Machine Learning and Predictive Analytics with Python Training Course

Introduction to Machine Learning

• Types of machine learning – supervised versus unsupervised
• Transitioning from statistical learning to machine learning
• The data mining workflow: business comprehension, data preparation, modelling, and deployment
• Selecting the appropriate algorithm for the task
• Overfitting and the bias-variance tradeoff

Overview of Python and ML Libraries

• Rationale for using programming languages in ML
• Comparing 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 versus 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 modelling
• Neural networks for classification and regression

Sales Forecasting and Predictive Analytics

• Time series versus 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