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Machine Learning with Python + Scikit-Learn

Master the art and science of machine learning using Python's most powerful ML library. Build, evaluate, and deploy machine learning models that solve real-world problems.

Course Overview

This comprehensive course takes you from machine learning fundamentals to advanced model deployment. Using Scikit-Learn and the broader Python ecosystem, you'll learn to build predictive models, classify data, discover patterns, and create intelligent systems that learn from data.

Learning Objectives

By the end of this course, you will be able to:

  • Understand core machine learning concepts and algorithms
  • Build and evaluate supervised learning models
  • Apply unsupervised learning techniques for pattern discovery
  • Perform feature engineering and selection
  • Optimize model performance through hyperparameter tuning
  • Deploy machine learning models to production environments

Course Curriculum

Week 1: Machine Learning Fundamentals

  • ML Overview: Types of machine learning and problem formulation
  • Scikit-Learn Ecosystem: Library structure and key components
  • Data Preparation: Feature scaling, encoding, and preprocessing
  • Train-Test Split: Proper data splitting and validation strategies

Week 2: Supervised Learning - Regression

  • Linear Regression: Simple and multiple linear regression
  • Polynomial Regression: Non-linear relationships and feature engineering
  • Regularization: Ridge, Lasso, and Elastic Net regression
  • Model Evaluation: Metrics for regression problems

Week 3: Supervised Learning - Classification

  • Logistic Regression: Binary and multiclass classification
  • Decision Trees: Tree-based classification and interpretation
  • Random Forest: Ensemble methods and feature importance
  • Support Vector Machines: SVM for classification tasks

Week 4: Advanced Classification Algorithms

  • Naive Bayes: Probabilistic classification methods
  • K-Nearest Neighbors: Instance-based learning
  • Gradient Boosting: XGBoost and advanced ensemble methods
  • Neural Networks: Multi-layer perceptrons with Scikit-Learn

Week 5: Model Evaluation and Selection

  • Cross-Validation: K-fold and stratified cross-validation
  • Performance Metrics: Accuracy, precision, recall, F1-score, ROC-AUC
  • Confusion Matrices: Understanding classification errors
  • Hyperparameter Tuning: Grid search and random search

Week 6: Unsupervised Learning

  • Clustering: K-means, hierarchical, and DBSCAN clustering
  • Dimensionality Reduction: PCA, t-SNE, and feature selection
  • Association Rules: Market basket analysis and recommendation systems
  • Anomaly Detection: Identifying outliers and unusual patterns

Week 7: Feature Engineering and Selection

  • Feature Creation: Polynomial features, interactions, and transformations
  • Feature Selection: Univariate selection, recursive feature elimination
  • Feature Importance: Tree-based and permutation importance
  • Handling Categorical Data: Encoding strategies and best practices

Week 8: Advanced Topics

  • Imbalanced Datasets: SMOTE, class weights, and sampling techniques
  • Pipeline Creation: Scikit-Learn pipelines for reproducible workflows
  • Model Persistence: Saving and loading trained models
  • Performance Optimization: Efficient model training and prediction

Week 9: Model Deployment and MLOps

  • Model Serialization: Pickle and joblib for model persistence
  • Web APIs: Flask and FastAPI for model serving
  • Containerization: Docker for model deployment
  • Monitoring: Model performance tracking and drift detection

Week 10: Capstone Project and Advanced Applications

  • End-to-End Project: Complete ML pipeline from data to deployment
  • Advanced Techniques: Ensemble methods and model stacking
  • Industry Applications: Real-world case studies and best practices
  • Career Preparation: Portfolio development and interview preparation

Hands-on Projects

Project 1: House Price Prediction

  • Build regression models to predict real estate prices
  • Practice feature engineering and model selection
  • Compare multiple algorithms and evaluation metrics

Project 2: Customer Churn Prediction

  • Develop classification models for customer retention
  • Handle imbalanced datasets and business constraints
  • Create actionable insights for business stakeholders

Project 3: Market Segmentation Analysis

  • Apply clustering techniques to customer data
  • Discover hidden patterns and customer segments
  • Develop targeted marketing strategies based on findings

Project 4: Recommendation System

  • Build collaborative filtering recommendation engine
  • Implement content-based and hybrid approaches
  • Evaluate recommendation quality and business impact

Capstone Project: Complete ML Solution

  • End-to-end machine learning project of your choice
  • Include data collection, preprocessing, modeling, and deployment
  • Present findings and demonstrate business value

