Machine Learning

A repository to record my learnings as a Machine learning Engineer

Contents

General

1. Algorithm List
2. Highlights
3. ML Tools
4. Data Science Roadmap
5. Interview Guide
6. QuickBites

Data Engineering

1. What is Data engineering?
2. Types of data

Applied Machine Learning

1. Introduction
2. Before project starts
3. Data collection and preparation
4. Feaure engineering
5. Model training (Traditional)
6. Model training (Deep Learning)
7. Model evaluation
8. Model deployment
9. Model Serving
10. Model monitoring
11. Model maintenance

Deep Learning

1. Timeline
2. Resources
3. What is Neural Network

NLP

1. Timeline
2. Resources
3. Introduction
4. Word Vectors and Word Senses

Data analysis

1. Basic Data analysis using Iris data

Statistics

1. Basic concepts of Measurement
2. Method of least squares
3. Method of central tendency
4. Discrete and Continuous Random Variables
5. Conditional Probability
6. Bayes Theorem
7. Likelihood
8. probability distribution
9. Statistical Learning
10. Inferential Statistics
11. Hypothesis Testing

ISLR_with_python

Introduction to Statistical Learning - Notes and Exercises implemented in Python

1. Statistical learning

References: