After watching many videos and having a solid foundational of a overview of concepts and tool in machine learing i beleive it finally time for me to make a curriculm!

Hmmm…How do i go about this?

Before asessing my weakness, what are my inspiration say is a good pathway? Well after watching Lex Fridman interview with Andrew Ng here! he speicficed the improtance of taking the refined coursework including ML, Deep Learning, Mlops, tensorflow certification courses then focusing once you have taken enough courses then work on projects and reading research papers.

Addtionally, Daniel Bourke, a fellow Australian has created a amazing mindmap (i love mindmaps) for a stack needed to become a ML engineer (here!)[https://whimsical.com/machine-learning-roadmap-2020-CA7f3ykvXpnJ9Az32vYXva]. The main focuses resrouces including fast.ai, CS50’s, Hnads-on Machine Learning by Aurelien Geron.

Focus on the weakness

I beleive (currently?) Machine learning ecosystem can be broken down into the follow skills

  • Mathematics
  • ML algorithms
  • MLops
  • Python skills
  • Cloud Services
  • SQL database

Since i have a currenlty pursuing a Mathhematics and Marketing degree, the main focus should be builing later half. Althought i have expericnes with Cloud Services including AWS and SQL database i will significnatly have to improve in this area. Addtionally, my Python skills a desent but thtere is a way to go

What i have currently done?

Well currently i have completed/doing:

  • CS29 by Andrew Ng
  • Hands-On Machine Learning with Scikit-Learn and Tensorflow
  • The Hundred-page Machine Learning Book by Andriy Burkov
  • Deep learning Specialisation by Andrew Ng

The Roadmap With Certifications

  • Programming
    • “Python 3 Object-Oriented Programming” by Dusty Phillips
    • “Fluent Python” Book by Luciano Ramalho
    • “Algorithms” Princeton University
    • “Coding Interview University” (github)[https://github.com/jwasham/coding-interview-university]
  • ML algorithms
    • Deep Learning for Coders with fastai & PyTorch
    • Introduction to Machine Learning with Python: A Guide for Data Scientists
    • Neural Networks : Zero to hero by Andrej Karpathy
    • Recipe for training neural networks by Andrej Karpathy
    • Papers with Code : Most popular and Recent machine learning papers
  • MLops
    • Desigining Machine Learing Systems by Chip Huyen
    • Stanford’s CS 329S: Machine Learning Systems Design by Chip Huyen
    • Coursera’s MLOps Specialization by DeepLearning.AI
    • Full Stack Deep Learning
    • fast.ai
  • Cloud
    • Microsoft Certified: Azure Fundamentals
  • SQL
    • LeetCode SQL
    • SQL for Data Analysis Advanced Techniques for Transforming Data into Insights
    • SQL Tutorial for Beginners (and Technical Interview Questions Solved) by freeCodeCamp.org

Structure

Currently since unviersity off i have a little time to concurrently complete 2 courses at the same time, the plan is i want to read for projects i want to have a project in mind work on whilst reading/watching resources. The currentl systtemiatic plan is;

  1. Deep learning Specialisation by Andrew Ng + Stanford’s CS 329S: Machine Learning Systems Design by Chip Huyen
  2. Neural Networks : Zero to hero by Andrej Karpathy + Recipe for training neural networks by Andrej Karpathy
  3. “Algorithms” Princeton University + “Fluent Python” Book by Luciano Ramalho
  4. Full Stack Deep Learning + fast.ai
  5. Microsoft Certified: Azure Fundamentals + SQL Tutorial for Beginners (and Technical Interview Questions Solved) by freeCodeCamp.org