Machine Learning & Predictive Analytics with Python

Course Overview

This course provides a deep dive into machine learning techniques for predictive analytics using Python. You’ll learn how to process data, build predictive models, and use real-world datasets to make data-driven decisions. By the end of the course, you’ll be able to apply machine learning concepts to business problems and drive actionable insights.

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Course Content

Module 1: Introduction to Machine Learning & Predictive Analytics

• What is machine learning?
• Supervised vs. unsupervised learning
• Applications of predictive analytics in business

Module 2: Data Preparation & Feature Engineering

• Data cleaning & preprocessing (handling missing data, outliers)
• Feature selection & feature engineering techniques
• Exploratory Data Analysis (EDA) with Python (Pandas, Seaborn)

Module 3: Supervised Learning – Regression Models

• Understanding linear & multiple regression
• Polynomial regression & regularization techniques (Ridge, Lasso)
• Evaluating model performance (R-squared, RMSE)

Module 4: Supervised Learning – Classification Models

• Logistic regression & decision trees
• Random forests & gradient boosting (XGBoost, LightGBM)
• Model evaluation metrics (Precision, Recall, F1-score, ROC-AUC)

Module 5: Unsupervised Learning & Clustering

• K-Means & Hierarchical clustering
• Principal Component Analysis (PCA) for dimensionality reduction
• Association rule learning (Apriori, Market Basket Analysis)

Module 6: Neural Networks & Deep Learning (Introductory)

• Introduction to artificial neural networks
• Understanding TensorFlow & Keras for deep learning
• Building a basic neural network model

Module 7: Model Deployment & Performance Optimization

• Hyperparameter tuning & cross-validation
• Deploying models using Flask & FastAPI
• Using cloud platforms (AWS, Google AI) for ML deployment

Module 8: Capstone Project & Certification

• Real-world case study (predictive analytics for sales forecasting, customer churn, etc.)
• Model building, evaluation & optimization
• Final project submission & certification

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Who Should Enroll?

• Aspiring data scientists & ML engineers
• Business analysts & professionals looking to apply ML techniques
• Anyone interested in learning how to predict future trends using data