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

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.

USD 150

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Program 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

Program Outline

Skills

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.

Alignment

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

Outcome

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

Understanding AI

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
Using TensorFlow

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
Simplify ML using Keras

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
Deep Learning

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
Recurrent Neural Networks Download full course agenda/ brochure

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Frequently asked questions

Registration

1. Any hardware requirements or specifications needed to attend the training?

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

2. Do I need to download any software for the training?

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

3. What if I miss one day of 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 support@greycampus.com and we will be happy to assist you.

4. I do not have any technical experience, can I join?

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.

5. Does GreyCampus guarantee 100% money back?

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 support@greycampus.com 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 support@greycampus.com. 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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