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

2k learners

The AI and ML foundation course is a complete beginner’s course with a blend of practical learning and theoretical concepts. This course offers you the basic fundamentals of AI and Machine Learning.

Contact Us
  • 1-year e-learning access

  • Course completion certificate

  • 100% money-back guarantee

Options available for

Online Self Learning USD 150 (USD 200) Enroll Now
Live Online Training -- Get Quote
Traditional Classroom -- Get Quote

Group Training

Looking for a personalized training for a group (3 or more participants) at your preferred location?
Contact us

Course Overview

The most advanced phenomenon in the history of Computer generations is the technological development in the field of Artificial Intelligence(AI). Here in this tutorial course, we bring you the fundamentals. Provide basics of Deep learning by giving an insight of Convolutional Neural Networks and Recurrent Neural Networks. All this will be done using TensorFlow, the programming library for Deep Learning. This course is a must-have for all those who are exploring the field of AI and ML.

Course Outline


Gain 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.


At the end of the course, you will be ready to apply these concepts at work to handle Classification & Regression problems.

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
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 role of Keras in TensorFlow
Keras vs TensorFlow's Lower Level APIs
Exercise: Building Linear Regression model in Keras
Notebook: Boston Housing Predictor in Keras
Exercise: Using 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 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


Call us


Live chat


Contact us

Frequently Asked Questions


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.
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.

Online Self-Learning courses have a 3-day refund policy. If you are not satisfied with the course and report this over an email to within 3 days from the date of purchase, we will refund the entire amount.

This guarantee is considered void in any of the following cases. If the candidate has-

  • Completed more than 30% of the course

  • Downloaded any of the offline materials

  • Attempted one or more mock exams

  • Used exam vouchers

The guarantee is valid for participants who have paid the entire enrollment fee.

500 +

Expert Instructors

100 +


150000 +

Professionals Trained

Got queries?