Machine Learning and Predictive Analytics with Python Training Course

Introduction to Machine Learning

• Distinctions between supervised and unsupervised machine learning types.
• Tracing the evolution from statistical learning to machine learning.
• The data mining lifecycle: business comprehension, data preparation, modeling, and deployment.
• Selecting appropriate algorithms for specific tasks.
• Addressing overfitting and the bias-variance tradeoff.

Overview of Python and ML Libraries

• The role of programming languages in machine learning.
• Comparing R and Python for machine learning tasks.
• A rapid introduction to Python and 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 strategies: holdout sets, 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.
• Conducting exploratory analysis and calculating summary statistics.
• Managing missing values and outliers.
• Applying standardization, normalization, and data transformation.
• Recoding qualitative data and wrangling data with pandas.

Classification Algorithms

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

Regression and Numerical Prediction

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

Neural Networks

• Fundamentals of neural networks and deep learning.
• Activation functions, network layers, and backpropagation.
• Multilayer perceptrons (MLP).
• Basic neural network modeling using TensorFlow or PyTorch.
• Applying neural networks to classification and regression problems.

Sales Forecasting and Predictive Analytics

• Comparing time series and regression-based forecasting.
• Processing seasonal and trend-based data.
• Developing a sales forecasting model with machine learning techniques.
• Assessing forecast accuracy and uncertainty.
• Interpreting and communicating business results.

Unsupervised Learning

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

Text Mining

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

Recommendation Systems

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

Association Pattern Mining

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

Outlier Detection

• Extreme value analysis.
• 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 outcomes and presenting findings.
• Model deployment strategies.

Summary and Future Steps