Readme.md

Machine Learning Engineer Roadmap

Machine learning engineer are the one who carry out modelling and deployment of the ML Model. They are the one with very good knowledge of the software and cloud as well as they possess strong programming skills.

Roadmap to become machine learning Engineer inspired by ml-engineer-roadmap

Disclaimer

This roadmap is created for to keep track of the things i should be doing and learning. Some of the things are taken from blogs and tech talks. The resources here i have listed are random and based on my watchlists.

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Roadmap

Software Engineering

(Languages recommended:- Python and C++)

1. Data structures and Algorithms
2. OOP concepts
3. Software design and Architecture
4. Design Patterns
5. Databases and Query Languages
6. Continous Integration
7. Testing
8. Cloud
9. Containerization (Docker or kubernetes)
10. Api development

Maths For Machine Learning

1. Linear Algebra
2. Calculus
3. Probability and Statistics

Machine Learning

1. Supervised Learning
2. Unsupervised Learning
3. Numpy
4. Pandas
5. Sklearn
6. Neural Network
7. Pytorch/Tensorflow (I recommend Pytorch)
8. MLops
9. Complete Projects and Resources at The end
10. Datasets

Software Engineering

Data structures and Algorithms

• Algorithms by Abdul Bari (must)

• Algorithms Lectures (must)

• Easy to Advanced Data Structure using Java

• CS Dojo Data structures and Algorithms (must)

• Dynamic Programming

• Data Structures by Neso Academy

• Data Structures using C++

• Algorithmic Design and Techniques (edx.org)

• Graph Algorithms (edx.org)

• NP-Complete Problems (edx.org)

OOP Concepts

• Python OOP by Corey Schafer

• Python OOP for beginners (must)

• OOP in python (must)

• C++ OOP by derek banas (must)

• C++ Programming All-in-one (beginner to advanced)

• Object Oriented Programming in C++

Software design and Architecture

• Software Design using Python

• A philosophy of Software Design (Talks)

• Software Design in 21st Century by Martin Flower (Talks)

• SOLID using Python

• Be a Better Developer by Using SOLID design principles

Design Patterns

• Design Patterns in Object Oriented Programming (must)

• Design Patterns in Python (Talks)

• Design Patterns in Python

• Advanced Python Programming

Databases and Query Languages

• Redis Crash Course

• Column vs Row Oriented Databases

• 5 use Cases of Redis (Talks)

• Introduction to noSQl database

• Nosql vs Sql

• Learning MYSQL

Continous Integration

• What is continous integration?

• Continous Integration in C++

• Understanding CI/CD

Testing

• Python Testing with pytest: Simple, Rapid, Effective, and Scalable (Book)

• Learn Pytest in 60 minutes

• Pytest: Test Framework

• Python testing 101 with Pytest (must-talks)

• Testing Your code with Pytest (talks)

Cloud

• Introduction to Cloud

• Cloud Computing Full Course

• Aws for Beginners

• What is serverless?

• Introduction to AWS Lambda and Serverless Architecture

• AWS S3 Tutorial

• AWS SageMaker for ML and DL

Containerization

• Docker for Beginners

• This is how docker works! The fun way (Talks)

• Docker Beginner Tutorial

• Docker Tutorials For Beginners

• Simplify all things with Docker-Compose

• What is kubernetes?

• Kubernetes in 5 mins

• Kubernetes beginners Tutorials

Api Development

• What is an api?

• What is api?

• Learn Flask (Only 45 mins video)

• Building a Flask Rest API

• Deploy machine learning model using Flask

• How to deploy machine learning model in Production (Talks)

Maths For Machine Learning

Linear Algebra

• Essence of Linear Algebra by 3Blue1Brown (must)

• Mathematics for Machine Learning (Linear Algebra) (edx course) (must)

• Linear Algebra By Khan Academy (must)

• Matrices

• Mathematics for Machine Learning: Linear Algebra

• Linear Algebra and Optimization for Machine Learning: A Textbook (Book)

