Artificial Intelligence Masterclass With Python : 1

Learn AI from scratch with hands-on projects: Machine Learning, Deep Learning, Reinforcement Learning

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: Mathematical Foundations for AI

Lecture 2 Vectors & Vector Operations Theory

Lecture 3 Vectors & Vector Operations Practice

Lecture 4 Probability Theory Basics

Section 3: Introduction to Machine Learning

Lecture 5 Introduction to Machine Learning

Lecture 6 Machine Learning Pipeline

Lecture 7 Overview of Python Libraries for Machine Learning

Section 4: Introduction Concepts and Notation for Machine Learning

Lecture 8 ML Introduction Concepts - 1

Lecture 9 ML Introduction Concepts - 2

Lecture 10 ML Introduction Concepts - 3

Lecture 11 ML Introduction Concepts - 4

Lecture 12 Notation

Section 5: Learning

Lecture 13 What is Learning?

Lecture 14 Why Do We Predict f?

Lecture 15 Curse of Dimensionality

Lecture 16 How Do We Predict f?

Lecture 17 Prediction Accuracy or Model Simplicity?

Lecture 18 Regression vs Classification

Section 6: Measuring Model Accuracy

Lecture 19 Measuring Prediction Quality

Lecture 20 Bias-Variance Trade-Off

Lecture 21 Classification Setup

Lecture 22 KNN Example

Section 7: Simple Linear Regression

Lecture 23 Mathematical Basis of Regression

Lecture 24 Regression - Visual Explanation

Section 8: Multiple Linear Regression

Lecture 25 Multiple Linear Regression

Lecture 26 OLS Table

Lecture 27 Hypothesis Testing

Section 9: KNN

Lecture 28 KNN Intro

Section 10: Naive Bayes

Lecture 29 Introduction

Lecture 30 Naive Bayes Project

Section 11: Logistic Regression

Lecture 31 Introduction

Lecture 32 Project - LR

Section 12: Model Performance Metrics

Lecture 33 Confusion Matrix

Lecture 34 Accuracy

Lecture 35 Precision

Lecture 36 Recall

Lecture 37 F1 Score

Lecture 38 ROC-AUC Curve

Lecture 39 Log-Loss

Section 13: Model Selection

Lecture 40 Cross Validation

Lecture 41 K-Fold Cross Validation - Regression

Lecture 42 K-Fold Cross Validation -Classification

Lecture 43 Grid Search & Random Search

Section 14: Regularization

Lecture 44 Mathematical Basis of Regularization

Section 15: Support Vector Machines (SVM)

Lecture 45 The Mathematical Foundation of SVM - 1

Lecture 46 The Mathematical Foundation of SVM - 2

Lecture 47 Kernels

Lecture 48 SVM Cost Function

Section 16: Decision Trees

Lecture 49 Fundamentals

Lecture 50 Gini Index & Overfitting

Section 17: Random Forest

Lecture 51 Random Forest - Intro

Section 18: Boosting - Machine Learning

Lecture 52 Boosting - Part 1

Lecture 53 Boosting - Part 2

Section 19: Unsupervised Learning

Lecture 54 Introduction to Unsupervised Learning

Lecture 55 K-Means Clustering - Part 1

Lecture 56 K-Means Clustering - Part 2

Lecture 57 Dimensionality Reduction: PCA - 1

Lecture 58 Dimensionality Reduction: PCA - Iris

Lecture 59 PCA - MNIST

Section 20: Neural Networks and Deep Learning

Lecture 60 Introduction to Neural Networks

Section 21: Convolutional Neural Networks (CNNs)

Lecture 61 Deep Learning Architectures: CNN

Lecture 62 CNN Architectures with PyTorch

Lecture 63 CNN Architectures with Julia - Flux

Lecture 64 CNN Architecture with MATLAB

Lecture 65 1993 Yann LeCun

Section 22: Multi-Layer Perceptron

Lecture 66 MLP Mixer Structure with Pytorch

Section 23: Residual Networks (ResNets)

Lecture 67 Implementing ResNets with Python - 1

Lecture 68 Implementing ResNets with Python - 2

Section 24: Python Programming (Optional)

Lecture 69 What is Python?

Lecture 70 Anaconda & Jupyter & Visual Studio Code

Lecture 71 Python Syntax & Basic Operations

Lecture 72 Data Structures: Lists, Tuples, Sets

Lecture 73 Control Structures & Looping

Lecture 74 Functions & Basic Functional Programming

Lecture 75 Intermediate Functions

Lecture 76 Dictionaries and Advanced Data Structures

Lecture 77 Modules, Packages & Importing Libraries

Lecture 78 File Handling

Lecture 79 Exception Handling & Robust Code

Lecture 80 OOP

Lecture 81 Data Visualization Basics

Lecture 82 Advanced List Operations & Comprehensions

Section 25: Data Preprocessing (Optional)

Lecture 83 Data Quality

Lecture 84 Data Cleaning Techniques

Lecture 85 Handling Missing Value

Lecture 86 Dealing With Outliers

Lecture 87 Feature Scaling and Normalization

Lecture 88 Standardization

Lecture 89 Encoding Categorical Variables

Lecture 90 Feature Engineering

Lecture 91 Dimensionality Reduction

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.