Machine Learning Fundamentals Training Course

This course equips participants with foundational knowledge and practical skills in machine learning. It focuses on teaching how machines learn from data, identify patterns, and make predictions. Participants will gain hands-on experience with core algorithms, data preparation, model training, and evaluation techniques used in real-world AI applications.

Target Groups

• Computer science and IT students
• Data analysts and aspiring data scientists
• Software developers transitioning into AI/ML
• Engineers and quantitative professionals
• Researchers and academics
• Tech entrepreneurs and innovators
• Anyone interested in artificial intelligence and machine learning

Course Objectives

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

• Understand core concepts of machine learning
• Differentiate between supervised and unsupervised learning
• Prepare and process datasets for modeling
• Train and evaluate machine learning models
• Apply basic ML algorithms to real problems
• Understand model accuracy and performance metrics
• Use Python-based ML libraries (e.g., Scikit-learn basics)
• Identify overfitting and underfitting issues
• Build simple predictive models
• Develop a strong foundation for advanced AI learning

Course Modules

Module 1: Introduction to Machine Learning

• What is machine learning
• AI vs ML vs deep learning
• Real-world applications of ML
• Types of machine learning
• Machine learning workflow

Module 2: Data Understanding and Preparation

• Types of data (structured and unstructured)
• Data collection and cleaning
• Handling missing values
• Feature selection and engineering
• Data normalization and scaling

Module 3: Supervised Learning

• Regression vs classification
• Linear regression basics
• Logistic regression
• Training and testing datasets
• Model evaluation concepts

Module 4: Unsupervised Learning

• Clustering techniques
• K-means clustering
• Hierarchical clustering basics
• Dimensionality reduction
• Pattern recognition

Module 5: Model Evaluation and Metrics

• Accuracy, precision, recall, and F1 score
• Confusion matrix
• Cross-validation techniques
• Bias vs variance tradeoff
• Model performance improvement

Module 6: Machine Learning Algorithms

• Decision trees
• Random forests
• K-nearest neighbors (KNN)
• Support vector machines (SVM) basics
• Algorithm selection principles

Module 7: Introduction to Python for ML

• Python libraries overview (NumPy, Pandas, Scikit-learn)
• Data manipulation techniques
• Building simple ML models
• Visualization basics
• Coding ML workflows

Module 8: Feature Engineering and Optimization

• Feature creation techniques
• Feature selection methods
• Handling categorical data
• Improving model performance
• Reducing overfitting

Module 9: Real-World Machine Learning Workflow

• End-to-end ML pipeline
• Model training and deployment basics
• Handling real datasets
• Common challenges in ML projects
• Introduction to model tuning

Module 10: Capstone Project and Case Studies

• Building a complete machine learning model
• Real-world prediction project (e.g., price prediction, classification task)
• Data preprocessing and model evaluation
• Model comparison and improvement
• Presentation and interpretation of results
• Emerging trends in machine learning, AutoML systems, AI-powered analytics, edge AI applications, and scalable machine learning pipelines in production environments