Ai Programming In C# - Beginner To Expert

Features 14 hands-on projects and 16 hours of content. Code AI, ML, Neural Networks, and more in C#!

What you'll learn

Build 14 hands-on AI projects using C#

Create an AI that navigates mazes using the TorchSharp library

Code a Neural Network and understand how they are architected

Learn how to create machine learning models

Build a classification AI that can tell if a movie review is positive or negative

Create an image classification AI that can tell the difference between puppies and kittens

Code a regression AI that predicts housing prices

Build a forecasting AI that predicts the future prices of stocks

Create a movie recommendation AI that can suggest movies based on past viewing history

Code a sentiment analysis AI that determines if a movie review is positive or negative

Build an anomaly detection AI that can spot anomalies in network data, like security threats

Develop a text generation AI that generates Shakespearean-style text.

Create a time series analysis AI will be able to predict future website traffic

Code a clustering AI that groups customers into clusters based on their purchase history

Build a reinforcement learning AI that learns how to play tic tac toe

Develop an artificially intelligent game character that performs collection, pathfinding, and obstacle avoidance in the Unity game engine

Gain an understanding of Large Language Models (LLM)

Master using ChatGPT to become more productive and assist you in daily tasks

Learn Linear Algebra and its applications to AI development

Understand AI algorithms like Q-Learning, Policy Gradient, A*, and more

Learn the best practices for optimizing and fine-tuning AI models

Gain an understanding of data manipulation and analysis

Get hands-on experience with scientific computing using NumSharp

Use the Deedle library to perform time series data analysis

Includes a C# Refresher section to brush up on key skills and concepts

Requirements

A computer running Windows, Mac, or Linux

Basic knowledge of C# programming (but a refresher section is included in the course)

Excitement about the topic of AI

All programs used are FREE (Unity requires email to create free account)

