## Course content

- Introduction to Backpropagation Algorithm
- Backpropagation Algorithmn- Matlab Application

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# Tag: Artificial Intelligence

## Backpropagation Learning Method in Matlab

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## Essential Big Data and AI Skills

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## Game Devs Unleash Artificial Intelligence: Flocking Agents

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## If You Can Cook You Can Code Vol 5: Artificial Intelligence

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## 10 Days of No Code Artificial Intelligence Bootcamp

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## Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)

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## The Data Science Course: Complete Data Science Bootcamp 2024

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## Deep Learning A-Z 2023: Neural Networks, AI & ChatGPT Prize

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## Learn Explainable AI (XAI)

## About this course

## Skills you’ll gain

## Intro to Generative AI

## About this course

## Skills you’ll gain

- Introduction to Backpropagation Algorithm
- Backpropagation Algorithmn- Matlab Application

- Big Data Big Picture
- Big Data Overview
- DB-SQL
- Big Data Core
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Presentation Tips For A1 and Big Data Environment
- Final Revision

- Introduction
- Insights
- Implement Basic Flocking in Pseudocode and Unity
- Refinements for Advanced Flocking Behavior In Pseudocode and Unity
- Conclusion
- Valuable Resources

- Introduction
- Measurement

- Welcome to the Course!
- Day 1: Develop an A1 model to classify fashion elements using Google Teachable
- Day 2: Deep Dive into A1 technicalities
- Day 3: Detect and classify face masks using Google Teachable Machines
- Day 4: Visualize Artificial Intelligence Models Using Tensorspace.JS and GTP
- Day 5: Develop an ML Model to predict used car prices using DataRobot
- Day 6: Develop an A1 model to predict employee’s attrition using DataRobot
- Day 7: Develop an A1 model to detect Diabetic Retinopathy Using DataRobot
- Day 8: Deploy an A1 model to predict customer sentiment from Text
- Day 9: Predict credit card default using AWS SageMaker Autopilot
- Day 10: Google Vertex A1-Powered Regression Model Prediction
- Congratulations!! Don’t forget your Prize ðŸ™‚

- Introduction
- Fundamentals of Reinforcement Learning
- Deep Learning Crash Course
- Human Level Control Through Deep Reinforcement Learning: From Paper to Code
- Deep Reinforcement Learning with Double Q Learning
- Dueling Network Architectures for Deep Reinforcement Learning
- Improving On Our Solutions
- Conclusion
- Bonus Lecture
- Tensorflow 2 Implementations
- Appendix

- Part 1: Introduction
- The Field of Data Science – The Various Data Science Disciplines
- The Field of Data Science – Connecting the Data Science Disciplines
- The Field of Data Science – The Benefits of Each Discipline
- The Field of Data Science – Popular Data Science Techniques
- The Field of Data Science – Popular Data Science Tools
- The Field of Data Science – Careers in Data Science
- The Field of Data Science – Debunking Common Misconceptions
- Part 2: Probability
- Probability – Combinatorics
- Probability – Bayesian Inference
- Probability – Distributions
- Probability – Probability in Other Fields
- Part 3: Statistics
- Statistics – Descriptive Statistics
- Statistics – Practical Example: Descriptive Statistics
- Statistics – Inferential Statistics Fundamentals
- Statistics – Inferential Statistics: Confidence Intervals
- Statistics – Practical Example: Inferential Statistics
- Statistics – Hypothesis Testing
- Statistics – Practical Example: Hypothesis Testing
- Part 4: Introduction to Python
- Python – Variables and Data Types
- Python – Basic Python Syntax
- Python – Other Python Operators
- Python – Conditional Statements
- Python – Python Functions
- Python – Sequences
- Python – Iterations
- Python – Advanced Python Tools
- Part 5: Advanced Statistical Methods in Python
- Advanced Statistical Methods – Linear Regression with StatsModels
- Advanced Statistical Methods – Multiple Linear Regression with StatsModels
- Advanced Statistical Methods – Linear Regression with sklearn
- Advanced Statistical Methods – Practical Example: Linear Regression
- Advanced Statistical Methods – Logistic Regression
- Advanced Statistical Methods – Cluster Analysis
- Advanced Statistical Methods – K-Means Clustering
- Advanced Statistical Methods – Other Types of Clustering
- Part 6: Mathematics
- Part 7: Deep Learning
- Deep Learning – Introduction to Neural Networks
- Deep Learning – How to Build a Neural Network from Scratch with NumPy
- Deep Learning – TensorFlow 2.0: Introduction
- Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
- Deep Learning – Overfitting
- Deep Learning – Initialization
- Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
- Deep Learning – Preprocessing
- Deep Learning – Classifying on the MNIST Dataset
- Deep Learning – Business Case Example
- Deep Learning – Conclusion
- Appendix: Deep Learning – TensorFlow 1: Introduction
- Appendix: Deep Learning – TensorFlow 1: Classifying on the MN 1ST Dataset
- Appendix: Deep Learning – TensorFlow 1: Business Case
- Software Integration
- Case Study – What’s Next in the Course?
- Case Study – Preprocessing the ‘Absenteeism_data’
- Case Study – Applying Machine Learning to Create the ‘absenteeism module’
- Case Study – Loading the ‘absenteeism _ module’
- Case Study – Analyzing the Predicted Outputs in Tableau
- Appendix – Additional Python Tools
- Appendix – pandas Fundamentals
- Appendix – Working with Text Files in Python
- Bonus Lecture

- Welcome to the course!
- Part 1 – Artificial Neural Networks
- ANN Intuition
- Building an ANN
- Part 2 – Convolutional Neural Networks
- CNN Intuition
- Building a CNN
- Part 3 – Recurrent Neural Networks
- RNN Intuition
- Building a RNN
- Evaluating and Improving the RNN
- Part 4 – Self Organizing Maps
- SOMs Intuition
- Building a SOM
- Mega Case Study
- Part 5 – Boltzmann Machines
- Boltzmann Machine Intuition
- Building a Boltzmann Machine
- Part 6 – AutoEncoders
- AutoEncoders Intuition
- Building an AutoEncoder
- Annex – Get the Machine Learning Basics
- Regression & Classification Intuition
- Data Preprocessing
- Data Preprocessing in Python
- Logistic Regression
- Congratulations!! Don’t forget your Prize

The deep learning models that power AI systems are often black boxes. Explainable AI tries to understand how these models make decisions, so that we can use them responsibly. In this course, you will learn the basic techniques of Explainable AI, including Generative Adversarial Networks (GANs). You will also learn about legal rights to explanation, and the role of explanation in regulating AI.

- Understand how neural networks function
- Evaluate common Explainable AI methods
- Understand legal rights to explanation

Artificial Intelligence is a fast-evolving field of technology that lets computers simulate human functions, such as learning and problem-solving. A subset of AI thatâ€™s been gaining traction recently is generative AI, which specializes in creating new content, be it text, images, audio, or videos. Take this course to learn about the different types of generative AI using interactive applets!

- Understand what generative AI is
- See the different types of generative AI
- Study the ethics of using generative AI