Artificial Intelligence Masterclass

Learn AI from scratch with hands-on projects: Machine Learning, Deep Learning, Reinforcement Learning, and more using Py

What you'll learn

Understand the foundational math behind AI, including linear algebra, probability, and optimization.

Build and train machine learning models from scratch using Python and PyTorch.

Develop deep learning systems such as CNNs, RNNs, Transformers, and Autoencoders with real code.

Apply reinforcement learning algorithms including SARSA, Q-learning, PPO, and A3C in interactive environments.

Use techniques like PCA, regularization, and cross-validation to improve model performance.

Explore advanced topics such as Graph Neural Networks, Bayesian methods, and Meta-Learning with working examples.

Requirements

No prior background in AI is required.

Basic programming knowledge helps, but there’s an optional Python section at the beginning for anyone who needs it.

You’ll need a computer that can run Python and a stable internet connection to follow along with the tools and notebooks.

Description

This course is built for learners who want a serious, structured path into Artificial Intelligence. Whether you’re coming from engineering, programming, or analytics — or even starting from scratch — you’ll find that everything here is laid out in a practical, step-by-step format.We start with foundational math and basic Python — so you don’t have to worry if you haven’t used linear algebra or probability in a while. You’ll get clear walkthroughs of the math behind algorithms, with Python implementations that you can run, change, and learn from directly.From there, we cover all the major building blocks of modern AI:Supervised and unsupervised learningModel accuracy and regularizationDeep learning with CNNs, RNNs, and TransformersReinforcement learning methods like Q-Learning, PPO, A3C, TRPOBayesian models, optimization methods, and neural architecture searchYou’ll work with real code, solve tasks visually, and understand why each method works — not just how to use it. We also use a mix of Python, PyTorch, Julia, and Colab notebooks where appropriate.If you’re looking for an over-the-top promo, you won’t find it here. This course is detailed, technical, and designed to make sure you walk away actually understanding AI.All content is developed and presented by Advancedor Academy.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Python Programming (Optional)

Lecture 2 What is Python?

Lecture 3 Anaconda & Jupyter & Visual Studio Code

Lecture 4 Google Colab

Lecture 5 Environment Setup

Lecture 6 Python Syntax & Basic Operations

Lecture 7 Data Structures: Lists, Tuples, Sets

Lecture 8 Control Structures & Looping

Lecture 9 Functions & Basic Functional Programming

Lecture 10 Intermediate Functions

Lecture 11 Dictionaries and Advanced Data Structures

Lecture 12 Modules, Packages & Importing Libraries

Lecture 13 File Handling

Lecture 14 Exception Handling & Robust Code

Lecture 15 OOP

Lecture 16 Data Visualization Basics

Lecture 17 Advanced List Operations & Comprehensions

Section 3: Mathematical Foundations for AI

Lecture 18 Linear Algebra Review: Vectors and Matrices

Lecture 19 Eigenvalues and Eigenvectors

Lecture 20 Probability Distributions

Lecture 21 Probability Theory Basics

Lecture 22 Bayesian Probability

Lecture 23 Statistics for AI: Descriptive and Inferential Statistics

Lecture 24 Inferential Statistics

Lecture 25 Gradient Descent

Lecture 26 Normal Distribution

Lecture 27 Derivatives and Differentiation Rules

Lecture 28 AdaGrad

Lecture 29 AdaGrad with Python

Lecture 30 RMSProp

Section 4: Data Preprocessing (Optional)

Lecture 31 Data Quality

Lecture 32 Data Cleaning Techniques

Lecture 33 Handling Missing Value

Lecture 34 Dealing With Outliers

Lecture 35 Feature Scaling and Normalization

Lecture 36 Standardization

Lecture 37 Encoding Categorical Variables

Lecture 38 Feature Engineering

Lecture 39 Dimensionality Reduction

Section 5: Exploratory Data Analysis (EDA)

Lecture 40 Descriptive Statistics

Lecture 41 Multivariate Analysis

Section 6: Introduction to Machine Learning

Lecture 42 Introduction to Machine Learning

Lecture 43 History and Evolution of Machine Learning

Lecture 44 Applications of Machine Learning

Lecture 45 Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

Lecture 46 Machine Learning Pipeline

Lecture 47 Overview of Python Libraries for Machine Learning

Section 7: Introduction Concepts and Notation for Machine Learning

Lecture 48 ML Introduction Concepts - 1

Lecture 49 ML Introduction Concepts - 2

Lecture 50 ML Introduction Concepts - 3

Lecture 51 ML Introduction Concepts - 4

Lecture 52 Notation

Section 8: Learning

Lecture 53 What is Learning?

Lecture 54 Why Do We Predict f?

Lecture 55 Curse of Dimensionality

Lecture 56 How Do We Predict f?

Lecture 57 Prediction Accuracy or Model Simplicity?