Description

The goal of this course is to take a student with little or no experience programming AI and to make them a complete master of the topic using the C# programming language.Whether you want to:Build the skills you need to land your first AI development jobTake your career to the next level by learning AI programming for improving your productivityUse your C# skills to build artificial intelligence programsUnderstand the various algorithms that are used to make AI think and learn like a humanCreate some exciting AI projects hands-on using the C# programming language…this AI Programming in C# tutorial is the course you need to do all of this, and more.Why should you learn AI Programming?AI is rapidly becoming more popular worldwide, across almost all industriesBy learning to build AI programs, you can set yourself apart and further your current careerWith the rapid growth in AI, there are many open roles for AI developersAI developers make a very lucrative salaryAI likely won’t take your job…but someone else who knows how to use AI better than you mightHow is the course structured?The course goes in order building up from basic to intermediate and then to advanced.There are a total of 27 sections in the course and 14 hands-on projects that we will build step-by-step. You’ll not only gain conceptual and theoretical knowledge but also get plenty of practice putting those concepts into action using C# code.Most sections of the course have a quiz at the end, then a video explaining the answers to the quiz questions. That means as you learn the material you will be ensuring that you grasp the key concepts and skills before moving onto the next topic.What topics are taught in this course?AI ConceptsGenerative AI with ChatGPTAI That Solves MazesNeural NetworksMachine Learning with ML NETHands-On: Creating a Classification AIHands-On: Building an Image Classification AIHands-On: Coding a Regression AIHands-On: Creating a Forecasting AIHands-On: Develop a Recommendation AIHands-On: Develop a Sentiment Analysis AIHands-On: Develop a Anomaly Detection AIHands-On: Develop a Text Generation AIHands-On: Develop a Time Series Analysis AIHands-On: Develop a Clustering AIHands-On: Develop a Reinforcement Learning AIData Manipulation and Analysis FundamentalsMath NET Numerics for Data AnalysisNumSharp for Scientific ComputingDeedle for Time Series Data AnalysisAccord NET for Machine Learning and Statistical AnalysisML Agents in Unity (Intelligent AI for Video Games)Best Practices and OptimizationAppendix 1: C# RefresherAppendix 2: Linear AlgebraHow is this course different from the other AI courses on Udemy?While there are plenty of AI development courses on Udemy, this course is the first one that provides a comprehensive understanding of AI programming using C#. Other courses focus on languages like Python, which is a great language that has many advantages. But if you already know C# or work a job that uses C# daily, why not learn to code AI in the language that you currently use?This course focuses on a wide range of topics including the fundamentals of AI development, machine learning, neural networks, Chat GPT, large language models, video game AI characters, classification, regression, forecasting, recommendation, sentiment analysis, anomaly detection, text generation, time series analysis, clustering, reinforcement learning, data analysis, scientific computing, statistical analysis, AI optimization, linear algebra, and C# programming.Are there real-world projects in this course where you can apply the skills you learn hands-on?There are! In fact, you will build 14 hands-on AI projects in this course! You will use your C# skills to develop:An AI that navigates mazes using the TorchSharp libraryA neural networkA classification AI that can tell if a movie review is positive or negativeAn image classification AI that can tell the difference between puppies and kittensA regression AI that predicts housing pricesA forecasting AI that predicts the future prices of stocksA movie recommendation AI that can suggest movies based on past viewing historyA sentiment analysis AI that determines if a movie review is positive, negative, or neutralAn anomaly detection AI that can spot anomalies in network data, like security threatsA text generation AI that generate Shakespearean style text based on user inputA time series analysis AI will be able to predict future website trafficA clustering AI that groups customers into clusters based on their purchase historyA reinforcement learning AI that learns how to play tic tac toeAn artificially intelligent game character that performs collection, pathfinding, and obstacle avoidance in the Unity game engineI will walk you through building each of these projects step by step, so don’t worry about getting overwhelmed or stuck! My students know I break down the big concepts into digestible pieces of information that anyone can understand.Who is your instructor?My name is Rob Gioia and I currently work as a Senior Solutions Architect in New York. C# is my favorite programming language, and most industry jobs that I have held have used C# as the primary programming language.During my time working with C# both in the professional and personal capacity, I’ve used C# to:Teach students how to program when I worked as a teacher’s assistant at the New Jersey Institute of TechnologyBuild virtual reality games to therapy children with convergence insufficiency, an eye disorder, and gamify their treatment.Build a lifestyle Scratch to Win mobile app with over 10 million installs (there was some Java programming involved in this one as well :-) )Develop card trader apps based around high end Intellectual Property like Marvel, Disney, and Star Wars.Create Udemy courses that students have used to build full length video games using Unity and C#.What if you have questions?Any questions you have can be posted to the Q&A forum or messaged to me on Udemy. I check my Udemy account every day to see if students have questions, and do my best to be as responsive and helpful as possible.If you get stuck at any point during this course, send me a message and I will get you unstuck!There is no risk in taking this course!This course comes with a full 30 day money-back guarantee. You either end up with C# skills, go on to develop great programs and potentially make an awesome career for yourself, or you try the course and simply get all your money back if you don’t like it…You literally can’t lose.Are you ready to master AI programming using the C# programming language and build an awesome set of AI development skills that can literally change your life? Then enroll now using the “Add to Cart” button on the right!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Introduction to Artificial Intelligence (AI)

Lecture 3 Why learn to code AI?

Lecture 4 What will we build in this course?

Lecture 5 Download and Install Visual Studio Code with C# Dev Kit (Mac)

Lecture 6 Download and Install Visual Studio Code with C# Dev Kit (Windows)

Section 2: AI Concepts

Lecture 7 Section Introduction

Lecture 8 Types of AI

Lecture 9 Neural Networks

Lecture 10 Machine Learning

Lecture 11 Q-Learning

Lecture 12 Deep Q-Learning

Lecture 13 Deep Convolutional Q-Learning

Lecture 14 Asynchronous Advantage Actor-Critic (A3C)

Lecture 15 Large Language Models (LLMs)

Lecture 16 Generative AI

Lecture 17 Computer Vision

Lecture 18 Answers & Explanations to Quiz Questions

Lecture 19 Section Summary

Section 3: Generative AI with ChatGPT

Lecture 20 Section Introduction

Lecture 21 What is ChatGPT?

