Unleashing The Power Of Ai

A Step-by-Step Guide to Building Your Own Intelligent System

What you'll learn
Understand the basics of artificial intelligence and its different subfields.
Learn how to use common AI techniques such as supervised and unsupervised learning.
Gain proficiency in programming languages and libraries commonly used in AI such as Python and TensorFlow.
Learn how to train and evaluate machine learning models.
Develop the ability to identify and solve real-world problems using AI.
Understand the ethical and societal implications of AI.
Learn how to interpret and communicate the results of AI models to non-technical stakeholders.
Develop the ability to continue learning and staying current with the rapidly evolving field of AI.

Requirements
Enthusiasm and a desire to learn
High School Math
Basic Python knowledge

Description
"Unleashing the Power of AI: A Step-by-Step Guide to Building Your Own Intelligent System" is a comprehensive course that will take students on a journey of learning the fundamentals of artificial intelligence and its applications. By the end of this course, students will have the knowledge and skills to build their own intelligent systems, using cutting-edge AI techniques such as machine learning, deep learning, and reinforcement learning.Throughout the course, students will learn the key concepts and techniques of AI, including supervised and unsupervised learning, natural language processing, computer vision, and decision-making. They will also learn about the latest developments in AI, such as deep learning and reinforcement learning, and how to apply these techniques to real-world problems.The course will also cover the ethical and societal implications of AI, and students will learn how to ensure that their AI systems are safe, fair, and transparent.The course will be hands-on, with a focus on practical, real-world applications. Students will have the opportunity to work on projects, case studies, and exercises, which will help them to apply the concepts they learn in class to real-world situations.This course is suitable for beginners who want to learn the basics of AI, as well as for those who want to deepen their understanding of AI and its applications. By the end of the course, students will have the knowledge and skills to build their own intelligent systems, and they will be able to unleash the power of AI to solve real-world problems.Learn Ai from Beginner to expert AI skills – Learn to code self-improving AI for a range of purposes. In fact, we code together with you. Every tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.Code templates – Plus, you’ll get downloadable Python code templates for every AI you build in the course. This makes building truly unique AI as simple as changing a few lines of code. If you unleash your imagination, the potential is unlimited.Intuition Tutorials – Where most courses simply bombard you with dense theory and set you on your way, we believe in developing a deep understanding for not only what you’re doing, but why you’re doing it. That’s why we don’t throw complex mathematics at you, but focus on building up your intuition in coding AI making for infinitely better results down the line.Real-world solutions – You’ll achieve your goal in not only 1 game but in 3. Each module is comprised of varying structures and difficulties, meaning you’ll be skilled enough to build AI adaptable to any environment in real life, rather than just passing a glorified memory “test and forget” like most other courses. Practice truly does make perfect.In-course support – We’re fully committed to making this the most accessible and results-driven AI course on the planet. This requires us to be there when you need our help. That’s why we’ve put together a team of professional Data Scientists to support you in your journey, meaning you’ll get a response from us within 48 hours maximum.

Overview
Section 1: Why Everyone should learn AI.

Lecture 1 Welcome to Unleashing the Power of AI

Lecture 2 Introduction

Lecture 3 Installing Anaconda on Windows or Mac computers.

Section 2: Fundamentals of Reinforcement Learning

Lecture 4 Reinforcement Learning (RL)

Lecture 5 Reinforcement Learning (RL) Video

Lecture 6 The Plan video

Lecture 7 Understanding The Bellman Equation

Lecture 8 Understanding The Bellman Equation Video

Lecture 9 Breaking down the Markov Decision Process

Lecture 10 Markov Decision Process Video

Lecture 11 Understanding the difference between Policy vs Plan in the context of (RL)

Lecture 12 Policy vs Plan

Lecture 13 Reinforcement Learning "Living Penalty and Reward"

