Comprehensive Guide To Artificial Intelligence(Ai) For All

Learn ML, NLP, Deep, Transfer and Reinforcement learning with IBM Watson, Tensorflow Sim, Keras, OpenAI Gym and more

What you'll learn
Clearly define what is AI and Deep Learning
Build Convolutional Neural Network on IBM Watson for MNIST and CIFAR 10 Datasets (No coding)
Build Supervised and Unsupervised Machine learning Models using IBM Watson (No coding)
Test Natural Language Processing (NLP) models using IBM Watson
Build VGG like nets, Stateful RNN nets, reuse ResNet50 using Keras
Test Reinforcement Learning with Keras and OpenAI Gym
Test Recurrent Neural Network (RNN) on Mathworks
Learn to code with Python the easy way
Test Feed Forward Neural Networks(Classification and Regression) on Tensor Flow simulator and Google Colab
Solve popular data sets like MNIST, CIFAR 10, with CNN using Keras
Learn a few useful and important application of popular libraries like Numpy, Pandas, Matplotlib
Migrate Deep Neural Network models from IBM Watson to run on local your Jupyter notebook
Apply Transfer Learning techniques such as Reusing, Retraining with keras
Be able to identify the positive and the negative impact that AI will create
Requirements
Basic knowledge of IT, Maths and Data
Description
If I can tell you, stop what ever you are doing and do a certain thing. I would say "Learn about AI and the impact it is going to have in your professional life, personal life and much more in the immediate future".Welcome to this exciting and eye opening course on Artificial Intelligence and more. 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.The course has over 11 hours of content with 100+ easy to consume, high quality, visually engaging, condensed and edited videos, over 10 Quizzes to check your understanding, reference material and code for further study.  This course has 3 parts, first 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 Smack in the middle we have easy and intuitive primer sections on how to code using Python, and also how to use popular libraries like Numpy, Pandas, Matplotlib all on the awesome browser based coding platform Jupyter notebook. These middle sections will prepare you for the next sections.The final set of sections we will take a deeper dive in testing real life use cases and AI applications with Keras, Keras-Reinforcement, OpenAI Gym and more. The focus will be on building the student's confidence in understanding the data and building solutions. In the  final sections you will see a bit more of code but the best part would be that by the end of the sections you will be running AI solutions powered by Deep Neural Networks on a browser with Jupyter Notebook on your Laptop !- Solving popular data sets like  MNIST, CIFAR 10, with CNN, Keras and Jupyter notebook running on your laptop- Building VGG like nets and Stateful RNN nets using Keras- Migrating Neural networks from IBM Watson to run on local your Jupyter notebook - Applying Transfer Learning technique such as Reusing, Retraining with keras- Testing Reinforcement Learning with Keras and OpenAI GymThe essence of the later sections will be to understand that there are so many libraries and resources available to you, and that it has been made easy for everyone. You just have to identify what you need to be done and look in the right direction.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: What is AI and its Impact on our society and future

Lecture 1 **Resources and Jupyter Notebooks **

Lecture 2 Introduction the course sections

Lecture 3 What is Artificial Intelligence (AI)

Lecture 4 Mapping human functions to AI technologies

Lecture 5 AI - Branches of Machine Learning Algorithms

Lecture 6 AI - Supervised Machine Learning Algorithms and Applications

Lecture 7 AI - Unsupervised Machine Learning Algorithms and Applications

Lecture 8 AI - Natural Language Processing and Applications

Lecture 9 AI - Computer Vision and Applications

Lecture 10 AI - IOT and Applications

Lecture 11 What are Neural Networks ?

Lecture 12 Neural Networks - Perceptron

Lecture 13 What are Deep Neural Networks ?

Lecture 14 Feed Forward Neural Networks (FFNN) Structure and Forward pass

Lecture 15 Input - Feed Forward Neural Networks (FFNN)

Lecture 16 Learning Phase - Feed Forward Neural Networks (FFNN)

Lecture 17 Back propagation and learning step -Feed Forward Neural Networks (FFNN)

Lecture 18 Applications and Limitations of Feed Forward Neural Networks( FFNN)

Lecture 19 CNN Introduction

Lecture 20 CNN - Convolution and Relu Layer

Lecture 21 CNN - Max Pooling Layer

Lecture 22 CNN - Example end to end

Lecture 23 Recurrent Neural Networks (RNN)

Lecture 24 RNN Architecture

Lecture 25 Generative Adversarial Networks (GAN)

Lecture 26 Reinforcement Learning

Lecture 27 Transfer Learning

Lecture 28 Market Potential of AI

Lecture 29 Who will loose to AI

Lecture 30 Need for retraining and reskilling

Lecture 31 How to take advantage and benefit from AI

Lecture 32 References for further study

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

Lecture 33 Building Supervised and Unsupervised Machine learning Models using IBM Watson

Lecture 34 Approach to building machine learning Models

Lecture 35 Account Setup and Configuration

Lecture 36 Supervised - Building a Binary classification(ML) model and Uploading Data

