Python Machine Learning: Regression, Classification, and Clustering

What Will You Learn?

• Build linear regression models to predict continuous outcomes such as sales, profit, customer spending, or house prices
• Use decision tree regression models to capture more complex relationships in data that linear regression cannot explain properly
• Build random forest regression models to improve prediction accuracy and reduce the risk of overfitting
• Apply support vector regression models to solve more advanced prediction problems where relationships between variables are not straightforward
• Understand how XGBoost regression works and why it is considered one of the most powerful algorithms for prediction tasks
• Build logistic regression models to predict categories such as yes or no, approved or rejected, churn or no churn, and fraud or non-fraud
• Use decision tree classification models to create easy-to-understand prediction systems with branching logic
• Apply random forest classification models to improve classification accuracy and reduce instability in predictions
• Build K Nearest Neighbours classification models to classify data points based on similarity with nearby observations
• Learn how LightGBM classification models work and why they are widely used in high-performance machine learning applications
• Build KMeans clustering models to identify hidden groups and patterns in datasets without needing predefined labels
• Understand the strengths and weaknesses of different machine learning algorithms so you can make smarter model choices
• Gain practical experience by building every model in Python instead of only learning theoretical concepts
• Develop the confidence to work on real-world machine learning projects related to customer segmentation, sales forecasting, fraud detection, churn prediction, and business analytics
• Learn how to move from raw data to actual prediction and decision-making models in a structured way