Learning to Work with Learning Machines - Introduction to Machine Learning Bootcamp

Overview

Dive into machine learning fundamentals with step-by-step guidance on problem-solving techniques, from basic concepts to hands-on exercises using Mathematica, enabling you to frame and solve ML problems effectively.

Syllabus

• Introduction to Machine Learning
Overview of Machine Learning and Its Applications
Key Terminology and Concepts
• Understanding and Preparing Data
Data Types and Sources
Data Cleaning and Preprocessing Techniques
Data Visualization and Exploration
• Fundamental Machine Learning Techniques
Supervised Learning: Regression and Classification
Unsupervised Learning: Clustering and Dimensionality Reduction
• Working with Mathematica
Introduction to Mathematica for Machine Learning
Using Mathematica’s Machine Learning Capabilities
• Developing Regression Models
Linear Regression
Evaluation Metrics (MSE, RMSE)
Hands-on Exercises with Mathematica
• Building Classification Models
Logistic Regression
Decision Trees and Random Forests
Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
Hands-on Exercises with Mathematica
• Implementing Clustering Algorithms
K-Means Clustering
Hierarchical Clustering
Evaluating Clustering Results
Hands-on Exercises with Mathematica
• Dimensionality Reduction Techniques
Principal Component Analysis (PCA)
Singular Value Decomposition (SVD)
Hands-on Exercises with Mathematica
• Model Evaluation and Validation
Cross-Validation Techniques
Overfitting and Underfitting
Model Selection and Hyperparameter Tuning
• Practical Applications and Case Studies
Real-world Machine Learning Case Studies
Problem-solving Workshops
• Course Review and Future Directions
Recap of Key Concepts
Introduction to Advanced Topics in Machine Learning
Resources for Continued Learning

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Subjects

Data Science