/thematrixML

Learning machine learning through group study

Learning path

Learning path are the actual Matrix Degree program

Beginners Plan 👶

Intermediate Plan 👦

Advanced Plan 👨

Who can learn this

Anyone who is really interested to become a data scientist or machine learning engineer. Check the Reality for more information 😊

Reality

  • People who understood concepts in Mathematics
    • Probability (Bayes theorem, Dice problems)
    • Statistics (Mean, Median, Mode, Distribution)
    • Linear Algebra (Vectors, Matrix)
  • People who have experience in Database & Visualization of Data
  • People who live to do analytics on everything they think and want to automate stuff in real life 😅
  • People who are passionate to experience new things
  • People who want to go beyond themselves (Tamil: riskelam engaluku rusk sapdamathiri 😂)
  • People who have lot of patience and determination for atleast 1 to 1 and half years to attain productive level skills

Is it preffered for a career change?

  • NO is my ideal answer. But its all depends on your will power
  • If you have a stable job and want to enhance your skill set in a more free way (around next 2 years), YES, this course is for you
  • I would recommend more easier path first to change your career and then pursue this, since this is become more and more complicated once we enter into hardcore mathematical world
  • Other options I would recommend are,
    • Front end development
    • Backend development
    • Mobile development
    • UI/UX designer

Where can we get free curriculum / study plan for other technologies like mobile development etc.?

  • We are working on it. Will update very soon 🙏

Hardware Requirement

  • Machine with any OS (preferably Ubuntu or Mac)
  • Atleast 4GB RAM

Courses

Courses are subjects to a Learning Path

Beginner courses

  • Introduction to python (material) or Programming Foundations with Python (udacity)
  • Introduction to computer science (udacity)
  • Intro to Descriptive Statistics (udacity)
  • Intro to Inferential Statistics (udacity)

Intermediate courses

  • Linear Algebra (udacity)
  • Data structures & Algorithms (material)
  • Introduction to Data Science (udacity)
  • Introduction to Machine Learning (udacity)

Advanced courses

  • Machine Learning (coursera)
  • Reinforcement Learning
  • Deep Learning (udacity)

Free resources

  • Udacity
  • Coursera (Machine Learning by Andrew Ng)
  • Khan Academy (Youtube Videos)
  • Data camp free resources - link