Welcome to my Machine Learning notes

I am Ikram Ali, I specialize in building ML engineering and data science teams from the ground up. My current passion is NLP and MLOps

I approach my career by purposefully building domain knowledge in all the cross-functional disciplines required to deliver successful Data Science projects. This includes Research, Data Engineering, Machine Learning Engineering, Management, as well as dabbling in Agile Program Management and Product Management within other roles. This allows me to lead cross-functional teams and know the pain points of bringing a model from ideation to production. For further information regarding my credentials, please refer to my LinkedIn and Github profiles

• https://www.linkedin.com/in/akkefa/

• https://www.github.com/akkefa

I would like to offer concise definitions and comprehensible explanations of Machine Learning and Deep Learning.

Contents

• ML Notation / Equations

Probability

• What is Probability
• Bayes Theorem
• Random Variables
• Discrete Distributions
• Continuous Distributions
• Joint Distributions
• Covariance and Correlation
• Estimators and Sampling Distributions
• Moments Generating Functions
• Maximum Likelihood Estimation
• Confidence Interval
• Hypothesis Testing

Calculus

• Introduction
• Derivatives and Partial Derivatives

Algebra

• Algebra Introduction

Deep learning

• What is Deep Learning
• Vectors, Matrices, and Tensors
• Loss Functions
• Evaluation Metrics

Mathematics

• Linear Algebra
• Statistics

Algorithms

• Sorting Algorithms
• Graphs Data Structure
• Tree Data Structure
• Shortest Path Algorithms
• Greedy Algorithms

Graph Theory

• What is Graphs Theory
• Graph Neural Networks
• Graph Equations

Learn Pytorch

• Pytorch Fundamental
• Pytorch Workflow
• PyTorch Neural Network Classification

Recommendation System

• Recommendation Systems
• Matrix Factorization

Practice

• Probability solutions
• R Solutions

Indices and tables

• Index

• Module Index

• Search Page