Lecture 22 Writing More Effective Generative AI Prompts

Lecture 23 ChatGPT Project 1: Using ChatGPT to Brainstorm Ideas

Lecture 24 ChatGPT Project 2: Using ChatGPT to Write a First Draft

Lecture 25 ChatGPT Project 3: Using ChatGPT to Create a Workout Plan

Lecture 26 ChatGPT Project 4: Using ChatGPT to Summarize a Book

Lecture 27 ChatGPT Project 5: Using ChatGPT to Write Code

Lecture 28 Answers & Explanations to Quiz Questions

Lecture 29 Section Summary

Section 4: AI That Solves Mazes

Lecture 30 Section Introduction

Lecture 31 What is TorchSharp?

Lecture 32 Creating the Maze (The Environment)

Lecture 33 Actions & Rewards

Lecture 34 Training the Model

Lecture 35 Final Result

Lecture 36 Answers & Explanations to Quiz Questions

Lecture 37 Section Summary

Section 5: Neural Networks

Lecture 38 Section Introduction

Lecture 39 What is a Neural Network?

Lecture 40 Neural Network Architecture

Lecture 41 Sneak Peek at Finished Project

Lecture 42 Coding a Neural Network: The Neural Network Class

Lecture 43 Coding a Neural Network: The Activate Function

Lecture 44 Coding a Neural Network: The Train Function

Lecture 45 Coding a Neural Network: The Dot Product Function

Lecture 46 Coding a Neural Network: The Perform Operation Function

Lecture 47 Coding a Neural Network: The Transpose Function

Lecture 48 Coding a Neural Network: Testing our Code

Lecture 49 Real World Applications of Neural Networks

Lecture 50 Answers & Explanations to Quiz Questions

Lecture 51 Section Summary

Section 6: Machine Learning with ML.NET

Lecture 52 Section Introduction

Lecture 53 What is ML.NET?