Lecture 58 Regression vs Classification

Section 9: Measuring Model Accuracy

Lecture 59 Measuring Prediction Quality

Lecture 60 Bias-Variance Trade-Off

Lecture 61 Classification Setup

Lecture 62 KNN Example

Section 10: Simple Linear Regression

Lecture 63 Mathematical Basis of Regression

Lecture 64 Regression - Visual Explanation

Section 11: Multiple Linear Regression

Lecture 65 Multiple Linear Regression

Lecture 66 OLS Table

Lecture 67 Hypothesis Testing

Section 12: KNN

Lecture 68 Part 1

Section 13: Naive Bayes

Lecture 69 Introduction

Section 14: Logistic Regression

Lecture 70 Introduction

Section 15: Model Performance Metrics

Lecture 71 Confusion Matrix

Lecture 72 Accuracy

Lecture 73 Precision

Lecture 74 Recall

Lecture 75 F1 Score

Lecture 76 ROC-AUC Curve

Lecture 77 Log-Loss

Section 16: Model Selection

Lecture 78 Cross Validation

Lecture 79 K-Fold Cross Validation - Regression

Lecture 80 K-Fold Cross Validation -Classification

Lecture 81 Grid Search & Random Search

Section 17: Regularization

Lecture 82 Mathematical Basis of Regularization

Section 18: Support Vector Machines (SVM)

Lecture 83 The Mathematical Foundation of SVM - 1

Lecture 84 The Mathematical Foundation of SVM - 2

Lecture 85 Kernels

Lecture 86 SVM Cost Function

Section 19: Decision Trees

Lecture 87 Fundamentals

Lecture 88 Gini Index & Overfitting

Section 20: Random Forest

Lecture 89 Introduction to RF

Section 21: Boosting - Machine Learning

Lecture 90 Boosting - Part 1

Lecture 91 Boosting - Part 2

Section 22: Unsupervised Learning

Lecture 92 Introduction to Unsupervised Learning

Lecture 93 K-Means Clustering - Part 1

Lecture 94 K-Means Clustering - Part 2

Lecture 95 Dimensionality Reduction: PCA - 1

Lecture 96 Dimensionality Reduction: PCA - Iris

Lecture 97 PCA - MNIST

Section 23: Neural Networks and Deep Learning

Lecture 98 Introduction to Neural Networks

Lecture 99 Deep Learning Architectures: CNN

Section 24: Deep Feedforward Neural Network

Lecture 100 XOR with a Deep Feedforward Neural Network

Lecture 101 Deep Feedforward Neural Network (DFFN) - MNIST

Section 25: Multi-Layer Perceptron

Lecture 102 MLP Mixer Structure with Pytorch

Section 26: Convolutional Neural Networks (CNNs)

Lecture 103 CNN Architectures with PyTorch

Lecture 104 CNN Architectures with Julia - Flux

Lecture 105 CNN Architecture with MATLAB

Lecture 106 1993 Yann LeCun

Section 27: Residual Networks (ResNets)

Lecture 107 Implementing ResNets with Python - 1

Lecture 108 Implementing ResNets with Python - 2

Section 28: Recurrent Neural Networks (RNNs)

Lecture 109 Multi Layer RNN

Section 29: Gated Recurrent Units (GRUs)

Lecture 110 Implementing GRU with Python

Section 30: Attention Mechanisms and Transformers

Lecture 111 Transformer Architecture from Scratch

Lecture 112 Training and Using Transformers

Section 31: TCN

Lecture 113 Building a TCN Model for Air Quality Forecasting - 1

Lecture 114 Building a TCN Model for Air Quality Forecasting - 2

Section 32: Time-Delayed Neural Networks

Lecture 115 TDNN From Scratch

Section 33: Sequence-to-Sequence Models

Lecture 116 Multi-step Time Series Forecasting with Seq2Seq LSTM

Section 34: Autoencoders

Lecture 117 Building Sparse Autoencoders with L1 and KL Regularization

Section 35: Graph Neural Networks (GNNs)

Lecture 118 Implementing GNNs with Python

Section 36: Bayesian Neural Network

Lecture 119 Bayesian Neural Network in PyTorch – Regression with Uncertainty Estimation

Section 37: HyperNetworks and Dynamic Neural Networks

Lecture 120 Implementing HyperNetworks

Lecture 121 Implementing a Fully Dynamic Neural Network in PyTorch

Section 38: Federated Learning

Lecture 122 FedProx

Section 39: Meta Learning

Lecture 123 Model-Agnostic Meta Learning

Lecture 124 Few-Shot Classification with Prototypical Networks

Lecture 125 Few-Shot Classification with Prototypical Networks | Outputs

Section 40: Reinforcement Learning Basics

Lecture 126 What's Reinforcement Learning?

Lecture 127 Components of Reinforcement Learning

Lecture 128 Markov Decision Processes

Lecture 129 Markov Decision Processes - Case

Lecture 130 Markov Decision Processes - Python

Lecture 131 Markov Decision Processes Code Output

Lecture 132 Dynamic Programming Principles

Lecture 133 Dynamic Programming - Case

Lecture 134 Dynamic Programming - Mathematical Model

Lecture 135 Dynamic Programming - Python Code

Lecture 136 Dynamic Programming - Output

Lecture 137 Policy Evaluation

Lecture 138 Iterative Policy Evaluation Algorithm with Python

Lecture 139 Monte Carlo Methods in RL

Section 41: Temporal Difference Learning

Lecture 140 What is SARSA?

