Techniques in Predictive Data Mining
( 24 Modules )

Module #1
Introduction to Predictive Data Mining
Overview of predictive data mining, its importance, and applications

Module #2
Types of Predictive Models
Overview of different types of predictive models, including regression, classification, clustering, and more

Module #3
Data Preparation for Predictive Modeling
Importance of data preparation, data cleaning, data transformation, and feature scaling

Module #4
Exploratory Data Analysis (EDA)
Techniques for exploratory data analysis, including data visualization, summary statistics, and correlation analysis

Module #5
Supervised Learning Fundamentals
Introduction to supervised learning, including types of supervised learning, cost functions, and performance metrics

Module #6
Linear Regression
Simple and multiple linear regression, including assumptions, coefficients, and interpretation

Module #7
Logistic Regression
Binary and multi-class logistic regression, including odds ratio, confusion matrix, and ROC curve

Module #8
Decision Trees
Introduction to decision trees, including CART, C4.5, and ID3 algorithms

Module #9
Random Forests
Ensemble learning with random forests, including bagging, boosting, and feature importance

Module #10
Support Vector Machines (SVMs)
Introduction to SVMs, including types of SVMs, kernels, and regularization

Module #11
Unsupervised Learning Fundamentals
Introduction to unsupervised learning, including clustering, density estimation, and dimensionality reduction

Module #12
K-Means Clustering
K-means clustering algorithm, including centroid initialization, convergence, and evaluation metrics

Module #13
Hierarchical Clustering
Hierarchical clustering, including agglomerative and divisive clustering, and dendrograms

Module #14
Principal Component Analysis (PCA)
Introduction to PCA, including eigenvalues, eigenvectors, and dimensionality reduction

Module #15
Text Mining and Natural Language Processing (NLP)
Introduction to text mining and NLP, including text preprocessing, tokenization, and sentiment analysis

Module #16
Association Rule Mining
Introduction to association rule mining, including apriori algorithm, support, confidence, and lift

Module #17
Model Evaluation and Selection
Evaluation metrics for regression and classification, including cross-validation, bias-variance tradeoff, and model selection

Module #18
Handling Imbalanced Datasets
Techniques for handling imbalanced datasets, including undersampling, oversampling, and cost-sensitive learning

Module #19
Ensemble Methods
Ensemble learning methods, including bagging, boosting, and stacking

Module #20
Deep Learning Fundamentals
Introduction to deep learning, including perceptron, backpropagation, and neural networks

Module #21
Convolutional Neural Networks (CNNs)
Introduction to CNNs, including convolutional layers, pooling layers, and image classification

Module #22
Recurrent Neural Networks (RNNs)
Introduction to RNNs, including simple RNNs, LSTM, and sequence prediction

Module #23
Big Data Analytics
Introduction to big data analytics, including Hadoop, Spark, and NoSQL databases

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Techniques in Predictive Data Mining career

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Techniques in Predictive Data Mining ( 24 Modules )

Techniques in Predictive Data Mining
( 24 Modules )