Predictive Modeling and Machine Learning
( 24 Modules )

Module #1
Introduction to Predictive Modeling and Machine Learning
Overview of predictive modeling and machine learning, importance, and applications

Module #2
Types of Machine Learning
Supervised, unsupervised, and reinforcement learning, differences and examples

Module #3
Regression Analysis
Simple and multiple linear regression, assumptions, and applications

Module #4
Logistic Regression
Binary and multi-class logistic regression, odds ratio, and probability

Module #5
Decision Trees
Basic concepts, building, and interpreting decision trees

Module #6
Random Forest
Ensemble learning, random forest algorithm, and hyperparameter tuning

Module #7
Support Vector Machines (SVMs)
Linear and non-linear SVMs, kernel trick, and applications

Module #8
K-Means Clustering
K-means algorithm, clustering evaluation metrics, and applications

Module #9
Hierarchical Clustering
Agglomerative and divisive clustering, dendrograms, and applications

Module #10
Dimensionality Reduction
PCA, t-SNE, and feature selection techniques

Module #11
Model Evaluation Metrics
Confusion matrix, accuracy, precision, recall, F1-score, and ROC-AUC

Module #12
Overfitting and Underfitting
Causes, consequences, and solutions to overfitting and underfitting

Module #13
Hyperparameter Tuning
Grid search, random search, and Bayesian optimization

Module #14
Deep Learning Fundamentals
Introduction to neural networks, activation functions, and backpropagation

Module #15
Convolutional Neural Networks (CNNs)
Architecture, convolutional layers, pooling layers, and applications

Module #16
Recurrent Neural Networks (RNNs)
Architecture, simple and LSTM RNNs, and applications

Module #17
Natural Language Processing (NLP)
Text preprocessing, tokenization, and word embeddings

Module #18
Model Deployment and Scaling
Model deployment, model serving, and scalability considerations

Module #19
Big Data and Distributed Computing
Hadoop, Spark, and distributed machine learning

Module #20
Machine Learning Best Practices
Model interpretability, explainability, and ethics in machine learning

Module #21
Case Studies in Predictive Modeling and Machine Learning
Real-world applications and case studies in various industries

Module #22
Hands-on Project Development
Developing and deploying a predictive model or machine learning project

Module #23
Model Monitoring and Maintenance
Model monitoring, updating, and maintenance strategies

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Predictive Modeling and Machine Learning career

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Predictive Modeling and Machine Learning ( 24 Modules )

Predictive Modeling and Machine Learning
( 24 Modules )