Sale Date Ended
➢ Introduction
➢ Python Revisited and Tools for Development
- Anaconda , Jupyter Notebook
➢ Machine Learning – Introduction
- Supervised and Unsupervised Learning.
- bias -variance dichotomy
➢ Python Lib for Mathematical Ops (NumPy)
- NumPy Overview
- Properties, Purpose, and Types of ndarray
- Class and Attributes of ndarray Object
- Basic Operations: Concept and Examples
- Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
- Copy and Views
- Shape Manipulation
- Broadcasting
- Linear Algebra
- Code Exercises
➢ Scientific Computing with Python
- SciPy
- SciPy sub-packages
- Subpackages: cluster, fftpack , linalg , signal, integrate, optimize, stats
- Code Exercises
➢ Data Manipulation with Python (Pandas)
- Introduction to Pandas Data Structures
- Series
- Dataframe
- Missing Values and Data Cleansing
- Data Operations
➢ Statistical and Mathematical operations
- Matrix operations, Probability Theory(Bayes' Theorem)
- Statistical knowledge for ML- Mean, Median, Mode , Z-scores,
- Hypothesis Testing
- Chi-Square Test
➢ Data Visualization (Matplotlib)
- Plotting Graphs and Charts
- Matplotlib Features:
✓ Line Properties Plot with (x, y)
✓ Controlling Line Patterns and Colors
✓ Set Axis, Labels, and Legend Properties
✓ Alpha and Annotation
✓ Multiple Plots
✓ Subplots
➢ Machine Learning with Python (Scikit–Learn)
- ML Glossary- Variable types, k-fold CV, AUC ,F1 score, Overfitting /Underfitting, Generalization, Data split & hyper parameter training
- Supervised learning – Regression
- Different types of Regression - Linear and Logistic
- Decision tree Algorithms
- Supervised Learning- Classification
- KNN Classification
- Clustering- Introduction , k-means clustering
- Code Exercises
➢ Advanced Topics – Introduction
- Dimensionality Reduction , Principal Component Analysis
- Support vector machine
