A recipe to get a job in machine learning:
• Get foundational knowledge
• Choose an area to specialize
• Start solving problems
• Write about your solutions
• (Networking is a plus)
• Start applying to job postings
Let's talk about getting foundational knowledge. ↓
• Get foundational knowledge
• Choose an area to specialize
• Start solving problems
• Write about your solutions
• (Networking is a plus)
• Start applying to job postings
Let's talk about getting foundational knowledge. ↓
These are my most basic recommendations:
• Introduction to Python Programming (Udacity)
• Machine Learning Crash Course (Google)
• Machine Learning (Coursera)
If nothing else, these 3 courses will give you most of what you need.
↓
• Introduction to Python Programming (Udacity)
• Machine Learning Crash Course (Google)
• Machine Learning (Coursera)
If nothing else, these 3 courses will give you most of what you need.
↓
If you don't have a lot of experience with Python, better to spend more time with it:
• Programming with Python (Brilliant)
• The Official Python Tutorial
• Book: Python Crash Course
Start solving problems as soon as you can.
Tutorial → Problem → Repeat.
↓
• Programming with Python (Brilliant)
• The Official Python Tutorial
• Book: Python Crash Course
Start solving problems as soon as you can.
Tutorial → Problem → Repeat.
↓
A lot of people talk about Math in machine learning.
Here is a rough distribution of how I use the math in my day-to-day job:
• 80% — Statistics & Probabilities
• 19% — Linear algebra
• 1% — Calculus
Keep this distribution in mind when studying every topic.
↓
Here is a rough distribution of how I use the math in my day-to-day job:
• 80% — Statistics & Probabilities
• 19% — Linear algebra
• 1% — Calculus
Keep this distribution in mind when studying every topic.
↓
My recommendation to start with machine learning math:
• Statistics 110: Probability — Harvard University
• Essence of Linear Algebra — 3Blue1Brown
• Multivariate Calculus — Coursera
Links and 3 additional free resources here: svpino.medium.com/are-you-ready-…
↓
• Statistics 110: Probability — Harvard University
• Essence of Linear Algebra — 3Blue1Brown
• Multivariate Calculus — Coursera
Links and 3 additional free resources here: svpino.medium.com/are-you-ready-…
↓
Wanna more machine learning content?
These 5 are from Coursera:
1. Machine Learning
2. Deep Learning Specialization
3. TensorFlow Developer Professional Certificate
4. TensorFlow: Advanced Techniques
5. Introduction to Machine Learning In Production
Take them in order.
↓
These 5 are from Coursera:
1. Machine Learning
2. Deep Learning Specialization
3. TensorFlow Developer Professional Certificate
4. TensorFlow: Advanced Techniques
5. Introduction to Machine Learning In Production
Take them in order.
↓
You can also take one of these free classes from top universities:
• MIT 6.S191 Introduction to Deep Learning
• DS-GA 1008 Deep Learning
• UC Berkeley Full Stack Deep Learning
• UC Berkeley CS 182 Deep Learning
• Cornell Tech CS 5787 Applied Machine Learning
↓
• MIT 6.S191 Introduction to Deep Learning
• DS-GA 1008 Deep Learning
• UC Berkeley Full Stack Deep Learning
• UC Berkeley CS 182 Deep Learning
• Cornell Tech CS 5787 Applied Machine Learning
↓
Finally, you will get a lot of benefit from an "Algorithms Fundamentals" course:
• Learn to write pseudocode
• Basic data structures
• Sorting and Searching
• Graphs and Trees
• Complexity analysis
• Approximation algorithms
It will be a game changer.
↓
• Learn to write pseudocode
• Basic data structures
• Sorting and Searching
• Graphs and Trees
• Complexity analysis
• Approximation algorithms
It will be a game changer.
↓
If you want more tips and an unfiltered view into the world of practical machine learning, follow me @svpino, and let's do this thing together!
• • •
Missing some Tweet in this thread? You can try to
force a refresh
