Machine Learning with Scikit-Learn & Python

Xcel Learning

Review Questions and Assessments

Included in course price

• Chapter 1: Introduction to Machine Learning 07:00

  • 1: Chapter 1: Introduction to Machine Learning Preview 07:00

• Chapter 2: Python Foundations for Machine Learning 07:00

  • 2: Chapter 2: Python Foundations for Machine Learning 07:00

• Chapter 3: Data Preprocessing Essentials 07:00

  • 3: Chapter 3: Data Preprocessing Essentials 07:00

• Chapter 4: Introduction to Scikit-Learn 07:00

  • 4: Chapter 4: Introduction to Scikit-Learn 07:00

• Chapter 5: Regression Algorithms 07:00

  • 5: Chapter 5: Regression Algorithms 07:00

• Chapter 6: Classification Algorithms 07:00

  • 6: Chapter 6: Classification Algorithms 07:00

• Chapter 7: Model Evaluation Techniques 07:00

  • 7: Chapter 7: Model Evaluation Techniques 07:00

• Chapter 8: Feature Engineering 07:00

  • 8: Chapter 8: Feature Engineering 07:00

• Chapter 9: Unsupervised Learning 07:00

  • 9: Chapter 9: Unsupervised Learning 07:00

• Chapter 10: Model Optimization 07:00

  • 10: Chapter 10: Model Optimization 07:00

• Chapter 11: Model Deployment Basics 07:00

  • 11: Chapter 11: Model Deployment Basics 07:00

• Chapter 12: Real-World Projects 07:00

  • 12: Chapter 12: Real-World Projects 07:00

• Review Questions and Assessments 00:00

  • 13: Review Questions and Assessments -

Exciting Journey Ahead: Discover What Awaits in This Course!

Chapter 1: Introduction to Machine Learning

1. What is Machine Learning?
2. Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
3. Real-world Applications of Machine Learning
4. Overview of the Scikit-Learn Ecosystem
5. Setting Up Python Environment (Anaconda, pip, virtualenv)

Chapter 2: Python Foundations for Machine Learning

1. Essential Python Syntax Review
2. NumPy for Numerical Computing
3. Pandas for Data Manipulation
4. Data Visualization with Matplotlib & Seaborn
5. Jupyter Notebook Workflow

Chapter 3: Data Preprocessing Essentials

1. Understanding Data Types and Structures
2. Handling Missing Values
3. Encoding Categorical Variables
4. Feature Scaling (Standardization vs Normalization)
5. Train-Test Split and Cross-Validation Basics

Chapter 4: Introduction to Scikit-Learn

1. Scikit-Learn API Design Principles
2. Estimators, Transformers, and Pipelines
3. Loading Built-in Datasets
4. Model Training Workflow
5. Saving and Loading Models with Joblib

Chapter 5: Regression Algorithms

1. Linear Regression
2. Polynomial Regression
3. Ridge and Lasso Regularization
4. Decision Tree Regression
5. Random Forest Regression

Chapter 6: Classification Algorithms

1. Logistic Regression
2. k-Nearest Neighbors (k-NN)
3. Support Vector Machines (SVM)
4. Decision Tree Classification
5. Random Forest Classification

Chapter 7: Model Evaluation Techniques

1. Evaluation Metrics for Regression (MAE, MSE, R2)
2. Evaluation Metrics for Classification (Accuracy, Precision, Recall, F1)
3. Confusion Matrix and ROC Curves
4. Cross-Validation Strategies
5. Bias-Variance Tradeoff

Chapter 8: Feature Engineering

1. Feature Selection Techniques
2. Feature Extraction (PCA Basics)
3. Creating Custom Features
4. Handling Imbalanced Data
5. Dimensionality Reduction Techniques

Chapter 9: Unsupervised Learning

1. Clustering Concepts
2. K-Means Clustering
3. Hierarchical Clustering
4. DBSCAN Clustering
5. Evaluating Clustering Results

Chapter 10: Model Optimization

1. Hyperparameter Tuning Concepts
2. Grid Search and Random Search
3. Pipeline Optimization
4. Ensemble Methods Overview
5. Model Comparison Strategies

Chapter 11: Model Deployment Basics

1. Exporting Models for Production
2. Building Simple APIs with Flask or FastAPI
3. Model Serialization Best Practices
4. Integrating Models into Applications
5. Monitoring Model Performance

Chapter 12: Real-World Projects

1. End-to-End Regression Project
2. End-to-End Classification Project
3. Customer Segmentation with Clustering
4. Model Optimization Case Study
5. Final Capstone Project

Don't miss out on the chance to discover your full potential. Enroll today and open the door to a world of opportunities. Receive an exclusive digital certificate upon completing the course!

This course is designed for beginners and intermediate learners who want to build practical machine learning skills using Python and Scikit-Learn. It suits students, data analysts, developers, and professionals seeking hands-on experience with real-world models, data preprocessing evaluation techniques, and deployment fundamentals. No prior advanced mathematics knowledge is strictly required.

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