Machine Learning and Predictive Analytics with Python Training Course

Introduction to Machine Learning

• Types of machine learning – supervised vs unsupervised.
• Transition from statistical learning to machine learning.
• The data mining workflow: business understanding, data preparation, modeling, deployment.
• Selecting the appropriate algorithm for the task.
• Overfitting and the bias-variance tradeoff.

Python and ML Libraries Overview

• Reasons for using programming languages in ML.
• Choosing between R and Python.
• Python crash course and Jupyter Notebooks.
• Python libraries: pandas, NumPy, scikit-learn, matplotlib, seaborn.

Testing and Evaluating ML Algorithms

• Generalization, overfitting, and model validation.
• Evaluation strategies: holdout, cross-validation, bootstrapping.
• Metrics for regression: ME, MSE, RMSE, MAPE.
• Metrics for classification: accuracy, confusion matrix, unbalanced classes.
• Model performance visualization: profit curve, ROC curve, lift curve.
• Model selection and grid search for tuning.

Data Preparation

• Data import and storage in Python.
• Exploratory analysis and summary statistics.
• Handling missing values and outliers.
• Standardization, normalization, and transformation.
• Qualitative data recoding and data wrangling with pandas.

Classification Algorithms

• Binary vs multiclass classification.
• Logistic regression and discriminant functions.
• Naïve Bayes, k-nearest neighbors.
• Decision trees: CART, Random Forests, Bagging, Boosting, XGBoost.
• Support Vector Machines and kernels.
• Ensemble learning techniques.

Regression and Numerical Prediction

• Least squares and variable selection.
• Regularization methods: L1, L2.
• Polynomial regression and nonlinear models.
• Regression trees and splines.

Neural Networks

• Introduction to neural networks and deep learning.
• Activation functions, layers, and backpropagation.
• Multilayer perceptrons (MLP).
• Using TensorFlow or PyTorch for basic neural network modeling.
• Neural networks for classification and regression.

Sales Forecasting and Predictive Analytics

• Time series vs regression-based forecasting.
• Handling seasonal and trend-based data.
• Building a sales forecasting model using ML techniques.
• Evaluating forecast accuracy and uncertainty.
• Business interpretation and communication of results.

Unsupervised Learning

• Clustering techniques: k-means, k-medoids, hierarchical clustering, SOMs.
• Dimensionality reduction: PCA, factor analysis, SVD.
• Multidimensional scaling.

Text Mining

• Text preprocessing and tokenization.
• Bag-of-words, stemming, and lemmatization.
• Sentiment analysis and word frequency.
• Visualizing text data with word clouds.

Recommendation Systems

• User-based and item-based collaborative filtering.
• Designing and evaluating recommendation engines.

Association Pattern Mining

• Frequent itemsets and Apriori algorithm.
• Market basket analysis and lift ratio.

Outlier Detection

• Extreme value analysis.
• Distance-based and density-based methods.
• Outlier detection in high-dimensional data.

Machine Learning Case Study

• Understanding the business problem.
• Data preprocessing and feature engineering.
• Model selection and parameter tuning.
• Evaluation and presentation of findings.
• Deployment.

Summary and Next Steps