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

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

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TRAINING SCHEDULES

Live-online classroom schedules

TOT (UTC +13) CHAST (UTC +12:45) ANAT (UTC +12) SBT (UTC +11) LHST (UTC +10:30) AEDT (UTC +10) ACST (UTC +9:30) JST (UTC +9) ACWST (UTC +8:45) PYT (UTC +8:30) HKT (UTC +8) SGT (UTC +8) MYT (UTC +8) WIB (UTC +7) MMT (UTC +6:30) BST (UTC +6) NPT (UTC +5:45) IST (UTC +5:30) UZT (UTC +5) IRDT (UTC +4:30) GST (UTC +4) AST (UTC +3) CEST (UTC +2) BST (UTC +1) GMT (UTC +0) CVT (UTC -1) WGST (UTC -2) NDT (UTC -2:30) ART (UTC -3) NDT (UTC -3:30) EDT (UTC -4) CDT (UTC -5) MDT (UTC -6) PDT (UTC -7) AKDT (UTC -8) HADT (UTC -9) MART (UTC -9:30) HAST (UTC -10) NUT (UTC -11) AOE (UTC -12) WAT(UTC +1)

UTC -4 EDT

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Artificial Intelligence & Machine Learning Training program overview

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.

Overview

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. 

Overview

Alignment

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

Overview

Outcome

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

The registration process

Once you have completed our simplified enrolment process, you’ll receive an email confirmation with your payment receipt in your registered email ID. You can then access the entire content of the online student portal immediately by logging in to your account on our site. Should you require any assistance please reach out to us via email (support@greycampus.com) or via our online chat system.

The course curriculum

The curriculum for this Blockchain Security training incorporates all updates to the certification exam. The following is a list of broad topics covered:

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
  • 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
  • End of Module Test
  • 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
  • End of Module Test
  • 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
  • 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
  • End of Module Test
  • 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
  • End of Module Test

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ANSWERS TO FREQUENTLY ASKED QUESTIONS

We love questions, almost as much as we love providing answers. Here are a few samplings of what we’re typically asked, along with our responses:

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.

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 support@greycampus.com 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 elearning@greycampus.com 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.

Have more questions?

If you want to learn more about our training program, have questions about the Artificial Intelligence (AI) and Machine Learning (ML) Foundation Course or want to schedule a customized group training at your location, email us at sales@greycampus.com

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