Machine Learning for Predictive Models
( 25 Modules )

Module #1
Introduction to Machine Learning
Overview of machine learning, types of machine learning, and importance of predictive models

Module #2
Mathematical Foundations
Linear Algebra, Calculus, and Probability Theory essentials for machine learning

Module #3
Types of Predictive Models
Regression, Classification, Clustering, and Dimensionality Reduction techniques

Module #4
Supervised Learning
Introduction to supervised learning, advantages, and real-world applications

Module #5
Unsupervised Learning
Introduction to unsupervised learning, advantages, and real-world applications

Module #6
Data Preprocessing
Handling missing values, feature scaling, normalization, and data transformation

Module #7
Feature Engineering
Techniques for feature extraction, selection, and creation

Module #8
Model Evaluation Metrics
Measuring model performance using accuracy, precision, recall, F1-score, and more

Module #9
Regression Analysis
Simple and multiple linear regression, polynomial regression, and regularization techniques

Module #10
Logistic Regression
Binary and multi-class logistic regression, confusion matrices, and ROC curves

Module #11
Decision Trees and Random Forests
Introduction to decision trees, random forests, and ensemble learning

Module #12
Support Vector Machines (SVMs)
SVMs for classification and regression, kernel methods, and regularization techniques

Module #13
K-Nearest Neighbors (KNN)
KNN algorithm for classification and regression, distance metrics, and parameter tuning

Module #14
Clustering Algorithms
K-means, Hierarchical clustering, and density-based clustering methods

Module #15
Neural Networks and Deep Learning
Introduction to neural networks, deep learning, and TensorFlow/Keras

Module #16
Natural Language Processing (NLP)
Text preprocessing, tokenization, and sentiment analysis using NLP techniques

Module #17
Model Selection and Hyperparameter Tuning
Cross-validation, grid search, and random search for hyperparameter tuning

Module #18
Overfitting and Underfitting
Understanding overfitting and underfitting, and techniques to prevent them

Module #19
Big Data and NoSQL Databases
Handling large datasets, NoSQL databases, and distributed computing

Module #20
Apache Spark and MLlib
Distributed machine learning using Apache Spark and MLlib

Module #21
Model Deployment and Integration
Deploying machine learning models, model serving, and integration with web applications

Module #22
Explainable AI and Model Interpretability
Techniques for model interpretation, feature importance, and SHAP values

Module #23
Ethics and Fairness in Machine Learning
Fairness, bias, and ethics in machine learning, and techniques to mitigate them

Module #24
Case Studies and Project Development
Real-world case studies and project development using machine learning techniques

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning for Predictive Models career

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