Machine Learning for Data Science with Python

• Basic understanding of statistics and linear algebra
• Familiarity with data analysis or business intelligence concepts
• Some exposure to programming (preferably Python or R) is recommended
• Interest in learning applied machine learning for data-driven projects

Audience

• Data analysts and scientists
• Statisticians and research professionals
• Developers and IT professionals exploring machine learning tools
• Anyone involved in data science or predictive analytics projects

Machine Learning is a foundational technology in modern data science and artificial intelligence, enabling computers to learn from data and make predictive decisions.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

By the end of this course, participants will be able to:

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Machine Learning Introduction

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

Python and ML Libraries Overview

• Why use programming languages for 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

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