediately!

Course Curriculum

  • How do machines work?
  • Can machines see the world?
  • Google Photos: Machine with a Vision
  • Understanding Natural Language
  • Learning Complex Games
  • What is Artificial Intelligence (AI)
  • What is Machine Learning (ML)
  • How Machine builds the logic
  • Machine's Goal: Understanding Loss function
  • The World of Gradient Descent - I
  • The World of Gradient Descent - II
  • Linear Regression Algorithm
  • Quiz: Understanding AI and ML
  • ML Math: Understanding Vector and Matrix
  • What's Next
  • Pre-requisite: What to know before going further
  • Components of Machine Learning
  • Installation Instructions
  • Building Hello World in TensorFlow
  • Notebook: Hello World in TensorFlow
  • Understanding Computational Graph
  • Computational Graph for Linear Regression
  • Exercise: Boston Housing Prices Predictor
  • What is Data Normalization?
  • Exercise: Boston Housing Prices with Data Normalization
  • Notebook: Housing Price Predictor
  • Assignment: Housing Predictor on Google Colab
  • What's Next
  • Understanding the role of Keras in TensorFlow
  • Keras vs TensorFlow's Lower Level APIs
  • Exercise: Building a Linear Regression model in Keras
  • Notebook: Boston Housing Predictor in Keras
  • Exercise: Using the ML model for Prediction
  • Notebook: Predict Housing Prices using ML Model
  • Assignment: How many Bikes are needed?
  • Regression vs Classification
  • Math in Classification
  • Using SoftMax in Classification
  • Loss and Accuracy in Classification
  • Exercise: Classify Handwritten numbers - I
  • Exercise: Classify Handwritten numbers - II
  • Notebook: Hand-written digits Predictor with DL
  • Mini-batching in ML
  • Exercise: Mini-batching in ML
  • Notebook: Mini-batching for MNIST Dataset
  • Exercise: Prediction using a Classification model
  • Improving ML model - Hyperparameters
  • What's Next
  • Problem with Linear Algorithm
  • How to capture Complex Logic?
  • What is Deep Learning?
  • Exercise: Deep Learning on MNIST Classification
  • Notebook: MNIST Classification with Deep Learning
  • Using TensorBoard: Visualizing ML Model
  • Notebook: Using TensorBoard
  • Activation functions in Deep Learning
  • Learning rate Decay
  • Dropout for Overfitting
  • Optimizers: Momentum & Nestrove Momentum
  • Optimizers: Adam, Adagrad
  • Hyper-parameters in Deep Learning
  • Exercise: ReLU, Adam & Dropout
  • Notebook: Applying ReLU, ADAM, and Dropout
  • Assignment: CIFAR-10 Classification
  • What's Next
  • Problem with Dense Layers
  • Understanding Convolutional Layer
  • Visualizing a Filter in Convolutional Layer
  • Filter Stride, Padding in Convolutional Layer
  • Convolution Neural Network (CNN) and Pooling
  • Exercise: CNN for MNIST Classification
  • Notebook: Using CNN for MNIST Classification
  • Assignment: CIFAR-10 Classification using CNN
  • What's Next
  • Working with Textual Data
  • TF-IDF Vectorization
  • Exercise: Sentiment Analysis with TF-IDF
  • Notebook: Movie Reviews Sentiment Analysis
  • Working with Sequences
  • Visualizing Recurrent Neural Network (RNN)
  • Math of RNN
  • Long short term memory (LSTM) Cell
  • Exercise: LSTM for Sentiment Analysis
  • Notebook: LSTM for IMDB Movie Reviews
  • Gated Recurrent Unit (GRU)
  • Assignment: Reuters Newswire Classification
  • What's Next

Program Dates

Start Date Time Training Mode


All that you need is a computer/laptop with a high-speed internet connection for distraction-free learning.

We will be using OpenSource software like TensorFlow, Python, etc. You will be guided with instructions to install these during our training.

Even if you miss Instructor Led session, you will be able to go through the topics using Video sessions which will be made available to you. You can always connect with us, by dropping an email to and we will be happy to assist you.

For any questions outside the Instructor-led sessions, you can contact the Support team by dropping an email to Our dedicated support team will assist you promptly with your questions.

This program does not require any technical or programming experience. Even if you are inclined towards programming or have some exposure, you are eligible to take up this training.

Additionally, this program is bundled with Python Programming self-learning course that will help you learn the basics of programming.

For more information

Download Artificial Intelligence (AI) and Machine Learning (ML) Foundation Course brochure for complete information.