Lecture 141 SARSA - Taxi Implementation

Lecture 142 SARSA - Taxi & Visual

Lecture 143 Q-Learning Intro

Lecture 144 Frozen Lake

Lecture 145 Frozen Lake Python

Lecture 146 Cliff Walking Python

Section 42: Function Approximation - Reinforcement Learning

Lecture 147 Function Approximation in RL

Lecture 148 Tile Coding

Lecture 149 Neural Networks in Reinforcement Learning

Section 43: Policy Gradient Methods

Lecture 150 What is Reinforce?

Lecture 151 REINFORCE - Python

Lecture 152 Generalized Advantage Estimation (GAE)

Lecture 153 Generalized Advantage Estimation (GAE) - Python

Lecture 154 Advantage Actor-Critic (A2C)

Lecture 155 Asynchronous Advantage Actor-Critic (A3C)

Lecture 156 Deterministic Policy Gradient (DPG)

Lecture 157 DDPG (Deep Deterministic Policy Gradient)

Lecture 158 TD3 (Twin Delayed DDPG)

Lecture 159 SAC (Soft Actor-Critic)

Lecture 160 Trust Region Policy Optimization (TRPO)

Lecture 161 Proximal Policy Optimization

Section 44: Deep Q-Networks

Lecture 162 DQN Intro

Section 45: Multi-Agent Reinforcement Learning

Lecture 163 Introduction to Multi-Agent Reinforcement Learning

Lecture 164 MARL Types

Lecture 165 MARL Training

Lecture 166 MARL Challenge

Lecture 167 MARL - Predator & Prey

Lecture 168 MARL - Predator & Prey Animated Outputs

Section 46: Sequential Decision Analytics

Lecture 169 Sequential Decision Making Intro

Lecture 170 SDA Project with Julia - 1

Lecture 171 Dynamic Inventory Management - Python

Lecture 172 Adaptive Market Planning

Lecture 173 Portfolio Management

Lecture 174 SDA Project with Julia - 2

Lecture 175 Airline Pricing with Python - Code

Lecture 176 Airline Pricing - Output

Section 47: Natural Language Processing (NLP)

Lecture 177 Mathematical Foundations of NLP

Lecture 178 Text Preprocessing Techniques

Lecture 179 Vector Space Models and Word Embeddings

Section 48: AI and Complexity Theory

Lecture 180 AI and the P vs NP Problem

Section 49: Latent Variable Models in AI

Lecture 181 Hidden Markov Models (HMMs)

Lecture 182 Latent Dirichlet Allocation (LDA)

Section 50: Advanced Deep Learning Techniques

Lecture 183 Neural Tangent Kernel (NTK)

Section 51: Industry 4.0 Projects with AI

Lecture 184 Introduction to Project

Lecture 185 EDA & Linear Regression

Lecture 186 Support Vector Regression - 1

Lecture 187 Support Vector Regression - 2

Lecture 188 Time Series

Lecture 189 Random Forest

Lecture 190 Neural Networks - 1

Lecture 191 Neural Networks - 2

Lecture 192 Multi-Layer Perceptron (MLP) - 1

Lecture 193 Multi-Layer Perceptron (MLP) - 2

Lecture 194 Multi-Layer Perceptron (MLP) - 3

Section 52: Explainable and Interpretable AI

Lecture 195 What is SHAP

Lecture 196 SHAP California Project

Lecture 197 LIME

Lecture 198 LIME Class Project

Lecture 199 LIME Project

Lecture 200 LIME Cancer Project

Lecture 201 LIME Film Text Project

Lecture 202 Boruta

Lecture 203 Boruta Project

Section 53: MLOps Basics

Lecture 204 What is MLOPS

Lecture 205 MLOps Components

Lecture 206 ML Projects Lifecycle

Lecture 207 CI CD Pipeline Diagram

Section 54: Advanced Theoretical Concepts and Algorithms

Lecture 208 Learning Theory: PAC Learning and VC Dimension

Lecture 209 Metric Learning

Lecture 210 Hopfield Networks

Lecture 211 Kohonen Networks

Lecture 212 Extreme Learning Machine

Lecture 213 Bayesian Optimization

Lecture 214 Restricted Boltzmann Machine

Lecture 215 Information Theory in Machine Learning

Lecture 216 Maximum Likelihood Estimation (MLE)

Lecture 217 Bayesian Inference

Lecture 218 Bayesian Inference in Machine Learning

Lecture 219 Bayesian Networks

This course is for learners who want to gain a solid understanding of artificial intelligence from the ground up. It’s a good fit for students, engineers, developers, or professionals who want to learn how AI systems work, how to implement them properly, and how to build from scratch instead of just using pre-built tools. If you're looking for a course that explains not only how, but also why — without skipping the math or the code — this is designed for you.