Machine Learning with Data Science
Artificial Intelligence (AI)
COURSE OVERVIEW
This intensive six-day training program is designed to provide participants with a strong foundation in both Data Science and Machine Learning. The course covers the complete data science workflow, from data preprocessing and statistical analysis to building, evaluating, and optimizing machine learning models. Participants will gain hands-on experience working with real-world datasets and implementing supervised and unsupervised learning techniques to solve practical business problems.
By the end of the course, learners will be able to design and deploy end-to-end machine learning solutions using data-driven approaches.
COURSE OBJECTIVES
By the end of this course, participants will be able to:
- Understand the end-to-end data science and machine learning lifecycle.
- Perform data preprocessing, cleaning, and exploratory data analysis.
- Apply statistical concepts in machine learning contexts.
- Build supervised learning models for regression and classification problems.
- Implement unsupervised learning techniques such as clustering and dimensionality reduction.
- Evaluate and optimize machine learning model performance.
- Understand ensemble learning and advanced modeling techniques.
- Develop and present a complete machine learning capstone project.
Duration: 6 Days / 48 Hours
Delivery Method: Classroom-based, Virtual Instructor Led Training
COURSE OUTLINE
Day 1: Data Science Foundations
Focus: Understanding the data science workflow
- Introduction to Data Science and Machine Learning
- Data science lifecycle (problem definition to deployment)
- Data collection and data types
- Data preprocessing techniques (cleaning, handling missing values)
- Exploratory Data Analysis (EDA)
- Data visualization for insights
- Hands-on: Performing EDA on a real-world dataset
Day 2: Statistics for Machine Learning
Focus: Building statistical foundations for ML
- Introduction to probability theory
- Random variables and distributions
- Descriptive vs inferential statistics
- Hypothesis testing concepts
- Confidence intervals and significance testing
- Correlation and covariance analysis
- Hands-on: Applying statistical methods using Python
Day 3: Supervised Learning
Focus: Learning from labeled data
- Introduction to supervised learning
- Regression techniques (Linear, Multiple Regression)
- Classification techniques (Logistic Regression, Decision Trees, KNN)
- Train-test split and cross-validation
- Model evaluation metrics (accuracy, precision, recall, F1-score)
- Bias-variance tradeoff
- Hands-on: Building supervised learning models using Scikit-learn
Day 4: Unsupervised Learning
Focus: Discovering hidden patterns in data
- Introduction to unsupervised learning
- Clustering techniques (K-Means, Hierarchical clustering)
- Dimensionality reduction (PCA)
- Pattern recognition in datasets
- Anomaly detection basics
- Use cases of unsupervised learning in business
- Hands-on: Clustering and PCA implementation
Day 5: Advanced Machine Learning
Focus: Improving model performance and deployment readiness
- Introduction to ensemble learning (Bagging, Boosting, Random Forest)
- Gradient Boosting Machines (XGBoost overview)
- Feature engineering and feature selection
- Hyperparameter tuning techniques
- Model optimization strategies
- Introduction to model deployment concepts
- Hands-on: Improving model accuracy using ensemble methods
Day 6: Capstone Project
Focus: End-to-end machine learning implementation
- Defining a real-world machine learning problem
- Data preparation and feature engineering
- Model selection and training
- Model evaluation and optimization
- Interpretation of results and insights
- Basic deployment workflow overview
- Final project presentation and assessment
- Feedback and learning review session
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