Applied Machine Learning with Python

Xcel Learning

Review Questions and Assessments

Included in course price

• Chapter 1: Introduction to Machine Learning and Python Ecosystem 06:00

  • 1: Chapter 1: Introduction to Machine Learning and Python Ecosystem Preview 06:00

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

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

• Chapter 3: Data Preprocessing and Feature Engineering 07:00

  • 3: Chapter 3: Data Preprocessing and Feature Engineering 07:00

• Chapter 4: Exploratory Data Analysis (EDA) 07:00

  • 4: Chapter 4: Exploratory Data Analysis (EDA) 07:00

• Chapter 5: Regression Models 06:00

  • 5: Chapter 5: Regression Models 06:00

• Chapter 6: Classification Models 06:00

  • 6: Chapter 6: Classification Models 06:00

• Chapter 7: Model Selection and Validation 06:00

  • 7: Chapter 7: Model Selection and Validation 06:00

• Chapter 8: Ensemble Learning Techniques 06:00

  • 8: Chapter 8: Ensemble Learning Techniques 06:00

• Chapter 9: Unsupervised Learning 06:00

  • 9: Chapter 9: Unsupervised Learning 06:00

• Chapter 10: Introduction to Neural Networks 06:00

  • 10: Chapter 10: Introduction to Neural Networks 06:00

• Chapter 11: Model Deployment and Productionization 06:00

  • 11: Chapter 11: Model Deployment and Productionization 06:00

• Chapter 12: Applied Machine Learning Projects 05:00

  • 12: Chapter 12: Applied Machine Learning Projects 05:00

• Review Questions and Assessments 00:00

  • 13: Review Questions and Assessments -

Exciting Adventures Await: Discover the Fascinating Topics This Course Will Explore!

Chapter 1: Introduction to Machine Learning and Python Ecosystem

1. What is Machine Learning? Types and Applications
2. Supervised vs Unsupervised vs Reinforcement Learning
3. Overview of the Python ML Ecosystem (NumPy, Pandas, Matplotlib, Scikit-learn)
4. Setting Up the Development Environment (Anaconda, Jupyter, VS Code)
5. End-to-End Machine Learning Workflow

Chapter 2: Python Foundations for Machine Learning

1. NumPy Arrays and Vectorized Computation
2. Data Manipulation with Pandas
3. Data Visualization with Matplotlib and Seaborn
4. Writing Efficient Python Code for ML
5. Working with Jupyter Notebooks for Experiments

Chapter 3: Data Preprocessing and Feature Engineering

1. Data Cleaning and Handling Missing Values
2. Encoding Categorical Variables
3. Feature Scaling and Normalization
4. Feature Extraction and Transformation
5. Building Preprocessing Pipelines with Scikit-learn

Chapter 4: Exploratory Data Analysis (EDA)

1. Descriptive Statistics for ML
2. Data Visualization Techniques for Pattern Discovery
3. Correlation Analysis and Multicollinearity
4. Detecting Outliers and Anomalies
5. Automating EDA with Python Tools

Chapter 5: Regression Models

1. Linear Regression and Assumptions
2. Regularized Regression (Ridge, Lasso, ElasticNet)
3. Polynomial Regression and Feature Expansion
4. Model Evaluation Metrics for Regression
5. Implementing Regression Models in Scikit-learn

Chapter 6: Classification Models

1. Logistic Regression Fundamentals
2. k-Nearest Neighbors (k-NN)
3. Decision Trees and Random Forests
4. Support Vector Machines (SVM)
5. Evaluation Metrics for Classification (Accuracy, Precision, Recall, F1, ROC-AUC)

Chapter 7: Model Selection and Validation

1. Train/Test Split and Cross-Validation
2. Bias-Variance Tradeoff
3. Hyperparameter Tuning (Grid Search, Random Search)
4. Performance Comparison and Model Selection
5. Handling Imbalanced Datasets

Chapter 8: Ensemble Learning Techniques

1. Bagging and Random Forests
2. Boosting (AdaBoost, Gradient Boosting)
3. XGBoost and LightGBM Overview
4. Stacking and Blending Models
5. Practical Ensemble Implementation in Python

Chapter 9: Unsupervised Learning

1. Clustering with k-Means
2. Hierarchical Clustering
3. DBSCAN and Density-Based Clustering
4. Dimensionality Reduction (PCA)
5. Evaluating Unsupervised Models

Chapter 10: Introduction to Neural Networks

1. Perceptron and Multilayer Perceptron (MLP)
2. Activation Functions and Loss Functions
3. Backpropagation and Optimization Algorithms
4. Implementing Neural Networks with Keras/TensorFlow
5. Preventing Overfitting in Neural Networks

Chapter 11: Model Deployment and Productionization

1. Saving and Loading ML Models (Pickle, Joblib)
2. Building APIs with Flask or FastAPI
3. Model Serialization and Versioning
4. Deploying ML Models to Cloud Platforms
5. Monitoring and Maintaining ML Systems

Chapter 12: Applied Machine Learning Projects

1. End-to-End Classification Project
2. End-to-End Regression Project
3. Customer Segmentation with Clustering
4. Building a Recommendation System
5. Capstone Project and Presentation

Unleash Your Potential: Join Us Today and Elevate Your Skills with a Prestigious Digital Certificate upon Course Completion!

This course is designed for aspiring data scientists, software developers, analysts, and students who want to apply machine learning using Python. It suits beginners with basic programming knowledge as well as professionals seeking practical skills in model building, data analysis, and real-world problem solving across various modern industries and domains.

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