Machine Learning Course
Welcome to the Machine Learning Course. This course takes you from the fundamental concepts and algorithms of Machine Learning to advanced methods such as Deep Learning. The goal is to provide both theoretical understanding and hands-on practice so that you can confidently apply ML techniques to real-world problems.
The course combines theory with practical examples, helping you build intuition while also learning how to implement algorithms in Python. Whenever advanced concepts are introduced, supporting reference materials will be provided for review.
Prerequisites
To make the most of this course, you should have a basic understanding of: - Python programming (variables, loops, functions, classes, and libraries such as NumPy and Pandas)
- Linear Algebra (vectors, matrices, operations)
- Calculus (derivatives and gradients)
- Basic Probability and Statistics
If you need a refresher on any of these topics, links and references will be included at the relevant points in the course.
Course Structure
The course is divided into several modules, each building on the previous one:
- Introduction to Machine Learning
- What is ML?
- Main branches of ML (Supervised, Unsupervised, Reinforcement Learning)
- Core concepts and terminology
- What is ML?
- Data Preparation and Preprocessing
- Data cleaning and transformation
- Feature scaling and encoding
- Train/test splits and validation
- Data cleaning and transformation
- Fundamental Algorithms
- Linear Regression
- Logistic Regression
- k-Nearest Neighbors (kNN)
- Naive Bayes
- Linear Regression
- Intermediate Algorithms
- Decision Trees
- Random Forests
- Support Vector Machines (SVM)
- Ensemble Methods
- Decision Trees
- Unsupervised Learning
- Clustering (k-Means, Hierarchical, DBSCAN)
- Dimensionality Reduction (PCA, t-SNE)
- Clustering (k-Means, Hierarchical, DBSCAN)
- Performance Metrics and Model Evaluation
- Accuracy, Precision, Recall, F1-Score
- ROC Curves and AUC
- Confusion Matrix
- Regression Metrics (MSE, RMSE, MAE, R²)
- Cross-validation techniques
- Bias-variance tradeoff
- Accuracy, Precision, Recall, F1-Score
- Neural Networks and Deep Learning
- Perceptrons and Feedforward Neural Networks
- Backpropagation and Gradient Descent
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs, LSTMs, GRUs)
- Transfer Learning
- Perceptrons and Feedforward Neural Networks
- Advanced Topics
- Generative Models (GANs, VAEs)
- Reinforcement Learning Basics
- Transformers and Attention Mechanisms
- Genetic Algorithms and Evolutionary Computation
- Generative Models (GANs, VAEs)
- Model Deployment and Serving
- Saving and loading models
- Hyperparameter tuning and optimization
- Model deployment in production environments
- Monitoring and maintaining ML systems
- Saving and loading models
Learning Approach
- Hands-on coding using Python libraries such as Scikit-Learn and TensorFlow
- Step-by-step theory explanations to understand the “why” behind algorithms
- Practical examples to connect concepts to real-world applications
By the end of this course, you will have both the practical skills and the conceptual foundations to apply Machine Learning methods effectively, from simple predictive models to state-of-the-art Deep Learning architectures.