Real-world Applications

Business and Finance

  • Credit Scoring: Assess loan default risk
  • Fraud Detection: Identify suspicious transactions
  • Algorithmic Trading: Automated investment strategies
  • Price Optimization: Dynamic pricing strategies

Healthcare and Life Sciences

  • Medical Diagnosis: Assist in disease detection
  • Drug Discovery: Identify potential therapeutic compounds
  • Personalized Medicine: Tailor treatments to individuals
  • Epidemiology: Track and predict disease outbreaks

Technology and Internet

  • Recommendation Systems: Personalized content and products
  • Search Engines: Improve search relevance and ranking
  • Computer Vision: Image recognition and analysis
  • Natural Language Processing: Text analysis and understanding

Marketing and Retail

  • Customer Segmentation: Targeted marketing campaigns
  • Demand Forecasting: Inventory optimization
  • A/B Testing: Optimize marketing strategies
  • Sentiment Analysis: Brand monitoring and reputation management

Tools and Technologies

Core Libraries

  • Scikit-Learn: Primary machine learning library
  • Pandas: Data manipulation and analysis
  • NumPy: Numerical computing foundation
  • Matplotlib/Seaborn: Data visualization

Advanced Tools

  • XGBoost/LightGBM: Gradient boosting frameworks
  • Imbalanced-Learn: Handling imbalanced datasets
  • Optuna: Hyperparameter optimization
  • SHAP: Model interpretability and explainability

Deployment Tools

  • Flask/FastAPI: Web framework for model APIs
  • Docker: Containerization for deployment
  • AWS/GCP/Azure: Cloud platforms for scaling
  • MLflow: Experiment tracking and model management

Course Features

Practical Focus

  • Real Datasets: Work with industry-standard datasets
  • Business Context: Understand ML applications in business
  • Best Practices: Learn professional ML development workflows
  • Code Quality: Write clean, maintainable ML code

Interactive Learning

  • Jupyter Notebooks: Interactive development environment
  • Live Coding: Follow instructor demonstrations
  • Peer Learning: Collaborate on challenging problems
  • Office Hours: Direct access to instructors

Career Preparation

  • Portfolio Projects: Showcase your ML skills
  • Industry Insights: Learn from experienced practitioners
  • Interview Preparation: Technical interview practice
  • Networking: Connect with ML professionals

Prerequisites

Technical Skills

  • Python Programming: Solid Python fundamentals
  • Data Analysis: Proficiency with Pandas and NumPy
  • Statistics: Understanding of statistical concepts
  • Mathematics: Basic linear algebra and calculus
  • Completion of "Data Analysis with Python" course
  • Understanding of probability and statistics
  • Familiarity with data visualization
  • Basic knowledge of algorithms and data structures

Career Outcomes

Job Roles

  • Machine Learning Engineer: Build and deploy ML systems
  • Data Scientist: Extract insights using advanced analytics
  • AI Researcher: Develop new ML algorithms and techniques
  • Product Manager (AI/ML): Guide AI product development

Salary Expectations

  • Entry Level: $70,000 - $95,000 annually
  • Mid Level: $95,000 - $130,000 annually
  • Senior Level: $130,000 - $180,000+ annually
  • Principal/Staff: $180,000 - $300,000+ annually

Industry Demand

  • High Growth: ML roles growing 22% annually
  • Diverse Industries: Opportunities across all sectors
  • Remote Work: Many positions offer remote flexibility
  • Startup to Enterprise: Opportunities at all company sizes

Assessment and Certification

Evaluation Methods

  • Weekly Assignments: Hands-on ML implementation exercises
  • Project Portfolio: Collection of ML projects
  • Peer Reviews: Collaborative learning and code review
  • Final Capstone: Comprehensive ML solution

Certification Value

  • Industry Recognition: Credential valued by top employers
  • Skill Verification: Demonstrate ML proficiency
  • Portfolio Enhancement: Professional project showcase
  • Career Advancement: Qualification for ML roles

Continuing Education

  • Deep Learning: Advanced neural networks with PyTorch
  • MLOps Specialization: Production ML systems
  • Domain Expertise: Industry-specific ML applications
  • Research Opportunities: Academic and industrial research

Launch your machine learning career! This course provides the comprehensive skills and practical experience needed to succeed as an ML professional in today's AI-driven world.

Join the next generation of machine learning practitioners and help build the future of intelligent systems.