Machine Learning with Python

Machine Learning with Python

Course Overview

This course provides a comprehensive introduction to Machine Learning using Python. It covers fundamental concepts, data preprocessing techniques, and key algorithms for regression, classification, clustering, and deep learning. Participants will gain hands-on experience applying machine learning techniques to real-world problems.

Course Goal

Upon completion of this course, students will be able to:

  • Understand core concepts and principles of Machine Learning
  • Utilize Python libraries commonly used in Machine Learning (e.g., NumPy, Pandas, Scikit-learn)
  • Apply regression and classification algorithms to solve real-world problems
  • Interpret and implement syntax and semantics in Machine Learning with Python
  • Design, evaluate, and analyze various Machine Learning models
  • Apply Machine Learning techniques across different application domains
Course Modules

Session 1: Introduction to Machine Learning

  • What is Machine Learning?
  • Applications for Machine Learning
  • Types of Learning: Supervised vs. Unsupervised
  • Overview of Python libraries for Machine Learning

Session 2: Data pre-processing

  • Handling Missing Values
  • Encoding Categorical Data
  • Splitting the Dataset (Training & Testing)
  • Feature Scaling

Session 3: Regression Techniques

  • Introduction to Regression
  • Types of Regression Models
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Decision Tree Regression
  • Random Forest Regression

Session 4: Classification Techniques

  • Fundamentals of Classification
  • Classification vs. Regression
  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naïve Bayes
  • Decision Tree Classification
  • Random Forest Classification

Session 5: Clustering

  • K-Means Clustering
  • Hierarchical Clustering

Session 6: Natural Language Processing (NLP)

  • Introduction to NLP
  • NLTK Installation and Setup
  • Tokenization Techniques
  • Case Study: Document Classification

Session 7: Deep Learning

  • Introduction to Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)

Session 8: Dimensionality Reduction

  • Introduction to Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Kernel PCA