Lecture 54 Setting up ML.NET

Lecture 55 Introduction to Machine Learning with ML.NET

Lecture 56 Data Preparation and Loading in ML.NET

Lecture 57 Feature Engineering in ML.NET

Lecture 58 Model Selection and Evaluation in ML.NET

Lecture 59 Training and Tuning Models in ML.NET

Lecture 60 Model Deployment and Integration with ML.NET

Lecture 61 Answers & Explanations to Quiz Questions

Lecture 62 Section Summary

Section 7: Hands-On: Creating a Classification AI

Lecture 63 Section Introduction

Lecture 64 Classification

Lecture 65 Sneak Peek at Finished Project

Lecture 66 Creating the Project

Lecture 67 Setting up the Data

Lecture 68 Loading the Data

Lecture 69 Training the Model

Lecture 70 Evaluating and Testing the Model

Lecture 71 Answers & Explanations to Quiz Questions

Lecture 72 Section Summary

Section 8: Hands-On: Building an Image Classification AI

Lecture 73 Section Introduction

Lecture 74 Image Classification

Lecture 75 Sneak Peek at Finished Project

Lecture 76 Creating the Project

Lecture 77 Setting up the Data

Lecture 78 Loading the Data

Lecture 79 Training the Model

Lecture 80 Evaluating and Testing the Model

Lecture 81 Answers & Explanations to Quiz Questions

Lecture 82 Section Summary

Section 9: Hands-On: Coding a Regression AI

Lecture 83 Section Introduction

Lecture 84 Regression

Lecture 85 Sneak Peek at Finished Project

Lecture 86 Creating the Project

Lecture 87 Preprocessing and Loading the Data

Lecture 88 Training the Model

Lecture 89 Evaluating the Model

Lecture 90 Testing the Model

Lecture 91 Answers & Explanations to Quiz Questions

Lecture 92 Section Summary

Section 10: Hands-On: Creating a Forecasting AI

Lecture 93 Section Introduction

Lecture 94 Forecasting

Lecture 95 Sneak Peek at Finished Project

Lecture 96 Creating the Project

Lecture 97 Setting up the Data

Lecture 98 Loading the Data

Lecture 99 Training the Model

Lecture 100 Evaluating and Testing the Model

Lecture 101 Answers & Explanations to Quiz Questions

Lecture 102 Section Summary

Section 11: Hands-On: Develop a Recommendation AI

Lecture 103 Section Introduction

Lecture 104 Recommendation

Lecture 105 Sneak Peek at Finished Project

Lecture 106 Creating the Project

Lecture 107 Preprocessing the Data

Lecture 108 Loading the Data

Lecture 109 Training the Model

Lecture 110 Evaluating and Testing the Model

Lecture 111 Answers & Explanations to Quiz Questions

Lecture 112 Section Summary

Section 12: Hands-On: Develop a Sentiment Analysis AI

Lecture 113 Section Introduction

Lecture 114 Sentiment Analysis

Lecture 115 Sneak Peek at Finished Project

Lecture 116 Creating the Project

Lecture 117 Preprocessing the Data

Lecture 118 Loading the Data

Lecture 119 Training the Model

Lecture 120 Evaluating and Testing the Model

Lecture 121 Answers & Explanations to Quiz Questions

Lecture 122 Section Summary

Section 13: Hands-On: Develop a Anomaly Detection AI

Lecture 123 Section Introduction

Lecture 124 Anomaly Detection

Lecture 125 Sneak Peek at Finished Project

Lecture 126 Creating the Project

Lecture 127 Preprocessing the Data

Lecture 128 Loading the Data

Lecture 129 Training the Model

Lecture 130 Evaluating and Testing the Model

Lecture 131 Answers & Explanations to Quiz Questions

Lecture 132 Section Summary

Section 14: Hands-On: Develop a Text Generation AI

Lecture 133 Section Introduction

Lecture 134 Text Generation

Lecture 135 Sneak Peek at Finished Project

Lecture 136 Creating the Project

Lecture 137 Preprocessing the Data

Lecture 138 Loading the Data

Lecture 139 Training the Model

Lecture 140 Evaluating and Testing the Model

Lecture 141 Answers & Explanations to Quiz Questions

Lecture 142 Section Summary

Section 15: Hands-On: Develop a Time Series Analysis AI

Lecture 143 Section Introduction

Lecture 144 Time Series Analysis

Lecture 145 Sneak Peek at Finished Project

Lecture 146 Creating the Project

Lecture 147 Loading the Data

Lecture 148 Preprocessing the Data

Lecture 149 Training the Model

Lecture 150 Evaluating and Testing the Model

Lecture 151 Answers & Explanations to Quiz Questions

Lecture 152 Section Summary

Section 16: Hands-On: Develop a Clustering AI

Lecture 153 Section Introduction

Lecture 154 Clustering

Lecture 155 Sneak Peek at Finished Project

Lecture 156 Creating the Project

Lecture 157 Loading the Data

Lecture 158 Training the Model

Lecture 159 Evaluating and Testing the Model

Lecture 160 Answers & Explanations to Quiz Questions

Lecture 161 Section Summary

Section 17: Hands-On: Develop a Reinforcement Learning AI

Lecture 162 Section Introduction

Lecture 163 Reinforcement Learning

Lecture 164 Sneak Peek at Finished Project

Lecture 165 Creating the Project

Lecture 166 Setting Up the Environment

Lecture 167 Defining the Q-Learning Algorithm

Lecture 168 Implementing the Training Process

Lecture 169 Testing the trained agent

Lecture 170 Answers & Explanations to Quiz Questions

Lecture 171 Section Summary

Section 18: Data Manipulation and Analysis Fundamentals

Lecture 172 Section Introduction

Lecture 173 Reading and Writing Data to Files

Lecture 174 Data Preprocessing Techniques

Lecture 175 Exploratory Data Analysis (EDA)

