Course Content

1. Introduction to AI & ML
2. Introduction to Python
3. Getting around Anaconda and Jupyter Notebook
4. Python Basics
   – Data Types
   – Conditional Statements, Loops and Control Statements
   – Functions
   – Lambda Functions and other built-in functions
   – File Handling
5. NumPy
   – NumPy Basics
   – Operations
   – Indexing, Slicing and Copies
6. Pandas
   – Series
   – DataFrames
   – Fix Missing Data
   – GroupBy
   – Merge
   – Operations
   – File Reading and Writing
7. Data Visualization Techniques
   – Matplotlib
   – Pandas built-in visualization
   – Seaborn
8. Machine Learning (Theory and Application)
   – Machine Learning with Python
   – Supervised- Linear Regression
   – Supervised- Logistic Regression
   – Supervised- Decision Tree
   – Supervised- Support Vector Machine
   – Supervised– K-Nearest Neighbours
   – Unsupervised– K-Means Clustering
   – Unsupervised– Association Rule Learning and Apriori
9. Image Processing
   – OpenCV
   – Drawing Functions
   – Basic Operations
   – Arithmetic Operations, Blending and Transition
   – Colour Spaces, Histograms, and Thresholding
   – Bitwise Operations and Masking
10. Computer Vision
    – Object Tracking
    – Optical Character Recognition
    – Face Detection
    – Facial Feature Detection
    – Car Detection and Tracking
11. Projects