• PART 1: Introduction to Python
- Introduction to Python programming language and its role in machine learning and deep learning.
- Python syntax and basic programming concepts, including variables, data types, operators, and control structures (if statements, loops).
- Introduction to functions and how to define and call them.
- Working with Python libraries such as NumPy, Pandas, and Matplotlib for data manipulation and visualization.
- Understanding the importance of Python for data analysis, machine learning, and deep learning tasks.
- Hands-on exercises and coding examples to practice Python syntax, data manipulation using libraries, and basic programming concepts.
- Guided exercises to load and manipulate data using NumPy arrays, perform basic statistical operations, and create plots using Matplotlib.
PART 2: Data Pre-processing
- Importance of data pre-processing in machine learning and deep learning.
- Data cleaning, handling missing values, and outlier detection.
- Feature scaling, encoding categorical variables, and feature transformation techniques.
PART 3: Supervised Learning Algorithms
- Simple linear regression and multiple linear regression for continuous variable prediction.
- Logistic regression for binary classification.
- k-NearestNeighbors (K-NN) algorithm for classification.
- Decision tree classification and random forest classification.
PART 4: Unsupervised Learning Algorithms
- Introduction to clustering algorithms.
- K-means clustering and hierarchical clustering techniques.
- Evaluation metrics for clustering algorithms.
PART 5: Introduction to Deep learning
- Basics of neural networks and their architecture.
- Single-layer neural network for simple classification tasks.
- Introduction to multi-layer neural networks and deep learning.
- Activation functions, loss functions, and optimization algorithms.
PART 6: Capstone Projects
- Real-world capstone projects applying machine learning and deep learning techniques.