Unleashing the Power of AI

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1 - Why Everyone should learn AI

1 - Welcome to Unleashing the Power of AI.html

2 - Introduction.mp4

15:12

3 - How-to-Install-and-Download-Anaconda-Distribution-Python-on-MAC-Tutorial-2023.mp4

02:17

3 - Installing Anaconda on Windows or Mac computers.html

3 - Install-Anaconda-Distribution-on-Windows-2022.mp4

01:41

2 - Fundamentals of Reinforcement Learning

4 - Reinforcement Learning RL.html

5 - Reinforcement Learning RL Video.mp4

10:25

6 - The Plan video.mp4

02:12

7 - Understanding The Bellman Equation.html

8 - Understanding The Bellman Equation Video.mp4

18:34

9 - Breaking down the Markov Decision Process.html

10 - Markov Decision Process Video.mp4

16:26

11 - Understanding the difference between Policy vs Plan in the context of RL.html

12 - Policy vs Plan.mp4

12:55

13 - Reinforcement Learning Living Penalty and Reward.html

14 - Living Penalty and Reward Video.mp4

09:47

15 - An introduction to QLearning reinforcement learning.html

16 - QLearning reinforcement learning Video.mp4

14:45

17 - Simple Reinforcement Learning Temporal Difference Learning.html

18 - Temporal Difference Learning Video.mp4

19:26

3 - Visualization

19 - Plan.mp4

04:13

20 - QLearning Visualization.html

21 - QLearning Visualization Video.mp4

08:02

22 - Using Reinforcement Learning to solve Gridworld.html

23 - Video Using Reinforcement Learning to solve Gridworld.mp4

13:31

4 - Deep Qlearning Intuition

24 - Deep QLearning Intuition Learning.html

25 - Video Deep QLearning Intuition Learning.mp4

15:15

26 - Deep QLearning Intuition Acting.html

27 - Video Deep QLearning Intuition Acting.mp4

06:06

28 - Experience Replay.html

29 - Experience Replay Video.mp4

15:45

5 - Using Qlearning Intuition

30 - Action Selection Policies.html

31 - Self Driving Car Build Step 1.html

32 - Self Driving Car Build Step 2.html

33 - Self Driving Car Build Step 3.html

34 - Self Driving Car Build Step 4.html

35 - Self Driving Car Build Step 5.html

36 - Self Driving Car Build Step 6.html

37 - Self Driving Car Build Code.html

6 - Deep Convolutional QLearning

38 - Deep Convolutional QLearning Intuition.html

39 - Intuitive Deep Learning Part 2 CNNs for Computer Vision.html

40 - Intuitive Deep Learning Part 3 RNNs for Natural Language Processing.html

41 - Build your first Convolutional Neural Network to recognize images.html

42 - Building a Web Application to Deploy Machine Learning Models.html

43 - Investigating Recurrence and Eligibility Traces in Deep QNetworks.html

7 - Automating Doom with Deep QLearning An Implementation in Tensorflow

44 - Doom.html

45 - Doom Code.html

8 - A3C

46 - The three As.html

47 - ActorCritic.html

48 - Asynchronous.html

49 - Advantage.html

50 - LSTM Layer.html

9 - A3c Implementation

51 - Getting Started With OpenAI Gym The Basic Building Blocks.html

52 - Getting Started With OpenAI Gym Creating Custom Gym Environments.html

53 - Breakout Code.html

10 - A3C Vizualzation

54 - AI Plays Breakout.mp4

36:02

55 - A3C Visualization.mp4

10:51

11 - Artificial neutral network

56 - What is Deep learning.html

57 - Neuron Network.html

58 - The math of neural networks in 3 equations.html

59 - Activation of neural network.html

60 - Gradient Descent.html

61 - Backpropagation.html

62 - Backpropagation algorithm works.html

63 - Backpropagation Training.html

64 - Artificial Neural Networks Part 1.mp4

31:41

65 - Artificial Neural Networks Part 2.mp4

34:15

66 - Artificial Neural Networks Part 3.mp4

24:19

12 - Convolutional Neural Networks for Beginners using Keras TensorFlow 2

67 - Introduction to convolutional neural networks.html

68 - Convolutional neural network.html

69 - Convolution Operation.html

70 - Step 1B The Rectified Linear Unit RELU.html

71 - Max Pooling.html

72 - Flattening.html

73 - Full connection.html

74 - Summary.html

75 - Softmax CrossEntropy.html

76 - Convolutional Neural Networks part 1.mp4

35:59

77 - Convolutional Neural Networks part 2.mp4

42:11

78 - Convolutional Neural Networks part 3.mp4

22:39

13 - Additional content Q Learning

79 - Q Learning Part 1.html

80 - Q Learning Part II.html

81 - Q Learning Part III.html

82 - Q Learning Hands on.html

Description

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.

Who is this for?

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.

More details

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 â€“ 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 â€“ Plus, youâ€™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 â€“ 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 youâ€™re doing, but why youâ€™re doing it. Thatâ€™s why we donâ€™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 â€“ Youâ€™ll achieve your goal in not only 1 game but in 3. Each module is comprised of varying structures and difficulties, meaning youâ€™ll be skilled enough to build AI adaptable to any environment in real life, rather than just passing a glorified memory â€œtest and forgetâ€ like most other courses. Practice truly does make perfect.
In-course support â€“ Weâ€™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. Thatâ€™s why weâ€™ve put together a team of professional Data Scientists to support you in your journey, meaning youâ€™ll get a response from us within 48 hours maximum.
Who this course is for:
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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Instructor's Courses

Get More Educated - GME - is a highly accomplished professionals with years of experience in the field as a AI scientist and researcher. GME teaches AI and machine learning. GME is highly respected in the community and is known for their contributions. GME has a passion for teaching and mentoring students and is dedicated to advancing the state of the art in AI.

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