Lecture 176 Feature Engineering

Lecture 177 Data Integration and Aggregation

Lecture 178 Answers & Explanations to Quiz Questions

Lecture 179 Section Summary

Section 19: Math.NET Numerics for Data Analysis

Lecture 180 Section Introduction

Lecture 181 Introduction to Math.NET Numerics

Lecture 182 Statistical Analysis with Math.NET Numerics

Lecture 183 Linear Algebra Operations with Math.NET Numerics

Lecture 184 Numerical Integration and Differentiation

Lecture 185 Solving Linear Equations and Systems

Lecture 186 Curve Fitting and Interpolation Techniques

Lecture 187 Optimization Methods in Math.NET Numerics

Lecture 188 Sparse Matrices and Compressed Storage Formats

Lecture 189 Eigenvalue Decomposition and Singular Value Decomposition

Lecture 190 Multivariate Data Analysis and Dimensionality Reduction

Lecture 191 Answers & Explanations to Quiz Questions

Lecture 192 Section Summary

Section 20: NumSharp for Scientific Computing

Lecture 193 Section Introduction

Lecture 194 Introduction to NumSharp

Lecture 195 Working with Arrays in NumSharp

Lecture 196 Basic Mathematical Operations with NumSharp

Lecture 197 Indexing and Slicing Arrays in NumSharp

Lecture 198 Linear Algebra Operations with NumSharp

Lecture 199 Statistical Analysis with NumSharp

Lecture 200 Array Broadcasting and Universal Functions (ufuncs)

Lecture 201 Advanced Array Manipulation Techniques

Lecture 202 Answers & Explanations to Quiz Questions

Lecture 203 Section Summary

Section 21: Deedle for Time Series Data Analysis

Lecture 204 Section Introduction

Lecture 205 Introduction to Deedle

Lecture 206 Loading and Manipulating Time Series Data with Deedle

Lecture 207 Basic Time Series Operations with Deedle

Lecture 208 Resampling and Aggregating Time Series Data

Lecture 209 Time Series Indexing and Slicing in Deedle

Lecture 210 Missing Data Handling in Time Series Analysis with Deedle

Lecture 211 Rolling Windows and Moving Averages

Lecture 212 Time Series Visualization with Deedle

Lecture 213 Seasonality and Trend Analysis in Time Series Data

Lecture 214 Stationarity Testing with Deedle

Lecture 215 Integration with ML.NET for Time Series Prediction

Lecture 216 Answers & Explanations to Quiz Questions

Lecture 217 Section Summary

Section 22: Accord.NET for Machine Learning and Statistical Analysis

Lecture 218 Section Introduction

Lecture 219 Introduction to Accord.NET

Lecture 220 Loading and Preprocessing Data with Accord.NET

Lecture 221 Exploratory Data Analysis (EDA) with Accord.NET

Lecture 222 Basic Statistical Analysis with Accord.NET

Lecture 223 Classification Algorithms in Accord.NET

Lecture 224 Regression Techniques with Accord.NET

Lecture 225 Clustering Methods in Accord.NET

Lecture 226 Dimensionality Reduction Techniques

Lecture 227 Ensemble Learning and Random Forests in Accord.NET

Lecture 228 Support Vector Machines (SVM) with Accord.NET

Lecture 229 Neural Networks and Deep Learning with Accord.NET

Lecture 230 Answers & Explanations to Quiz Questions

Lecture 231 Section Summary

Section 23: ML Agents in Unity (Intelligent AI for Video Games)

Lecture 232 Section Introduction

Lecture 233 Download and Install Unity

Lecture 234 What are ML Agents?

