Machine Learning and Predictive Analytics with Python Training Course

Introduction to Machine Learning

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

Overview of Python and ML Libraries

• The role of programming languages in ML
• Comparing R and Python for machine learning
• A rapid introduction to Python and Jupyter Notebooks
• Key Python libraries: pandas, NumPy, scikit-learn, matplotlib, and seaborn

Testing and Evaluation of ML Algorithms

• Generalization, overfitting, and model validation strategies
• Evaluation techniques: holdout, cross-validation, and bootstrapping
• Regression metrics: ME, MSE, RMSE, and MAPE
• Classification metrics: accuracy, confusion matrices, and handling unbalanced classes
• Visualizing model performance: profit curves, ROC curves, and lift curves
• Model selection and tuning via grid search

Data Preparation

• Importing and storing data in Python
• Exploratory analysis and calculating summary statistics
• Managing missing values and outliers
• Standardization, normalization, and data transformation
• Recoding qualitative data and data wrangling using pandas

Classification Algorithms

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

Regression and Numerical Prediction

• Least squares estimation and variable selection
• Regularization techniques: L1 and L2
• Polynomial regression and nonlinear models
• Regression trees and spline methods

Neural Networks

• Introduction to neural networks and deep learning
• Activation functions, layers, and the backpropagation algorithm
• Multilayer perceptrons (MLP)
• Basic neural network modeling with TensorFlow or PyTorch
• Applying neural networks to classification and regression problems

Sales Forecasting and Predictive Analytics

• Time series vs. regression-based forecasting approaches
• Managing seasonal patterns and trend-based data
• Constructing sales forecasting models using ML techniques
• Assessing forecast accuracy and uncertainty
• Interpreting results for business insights and communication

Unsupervised Learning

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

Text Mining

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

Recommendation Systems

• Collaborative filtering: user-based and item-based methods
• Designing and evaluating recommendation engines

Association Pattern Mining

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

Outlier Detection

• Extreme value analysis
• Distance-based and density-based detection methods
• Detecting outliers in high-dimensional datasets

Machine Learning Case Study

• Analyzing and understanding the business problem
• Data preprocessing and feature engineering
• Model selection and parameter tuning
• Evaluating results and presenting findings
• Deployment strategies

Summary and Next Steps