Calculus

• Essence of Calculus By 3Blue1Brown

• Calculus Introduction

• Calculus by Professor Leonard

• Calculus full College Course

• Calculus by Khan Academy

Probability and Statistics

• Statistics Fundamentals by StatsQuest (must)

• Statistics by Khan Academy

• Statistics by Professor Leonard

• Statistics full University Course on Data Science

• Probability by Khan Academy

• Probability Basics by 365 Data Science

• Bayesian Statistics Made Simple (Scipy) (Talks)

• Think stats (Book)

• Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python (Book)

• Naked Statistics (Book)

• Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) (Book)

• Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop (Author) (Book)

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) (Book)

Machine Learning

Supervised Learning

• The Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition (Book)

• The Hundred-Page Machine Learning Book (Book)

• Supervised Learning Crash Course

• An Introduction to Linear Regression

• Supervised Learning | Linear Regression

• Simple Linear Regression

Unsupervised Learning

• Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data 1st Edition

• Unsupervised learning explained

• Unsupervised Learning Crash Course

Numpy

• Python NumPy tutorials for Beginners

• NumPy tutorials 2020

• Introduction to NumPy

• Introduction to Numerical Computing with NumPy | SciPy 2019 Tutorial | Alex Chabot-Leclerc (Talks)

Pandas

• Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

• Introduction to Data Processing in Python with Pandas | SciPy 2019 Tutorial | Daniel Chen (Talks)

• Solving real world data science tasks with Python Pandas!

• Python Pandas by Derek Banas

Sklearn

• Data science From scratch using Python (Book)

• Scikit-Learn Course - Machine Learning in Python Tutorial

• Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

• Machine Learning with Scikit-Learn, Part 1 | SciPy 2018 Tutorial | Lemaitre and Grisel (Talks)

• Machine Learning with scikit-learn Part 2 | SciPy 2018 Tutorial | Lemaitre and Grisel

• Machine Learning with Python

Neural Network

• Neural Networks by 3Blue1Brown

• Beginner Intro to Neural Networks

• CS231 winter 2016 (must)

• Make Your Own Neural Network (Book)

• Neural Networks and Deep Learning: A Textbook (Book)

• Deep Learning (Adaptive Computation and Machine Learning series) (Book)

• Neural Network Full Course | Neural Network Tutorial For Beginners | Neural Networks

Pytorch

• Pytorch Complete Beginner Course (must)

• Pytorch Examples (github)

• Pytorch Tutorials Yunjey (github)

• Pytorch Tutorials (github)

• Pytorch Tutorials (must)

• Pytorch for Deep Learning

• Neural Network Programming with Pytorch (must)

• Lecture Pytorch (New York University)

• Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications (Book)

• Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools (Book)

MLops

• What are MLOps and Why Does it Matter?

• MLOps: Overview of Machine Learning Operations on the Cloud AISC

• Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

• MLops Community "Videos Collection"

• MLOps: Machine Learning as an Engineering Discipline (Blog)

Projects and Resources

• Andrew ng Machine Learning Course

• Standford CS229: Machine Learning

• Deep Learning Essentials , Mila University (edx.org)

• Deep Learning 2019 by jermy howard- Fast.ai

• Machine Learning and Deep Learning Fundamentals

• Deep Learning Theories - Public Playlist

• Introduction to deep learning fall 2019 - CMU

• Complete deep learning by krish naik

• Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD (Book)

• MLcourse.ai by Yury Kashnitskiy

• Complete machine learning course by Krish Naik

• Applied machine learning course 2020

• Machine Learning Algorithms

• How to make your first Kaggle submission from scratch! (Titanic Dataset)

• End to end ML pipeline to solve real-world industry problems | Machine Learning

• Building a Movie Recommendation Engine | Machine Learning Projects

• Face Recognition using PCA | Face Recognition Machine Learning

• Machine Learning From Scratch

• An End-to End Data Science Project on California Housing Price Prediction

• Machine Learning Engineering (Book)

• Building Machine Learning Powered Applications: Going from Idea to Product (Book)

Datasets

• AWESOME PUBLIC DATASETS

• 70+ machine Learning Datasets

Wrap up

If you think this roadmap lacks resources or is incomplete , feel free to message me on Linkedin.