Machine Learning and Predictive Analytics with Python Training Course

Introduction to Machine Learning

• Machine learning paradigms: supervised versus unsupervised.
• The evolution from statistical learning to modern machine learning.
• The data mining lifecycle: business context, data preparation, modeling, and deployment.
• Selecting appropriate algorithms for specific tasks.
• Managing overfitting and the bias-variance trade-off.

Overview of Python and ML Libraries

• The role of programming languages in machine learning.
• Comparative analysis of R and Python.
• Python fundamentals and an introduction to Jupyter Notebooks.
• Key Python libraries: pandas, NumPy, scikit-learn, matplotlib, and seaborn.

Testing and Evaluating ML Algorithms

• Concepts of generalization, overfitting, and model validation.
• Validation methodologies: holdout sets, cross-validation, and bootstrapping.
• Regression performance metrics: ME, MSE, RMSE, and MAPE.
• Classification metrics: accuracy, confusion matrices, and handling class imbalance.
• Visualizing model performance via profit, ROC, and lift curves.
• Model selection and hyperparameter tuning using grid search.

Data Preparation

• Importing and managing data in Python.
• Exploratory analysis and computing summary statistics.
• Addressing missing values and outliers.
• Data standardization, normalization, and transformation techniques.
• Recoding categorical data and data wrangling with pandas.

Classification Algorithms

• Binary versus multiclass classification problems.
• Logistic regression and discriminant analysis.
• Naïve Bayes and k-nearest neighbors algorithms.
• Decision tree methodologies: CART, Random Forests, Bagging, Boosting, and XGBoost.
• Support Vector Machines (SVMs) and kernel functions.
• Ensemble learning strategies.

Regression and Numerical Prediction

• Least squares estimation and variable selection.
• Regularization techniques: L1 and L2.
• Polynomial regression and non-linear modeling.
• Regression trees and spline models.

Neural Networks

• Fundamentals of neural networks and deep learning.
• Activation functions, network layers, and backpropagation.
• Multilayer Perceptrons (MLP).
• Building basic neural network models using TensorFlow or PyTorch.
• Applying neural networks to classification and regression tasks.

Sales Forecasting and Predictive Analytics

• Time series analysis versus regression-based forecasting.
• Managing seasonal patterns and trends.
• Developing sales forecasting models using machine learning techniques.
• Assessing forecast precision and uncertainty.
• Interpreting and communicating business insights.

Unsupervised Learning

• Clustering methods: k-means, k-medoids, hierarchical clustering, and Self-Organizing Maps (SOMs).
• Dimensionality reduction techniques: PCA, factor analysis, and SVD.
• Multidimensional scaling.

Text Mining

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

Recommendation Systems

• Collaborative filtering: user-based and item-based approaches.
• Design and evaluation of recommendation engines.

Association Pattern Mining

• Identifying frequent itemsets using the Apriori algorithm.
• Market basket analysis and lift ratio calculation.

Outlier Detection

• Analysis of extreme values.
• Distance-based and density-based detection methods.
• Identifying outliers in high-dimensional datasets.

Machine Learning Case Study

• Defining the business problem.
• Data preprocessing and feature engineering.
• Selecting models and tuning parameters.
• Evaluating results and presenting findings.
• Deployment strategies.

Summary and Future Steps