Lecture 37 Supervised -Training and testing your model using logistic regression

Lecture 38 Supervised - Building a Multi class classification(ML) model end to end

Lecture 39 Unsupervised - Building a Regressive(ML) Model end to end

Lecture 40 Performance Evaluation Parameters for ML Algorithms

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

Lecture 41 Introduction to the Section

Lecture 42 IBM Watson - Text to Speech

Lecture 43 IBM Watson - Speech to Text

Lecture 44 IBM Watson - Semantic extraction

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

Lecture 45 Introduction to the Section and the experiment sheet

Lecture 46 Building a Perceptron

Lecture 47 Building a Feed Forward Neural Network with one Hidden layer - Supervised

Lecture 48 Building a Deep Feed Forward Neural Network - Supervised

Lecture 49 High Level Introduction to Tensor Flow, Data and Setup - Unsupervised

Lecture 50 Building a Regressive Feed Forward Neural Network(FFNN) - Unsupervised

Lecture 51 Building a SHALLOW Regressive Feed Forward Neural Network - Unsupervised

Lecture 52 Building a DEEP Regressive FFNN - Unsupervised

Lecture 53 Building a Regressive FFNN with different AdamOptimizer

Lecture 54 Building a Regressive FFNN with different learning Rates and Epochs

Lecture 55 Performance Analysis of Feed Forward Neural Networks

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

Lecture 56 Section Introduction and data

Lecture 57 CNN for MNIST Architecture Walkthrough

Lecture 58 IBM Watson Account Setup Basics

Lecture 59 CNN - Setup and First Run with MNIST example - Part 1

Lecture 60 CNN - Setup and First Run with MNIST example - Part 2

Lecture 61 CNN for MNIST with SGD

Lecture 62 Optimizing CNN for MNIST

Lecture 63 CNN for CIFAR 10

Lecture 64 Optimization options for CNN on CIFAR 10

Lecture 65 CNN - Unconverging Experiments

Section 6: Recurrent Neural Network (RNN) with Mathworks

Lecture 66 Introduction the section

Lecture 67 Japanese Vowels classification with LSTM- Walk through of Mathworks example

Lecture 68 Classification of human activities with LSTM- Walk through of Mathworks example

Section 7: ***Way forward***

Lecture 69 Introduction to sections below

Lecture 70 Installation of softwares and libraries for all the sections below

Section 8: Introduction to Python with Jupyter Notebook

Lecture 71 Introduction to Python

Lecture 72 Numbers and Variables

Lecture 73 Strings and Lists

Lecture 74 Control Structures

Lecture 75 Control Structures Part 2

Lecture 76 Data Structures Part 1

Lecture 77 Data Structures Part 2

Lecture 78 Classes Part 1

Lecture 79 Classes Part 2

Lecture 80 I/o , Error Handling and Library Walk through

Section 9: Introduction to Numpy

Lecture 81 Introduction and Creating arrays

Lecture 82 Creating 1D, 2D, 3D Arrays

Lecture 83 Creating Dummy Data

Lecture 84 Reshaping 1D, 2D, and 3D Arrays

Lecture 85 Slicing, Dicing and Splitting Arrays

Section 10: Introduction to Pandas, Matplotlib and OpenCV

Lecture 86 Introduction and Data Frames

Lecture 87 Slicing and Dicing

Lecture 88 Import/Export(csv,excel,json,pickle)

Lecture 89 Basic Plotting with Matplotlib

Lecture 90 Displaying CIFAR 10 images with Matplotlib

Lecture 91 Object Recognition, Video Analysis and more with OpenCV

Section 11: Working with Keras - Advanced Deep Learning

Lecture 92 Introduction to Keras

Lecture 93 Multi Class Classification using a Deep Neural Network with Keras

Lecture 94 Binary Classification using a Deep Neural Network with Keras

Lecture 95 Building a VGG16 like Deep Neural Network with Keras

Lecture 96 Solving MNIST data set using a Deep Neural Network with Keras - Part 1

Lecture 97 Solving MNIST data set using a Deep Neural Network with Keras - Part 2

Lecture 98 Solving MNIST data set using a Deep Neural Network with Keras - Part 3

Lecture 99 Migrating models from IBM Watson to run on local your Jupyter Notebook

Lecture 100 Building Stateful RNN on Jupyter Notebook

Section 12: Transfer and Reinforcement Learning

Lecture 101 Transfer Learning - Reusing a Pre-built ResNet50 Model to Predict

Lecture 102 Transfer Learning feature extraction from VGG16 Model

Lecture 103 Transfer Learning - Retraining the last layers

Lecture 104 Reinforcement Learning - Cart-Pole Example Part 1

Lecture 105 Reinforcement Learning - Cart-Pole Example Part 2

Lecture 106 Reinforcement Learning - Pendulum Example

Folks who are curious about AI and want to learn it, in the fastest, easiest and the most effective way