Lecture 14 "Living Penalty and Reward" Video

Lecture 15 An introduction to Q-Learning: reinforcement learning

Lecture 16 Q-Learning: reinforcement learning Video

Lecture 17 Simple Reinforcement Learning: Temporal Difference Learning

Lecture 18 Temporal Difference Learning Video

Section 3: Visualization

Lecture 19 Plan

Lecture 20 Q-Learning Visualization

Lecture 21 Q-Learning Visualization Video

Lecture 22 Using Reinforcement Learning to solve Gridworld

Lecture 23 Video Using Reinforcement Learning to solve Gridworld

Section 4: Deep Q-learning Intuition

Lecture 24 Deep Q-Learning Intuition - Learning

Lecture 25 Video - Deep Q-Learning Intuition - Learning

Lecture 26 Deep Q-Learning Intuition - Acting

Lecture 27 Video - Deep Q-Learning Intuition - Acting

Lecture 28 Experience Replay

Lecture 29 Experience Replay - Video

Section 5: Using Q-learning Intuition

Lecture 30 Action Selection Policies

Lecture 31 Self Driving Car Build - Step 1

Lecture 32 Self Driving Car Build - Step 2

Lecture 33 Self Driving Car Build - Step 3

Lecture 34 Self Driving Car Build - Step 4

Lecture 35 Self Driving Car Build - Step 5

Lecture 36 Self Driving Car Build - Step 6

Lecture 37 Self Driving Car Build Code

Section 6: Deep Convolutional Q-Learning

Lecture 38 Deep Convolutional Q-Learning Intuition

Lecture 39 Intuitive Deep Learning Part 2: CNNs for Computer Vision

Lecture 40 Intuitive Deep Learning Part 3: RNNs for Natural Language Processing

Lecture 41 Build your first Convolutional Neural Network to recognize images

Lecture 42 Building a Web Application to Deploy Machine Learning Models

Lecture 43 Investigating Recurrence and Eligibility Traces in Deep Q-Networks

Section 7: Automating Doom with Deep Q-Learning: An Implementation in Tensorflow

Lecture 44 Doom -

Lecture 45 Doom Code

Section 8: A3C

Lecture 46 The three A's

Lecture 47 Actor-Critic

Lecture 48 Asynchronous

Lecture 49 Advantage

Lecture 50 LSTM Layer

Section 9: A3c Implementation

Lecture 51 Getting Started With OpenAI Gym: The Basic Building Blocks

Lecture 52 Getting Started With OpenAI Gym: Creating Custom Gym Environments

Lecture 53 Breakout Code

Section 10: A3C Vizualzation

Lecture 54 AI Plays Breakout

Lecture 55 A3C Visualization

Section 11: Artificial neutral network

Lecture 56 What is Deep learning

Lecture 57 Neuron Network

Lecture 58 The math of neural networks in 3 equations

Lecture 59 Activation of neural network

Lecture 60 Gradient Descent

Lecture 61 Backpropagation

Lecture 62 Backpropagation algorithm works

Lecture 63 Backpropagation Training

Lecture 64 Artificial Neural Networks - Part 1

Lecture 65 Artificial Neural Networks - Part 2

Lecture 66 Artificial Neural Networks - Part 3

Section 12: Convolutional Neural Networks for Beginners using Keras & TensorFlow 2

Lecture 67 Introduction to convolutional neural networks

Lecture 68 Convolutional neural network

Lecture 69 Convolution Operation

Lecture 70 Step 1(B): The Rectified Linear Unit (RELU)

Lecture 71 Max Pooling

Lecture 72 Flattening

Lecture 73 Full connection

Lecture 74 Summary

Lecture 75 Softmax & Cross-Entropy

Lecture 76 Convolutional Neural Networks - part 1

Lecture 77 Convolutional Neural Networks - part 2

Lecture 78 Convolutional Neural Networks - part 3

Section 13: "Additional content -Q- Learning

Lecture 79 Q Learning - Part 1

Lecture 80 Q - Learning Part II

Lecture 81 Q - Learning Part III

Lecture 82 Q Learning Hands on

This AI course is made for a variety of individuals who have an interest in learning about Artificial Intelligence and how to build AI systems. Some examples of people who would benefit from this course include: Software developers and engineers looking to expand their skills and knowledge in the field of AI. Data scientists and analysts who want to learn more about AI and how to apply it to their work. IT professionals and managers who want to understand how AI can be used in their organizations. Students who are studying computer science, data science, or related fields and want to learn more about AI. Entrepreneurs and business leaders who want to understand how AI can be used to drive innovation and growth in their companies. This course is designed for those who have basic understanding of programming, mathematics and statistics and are looking to improve their skills and knowledge in the field of AI.

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