Introduction

Welcome to the "Math Meets Machine learning" blog series. This series are designed to provide a clear and structured path for beginners seeking to understand the fundamentals of machine learning (ML). Machine learning is a rapidly growing field that combines computer science, statistics, and domain expertise to enable computers to learn from data and make decisions.

This series will cover the essential concepts and mathematical foundations necessary to build a solid understanding of machine learning. Whether you are a student, a developer, or a data enthusiast, this roadmap will guide you through the topics systematically to prepare you for advanced learning and practical applications.

Machine Learning Roadmap

1. Basics of Machine Learning

What is Machine Learning?
Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
Key Concepts: Dataset, Features, Labels, Training, Testing, Validation
Understanding Overfitting and Underfitting
Evaluation Metrics: Accuracy, Precision, Recall, F1 Score

2. Mathematical Foundations for Machine Learning

Basic Statistics: Mean, Median, Mode, Variance, Standard Deviation
Probability Theory: Conditional Probability, Bayes Theorem
Linear Algebra: Vectors, Matrices, Matrix Multiplication
Calculus: Derivatives and Gradients
Optimization: Gradient Descent and Variants

3. Core Machine Learning Algorithms

Linear Regression
Logistic Regression
Decision Trees
K-Nearest Neighbors (KNN)
Support Vector Machines (SVM)
Naive Bayes Classifier
Ensemble Methods: Random Forest, Gradient Boosting

4. Data Preprocessing and Feature Engineering

Data Cleaning and Handling Missing Values
Feature Scaling: Normalization and Standardization
Feature Selection Techniques
Dimensionality Reduction: PCA, t-SNE

5. Model Evaluation and Validation

Cross-Validation Techniques
Confusion Matrix
ROC Curve and AUC
Hyperparameter Tuning: Grid Search, Random Search

6. Introduction to Neural Networks and Deep Learning

Perceptron and Multi-Layer Perceptron
Activation Functions
Backpropagation Algorithm
Introduction to Convolutional Neural Networks (CNN)
Introduction to Recurrent Neural Networks (RNN)

7. Practical Machine Learning Tools and Libraries

Python Programming Basics for ML
Introduction to NumPy, Pandas, and Matplotlib
Scikit-learn for Machine Learning Algorithms
TensorFlow and PyTorch for Deep Learning

8. Advanced Topics and Future Learning Paths

Natural Language Processing (NLP)
Computer Vision
Reinforcement Learning
Unsupervised Learning: Clustering, Anomaly Detection
Model Deployment and Monitoring
Introduction to MLOps: CI/CD for ML, Docker, Data Version Control (DVC)

Outro

This roadmap provides a comprehensive guide to mastering machine learning from the ground up. Each topic will be explored in detail in the blog series, with practical examples, mathematical explanations, and real-world applications to ensure a thorough understanding.

By following this series, you will be equipped to pursue advanced studies in machine learning, contribute to data-driven projects, and effectively apply ML techniques in various domains.

Introduction to Machine Learning: A Roadmap for Beginners

Table of contents