Lecture 235 Importing the ML Agents Unity Package

Lecture 236 ML Agents in Unity: Building a Mario Agent

Lecture 237 Setting Up Mario's Environment: Level Design & Initialization

Lecture 238 Observations & Actions: Input & Output Spaces

Lecture 239 Coin Collection: Rewards & Greedy Algorithm

Lecture 240 Advanced Coin Collection: Q-Learning & Policy Gradient

Lecture 241 Obstacle Avoidance and Pathfinding Strategies for Mario

Lecture 242 Dynamic Difficulty Adjustment: Adaptive Algorithms

Lecture 243 UI Design: Algorithm Selection & Configuration

Lecture 244 Evaluating Mario's Performance: Metrics & Visualization

Lecture 245 Hyperparameter Fine-Tuning: Optimization Techniques

Lecture 246 Handling Sparse Rewards: Reward Shaping & Function Approximation

Lecture 247 Exploration-Exploitation Strategies: Epsilon-Greedy & Softmax

Lecture 248 Debugging & Troubleshooting ML Agents

Lecture 249 Model Interpretability: Understanding Agent Behavior

Lecture 250 Continuous & Transfer Learning Strategies

Lecture 251 Deploying the Trained Mario Agent in Unity

Lecture 252 Answers & Explanations to Quiz Questions

Lecture 253 Section Summary

Section 24: Best Practices and Optimization

Lecture 254 Section Introduction

Lecture 255 Model Performance Evaluation: Metrics & Techniques

Lecture 256 Hyperparameter Tuning Strategies for Better Performance

Lecture 257 Grid vs. Random Search: Hyperparameter Optimization

Lecture 258 Bayesian Optimization for Hyperparameter Tuning

Lecture 259 Automated Machine Learning (AutoML) for Model Selection

Lecture 260 Model Interpretability Techniques

Lecture 261 Pruning Models for Efficiency

Lecture 262 Quantization & Compression for Model Optimization

Lecture 263 Distributed Training: Scaling Model Training

Lecture 264 Answers & Explanations to Quiz Questions

Lecture 265 Section Summary

Section 25: Appendix 1: C# Refresher

Lecture 266 Section Introduction

Lecture 267 Introduction to C# Programming Language

Lecture 268 Understanding Variables and Data Types in C#

Lecture 269 Control Flow: If Statements and Switch Statements

Lecture 270 Looping Constructs: For, While, and Do-While Loops

Lecture 271 Introduction to Object-Oriented Programming (OOP) in C#

Lecture 272 Encapsulation: Access Modifiers and Data Hiding

Lecture 273 Working with Collections: Arrays, Lists, and Dictionaries

Lecture 274 Introduction to Generics in C#

Lecture 275 Error Handling: Exception Handling and Try-Catch Blocks

Lecture 276 Asynchronous Programming with async/await

Lecture 277 LINQ Fundamentals: Querying Collections

Lecture 278 LINQ Operators: Filtering, Sorting, and Grouping Data

Lecture 279 Delegates and Events: Understanding Event-Driven Programming

Lecture 280 Reflection and Attributes in C#: Accessing Type Information

Lecture 281 File I/O Operations: Reading and Writing Files

Lecture 282 Working with Streams: Stream-Based I/O Operations

Lecture 283 Best Practices and Coding Standards for C# Programming

Lecture 284 Answers & Explanations to Quiz Questions

Lecture 285 Section Summary

Section 26: Appendix 2: Linear Algebra

Lecture 286 Section Introduction

Lecture 287 Introduction to Linear Algebra

Lecture 288 Matrices and Operations

Lecture 289 Solving Linear Systems

Lecture 290 Vector Spaces and Subspaces

Lecture 291 Linear Independence and Span

Lecture 292 Orthogonality and Orthogonal Matrices

Lecture 293 Matrix Transformations and Geometry

Lecture 294 Inner Products and Norms

Lecture 295 Eigenvalues and Eigenvectors

Lecture 296 Matrix Rank and Null Space

Lecture 297 Singular Value Decomposition (SVD)

Lecture 298 Least Squares Estimation

Lecture 299 Linear Transformations and Kernel

Lecture 300 Principal Component Analysis (PCA) and Dimensionality Reduction

Lecture 301 Applications of Linear Algebra in Machine Learning

Lecture 302 Answers & Explanations to Quiz Questions

Lecture 303 Section Summary

Section 27: Course Summary & Wrap-Up

Lecture 304 Course Summary & Wrap-Up

Lecture 305 Bonus Lecture

Anyone that wants to learn AI programming using C#,C# developers that want to use AI to take their career to the next level,Beginners that haven’t worked with AI and want to obtain mastery of it