Comprehensive Guide to Artificial Intelligence(AI) for All

Comprehensive Guide to Artificial Intelligence(AI) for All, available at $44.99, has an average rating of 4, with 106 lectures, 11 quizzes, based on 30 reviews, and has 332 subscribers.

You will learn about 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 This course is ideal for individuals who are Folks who are curious about AI and want to learn it, in the fastest, easiest and the most effective way It is particularly useful for Folks who are curious about AI and want to learn it, in the fastest, easiest and the most effective way.

Enroll now: Comprehensive Guide to Artificial Intelligence(AI) for All

Summary

Title: Comprehensive Guide to Artificial Intelligence(AI) for All

Price: $44.99

Average Rating: 4

Number of Lectures: 106

Number of Quizzes: 11

Number of Published Lectures: 106

Number of Published Quizzes: 11

Number of Curriculum Items: 117

Number of Published Curriculum Objects: 117

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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, Matplotliball 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 Gym

The 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.

Course Curriculum

Chapter 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

Chapter 2: IBM Watson – Supervised and Unsupervised Machine Learning Models

Lecture 1: Building Supervised and Unsupervised Machine learning Models using IBM Watson

Lecture 2: Approach to building machine learning Models

Lecture 3: Account Setup and Configuration

Lecture 4: Supervised – Building a Binary classification(ML) model and Uploading Data

Lecture 5: Supervised -Training and testing your model using logistic regression

Lecture 6: Supervised – Building a Multi class classification(ML) model end to end

Lecture 7: Unsupervised – Building a Regressive(ML) Model end to end

Lecture 8: Performance Evaluation Parameters for ML Algorithms

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

Lecture 1: Introduction to the Section

Lecture 2: IBM Watson – Text to Speech

Lecture 3: IBM Watson – Speech to Text

Lecture 4: IBM Watson – Semantic extraction

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

Lecture 1: Introduction to the Section and the experiment sheet

Lecture 2: Building a Perceptron

Lecture 3: Building a Feed Forward Neural Network with one Hidden layer – Supervised

Lecture 4: Building a Deep Feed Forward Neural Network – Supervised

Lecture 5: High Level Introduction to Tensor Flow, Data and Setup – Unsupervised

Lecture 6: Building a Regressive Feed Forward Neural Network(FFNN) – Unsupervised

Lecture 7: Building a SHALLOW Regressive Feed Forward Neural Network – Unsupervised

Lecture 8: Building a DEEP Regressive FFNN – Unsupervised

Lecture 9: Building a Regressive FFNN with different AdamOptimizer

Lecture 10: Building a Regressive FFNN with different learning Rates and Epochs

Lecture 11: Performance Analysis of Feed Forward Neural Networks

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

Lecture 1: Section Introduction and data

Lecture 2: CNN for MNIST Architecture Walkthrough

Lecture 3: IBM Watson Account Setup Basics

Lecture 4: CNN – Setup and First Run with MNIST example – Part 1

Lecture 5: CNN – Setup and First Run with MNIST example – Part 2

Lecture 6: CNN for MNIST with SGD

Lecture 7: Optimizing CNN for MNIST

Lecture 8: CNN for CIFAR 10

Lecture 9: Optimization options for CNN on CIFAR 10

Lecture 10: CNN – Unconverging Experiments

Chapter 6: Recurrent Neural Network (RNN) with Mathworks

Lecture 1: Introduction the section

Lecture 2: Japanese Vowels classification with LSTM- Walk through of Mathworks example

Lecture 3: Classification of human activities with LSTM- Walk through of Mathworks example

Chapter 7: Way forward

Lecture 1: Introduction to sections below

Lecture 2: Installation of softwares and libraries for all the sections below

Chapter 8: Introduction to Python with Jupyter Notebook

Lecture 1: Introduction to Python

Lecture 2: Numbers and Variables

Lecture 3: Strings and Lists

Lecture 4: Control Structures

Lecture 5: Control Structures Part 2

Lecture 6: Data Structures Part 1

Lecture 7: Data Structures Part 2

Lecture 8: Classes Part 1

Lecture 9: Classes Part 2

Lecture 10: I/o , Error Handling and Library Walk through

Chapter 9: Introduction to Numpy

Lecture 1: Introduction and Creating arrays

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

Instructors

Junaid Ahmed
Technology Author, Leader, Strategist and Trainer

Rating Distribution

1 stars: 0 votes
2 stars: 4 votes
3 stars: 5 votes
4 stars: 12 votes
5 stars: 9 votes

Frequently Asked Questions

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