Full Course: AI & Machine Learning (ML)
Full Course: AI & Machine Learning (ML)
A comprehensive journey from the absolute basics of Artificial Intelligence to advanced Machine Learning algorithms. This course covers Python programming, mathematical foundations, supervised and unsupervised learning, deep learning, and real-world deployment. Designed for beginners, it bridges the gap between theory and practical application using industry-standard tools like Scikit-Learn, TensorFlow, and Pandas.
Lessons
- Introduction to Artificial Intelligence
- What is Machine Learning?
- History and Evolution of AI
- Types of Machine Learning
- Setting Up the Python Environment
- Python Basics for Data Science
- Linear Algebra: Scalars and Vectors
- Matrices and Tensors
- Calculus: Derivatives for Optimization
- Statistics: Mean, Median, and Mode
- Probability Basics for AI
- NumPy: Working with Numerical Data
- Pandas: Data Analysis Made Easy
- Data Visualization with Matplotlib
- Advanced Visuals with Seaborn
- Data Cleaning: Handling Missing Values
- Exploratory Data Analysis (EDA)
- Simple Linear Regression
- Cost Function and Gradient Descent
- Multiple Linear Regression
- Train/Test Split and Model Evaluation
- Categorical Data Encoding
- Feature Scaling: Standard vs Normal
- Logistic Regression for Classification
- The Sigmoid Function Explained
- Confusion Matrix and Accuracy
- Precision, Recall, and F1-Score
- K-Nearest Neighbors (KNN)
- Decision Trees: Theory
- Entropy and Information Gain
- Random Forest: Ensemble Learning
- Support Vector Machines (SVM)
- The Kernel Trick in SVM
- Naive Bayes Classifier
- Bias vs Variance Tradeoff
- Regularization: Lasso and Ridge
- Hyperparameter Tuning with Grid Search
- K-Fold Cross-Validation
- Introduction to Unsupervised Learning
- K-Means Clustering
- The Elbow Method
- Hierarchical Clustering
- DBSCAN: Density-Based Clustering
- Principal Component Analysis (PCA)
- Association Rule Learning
- Intro to Recommender Systems
- Collaborative vs Content Filtering
- Intro to Neural Networks
- The Perceptron: The Single Neuron
- Activation Functions: ReLU and Sigmoid
- Multilayer Perceptron (MLP)
- Backpropagation Algorithm
- Loss Functions in Deep Learning
- Optimizers: SGD and Adam
- Overfitting in Deep Learning
- Dropout and Early Stopping
- Intro to Convolutional Neural Networks (CNN)
- Pooling and Stride in CNNs
- Building an Image Classifier
- Intro to Recurrent Neural Networks (RNN)
- LSTMs and GRUs
- Natural Language Processing (NLP) Basics
- Word Embeddings: Word2Vec
- Sentiment Analysis Project
- Transfer Learning
- Introduction to Transformers
- GANs: Generative Adversarial Networks
- Ethics in AI: Bias and Fairness
- Deploying Models with Flask
- Streamlit for Data Apps
- Introduction to MLOps
- Time Series Forecasting Intro
- Reinforcement Learning: Q-Learning
- Dimensionality Reduction: t-SNE
- Hyperparameter Optimization with Optuna
- Cloud AI: Intro to AWS SageMaker
- Object Detection vs Image Classification
- Introduction to PyTorch
- Future Trends: Generative AI and LLMs
- Final Capstone Project Guide