Algorithm Alchemy: Unlocking the Secrets of Machine Learning

Overview

In today's data-driven world, Machine Learning (ML) is at the forefront of technological innovation, powering applications from personalized recommendations to advanced medical diagnostics. This comprehensive course is designed to equip you with a strong foundation in Machine Learning algorithms and their real-world applications.

Whether you're a beginner or someone with some prior exposure to ML, this course will guide you step-by-step through the essential concepts and practical techniques needed to excel in this field.

Syllabus

• Introduction to Machine Learning
Definition and Importance of Machine Learning
Overview of Machine Learning Applications
• Fundamentals of Machine Learning
Key Concepts and Terminology
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Setting Up Your Environment
Introduction to Python and Jupyter Notebooks
Key Libraries: NumPy, Pandas, Matplotlib, Scikit-Learn
• Data Preprocessing
Data Cleaning and Preparation
Feature Scaling and Normalization
Handling Missing Values
• Supervised Learning Algorithms
Linear Regression
Logistic Regression
Decision Trees and Random Forests
Support Vector Machines
K-Nearest Neighbors
• Model Evaluation Techniques
Train/Test Split and Cross-Validation
Evaluation Metrics: Accuracy, Precision, Recall, F1-Score
• Unsupervised Learning Algorithms
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
• Introduction to Neural Networks
Basics of Neural Networks
Introduction to Deep Learning and Neural Network Structures
• Practical Techniques in Machine Learning
Overfitting and Underfitting
Hyperparameter Tuning
Model Selection and Validation
• Real-World Applications of Machine Learning
Recommendation Systems
Natural Language Processing Basics
Image Classification
• Cutting-Edge Trends in Machine Learning
Transfer Learning
Automated Machine Learning (AutoML)
• Final Project
Identifying a Problem and Choosing the Right Algorithm
Building, Evaluating, and Presenting a Machine Learning Solution
• Course Review and Next Steps
Recap of Key Concepts
Further Learning Resources and Career Pathways in AI and Machine Learning

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Taught by

Vivian Aranha

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Subjects

Computer Science