Hands-On Introduction To Artificial Intelligence(Ai)

Learn Basics of Machine learning, Supervised , Unsupervised, FFNN, CNN, NLP, RNN

What you'll learn
Fundamental concepts of Artificial Intelligence
Be able to identify the positive and the negative impact that AI will create
Clearly define what is AI and Deep Learning
Test Feed Forward Neural Networks(Classification and Regression) on Tensor Flow simulator and Google Colab
Test Natural Language Processing (NLP) models using IBM Watson
Build Convolutional Neural Network(CNN) on IBM Watson for MNIST and CIFAR 10 Datasets (No coding)
Build Supervised and Unsupervised Machine learning Models using IBM Watson (No coding)
Test Recurrent Neural Network (RNN) on Mathworks
Requirements
Basic knowledge of IT, Maths and Data
Description
Welcome to this exciting and eye opening course on Artificial Intelligence(Part 1) . We believe that AI will touch everybody in some level, whether you are a technical or a non technical person and also that you can excel in many roles in AI with just a functional understanding of coding.We will start from the basics , break myths, clarify your understanding as to what is this mysterious term AI, (many are surprised to know that it encompasses, Machine Learning, NLP,Computer Vision, IOT, Robotics and more). We will also understand the current state of AI and its positive and negative impact in the near future.Then we will apply the concepts we learnt with zero to little coding Involved.- Machine learning (Supervised and Unsupervised)  with IBM Watson - Natural Language Processing (NLP) with IBM Watson- Feed Forward Neural Networks (FFNN) with Tensor Flow Simulator- Convolutional  Neural Networks with (CNN) with IBM Watson-  Recurrent Neural Networks (RNN) with Mathworks AI brings tremendous opportunity like higher economic growth, productivity and prosperity but the picture is not all rosy. lets look at some data points from the renowned Mckinsey&Company." 250 million new jobs are likely to be created by 2030"*" In the midpoint adoption scenario 400 million Jobs are likely to be lost by 2030"*" In the midpoint adoption scenario 75 million will need change occupational categories by 2030"*AI is the top priority for Companies, governments and institutions alike. AI surpasses a certain product, or vertical, or function, or a specific industry , it encompasses everything. It is all prevalent.Based on the report there will be considerable shortages in the IT sector and companies are looking to fill these gaps by retraining, hiring, redeploying, contracting and even hiring from non traditional sources. Technological skill is the TOP skill that will be required during this time and by one research they will need 250,000 data scientists by 2030.  If you develop these skills and knowledge , you can take advantage of this revolution irrespective of your role, company or Industry you belong to. So if you are "AI ready then you are future ready"AI is here to stay and the ones who get on board fast and adapt to it will be in a much better position to face the exciting but uncertain future.Choose Success , make yourself invaluable and irreplaceable. I will see "YOU" on the inside.God Speed.

Overview

Section 1: Introduction

Lecture 1 What is Artificial Intelligence (AI)

Lecture 2 Mapping human functions to AI technologies

Lecture 3 AI - Branches of Machine Learning Algorithms

Lecture 4 AI - Supervised Machine Learning Algorithms and Applications

Lecture 5 AI - Unsupervised Machine Learning Algorithms and Applications

Lecture 6 AI - Natural Language Processing and Applications

Lecture 7 AI - Computer Vision and Applications

Lecture 8 AI - IOT and Applications

Lecture 9 What are Neural Networks ?

Lecture 10 Neural Networks - Perceptron

Lecture 11 What are Deep Neural Networks ?

Lecture 12 Feed Forward Neural Networks (FFNN) Structure and Forward pass

Lecture 13 Input - Feed Forward Neural Networks (FFNN)

Lecture 14 Learning Phase - Feed Forward Neural Networks (FFNN)

Lecture 15 Back propagation and learning step -Feed Forward Neural Networks (FFNN)

Lecture 16 Applications and Limitations of Feed Forward Neural Networks( FFNN)

Lecture 17 CNN Introduction

Lecture 18 CNN - Convolution and Relu Layer

Lecture 19 CNN - Max Pooling Layer

Lecture 20 CNN - Example end to end

Lecture 21 Recurrent Neural Network (RNN)

Lecture 22 RNN Architecture

Lecture 23 Generative Adversarial Networks (GAN)

Lecture 24 Reinforcement Learning

Lecture 25 Transfer Learning

Lecture 26 Market Potential of AI

Lecture 27 Who will loose to AI

Lecture 28 Need for retraining and reskilling

Lecture 29 How to take advantage and benefit from AI

Section 2: IBM Watson - Supervised and Unsupervised Machine Learning Models

Lecture 30 Building Supervised and Unsupervised Machine learning Models using IBM Watson

Lecture 31 Approach to building machine learning Models

Lecture 32 Account Setup and Configuration

Lecture 33 Supervised - Building a Binary classification(ML) model and Uploading Data

Lecture 34 Supervised -Training and testing your model using logistic regression

Lecture 35 Supervised - Building a Multi class classification(ML) model end to end

Lecture 36 Unsupervised - Building a Regressive(ML) Model end to end

Lecture 37 Performance Evaluation Parameters for ML Algorithms

Section 3: Natural Language Processing (NLP) with IBM Watson

Lecture 38 Introduction to the Section

Lecture 39 IBM Watson - Text to Speech

Lecture 40 IBM Watson - Speech to Text

Lecture 41 IBM Watson - Semantic extraction

Section 4: Feed Forward Neural Networks (FFNN) with Tensor Flow Simulator and Google Colab

Lecture 42 Introduction to the Section and the experiment sheet

Lecture 43 Building a Perceptron

Lecture 44 Building a Feed Forward Neural Network with one Hidden layer - Supervised

Lecture 45 Building a Deep Feed Forward Neural Network - Supervised

Lecture 46 High Level Introduction to Tensor Flow, Data and Setup - Unsupervised

Lecture 47 Building a Regressive Feed Forward Neural Network(FFNN) - Unsupervised

Lecture 48 Building a SHALLOW Regressive Feed Forward Neural Network - Unsupervised

Lecture 49 Building a DEEP Regressive FFNN - Unsupervised

Lecture 50 Building a Regressive FFNN with different AdamOptimizer

Lecture 51 Building a Regressive FFNN with different learning Rates and Epochs

Lecture 52 Performance Analysis of Feed Forward Neural Networks

Section 5: Convolutional Neural Networks (CNN) with IBM Watson

Lecture 53 Section Introduction and data

Lecture 54 CNN for MNIST Architecture Walkthrough

Lecture 55 IBM Watson Account Setup Basics

Lecture 56 CNN - Setup and First Run with MNIST example - Part 1

Lecture 57 CNN - Setup and First Run with MNIST example - Part 2

Lecture 58 CNN for MNIST with SGD

Lecture 59 Optimizing CNN for MNIST

Lecture 60 CNN for CIFAR 10

Lecture 61 Optimization options for CNN on CIFAR 10

Lecture 62 CNN - Unconverging Experiments

Section 6: Recurrent Neural Network (RNN) with Mathworks

Lecture 63 Introduction the section

Lecture 64 Japanese Vowels classification with LSTM- Walk through of Mathworks example

Lecture 65 Classification of human activities with LSTM- Walk through of Mathworks example

Techno functional, Business and Power users who want to understand the fundamentals of AI with theory and practicals