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.

  • 1-year access to audio-video lectures

  • Course completion certificate

  • 100% money-back guarantee

USD 150

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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.
  • 1-year access to audio-video lectures
  • Course completion certificate

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


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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 live-online class, 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.

Without BootCamp: Training sessions without BootCamps are governed by a 3-day trial policy. In the unlikely case of being dissatisfied with the course content, a delegate must reach out to the support team at within 3 days from the date of purchase. After verification, the entire amount will be credited in the original payment mode.
The refund policy mentioned above will stand void and thus unenforceable in case a delegate is found to be involved in any of the following scenarios:
Accessing more than 30% of the content available
Downloading eBook or any offline material
Attempting one or more mock tests
Using exam vouchers
BootCamp: BootCamp training refunds are subject to the 1st day of 1st training class refund policy. Upon finding the training unsatisfactory, the delegate must inform GreyCampus within 24 hours from the time when the first session began. All such communications should be directed to the support team reachable at In such cases, the delegate will be refunded the entire amount paid in the original mode of payment.
The guarantee is valid for participants who have paid the entire enrollment fee.
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