By the time you are reading this, I am done with my PyCon Uganda 2024 presentation. Yey!
Note: For Coursera Paid Courses, you can apply for financial aid. Financial aid allows you to study these courses for free. Find out how to apply for financial aid here or using this YouTube video.
Okay, let's start! This post has only 3 segments, a list of resources from DeepMind, a skill checklist and some advice (learning plan).
DeepMind's resources
Skills Checklist
Before Starting any ML Project
Machine Learning Mastery Guide 📚:
Start Here: Applying the Machine Learning Process
Learning Path
This plan is a learning plan I developed for myself. Do not take it as final but use it to guide yourself in creating your own plan. I created this learning path for my journey to become a machine learning engineer. We may not share the same goal, but if it's anything with ML, I'm sure you will find this path helpful. Remember, learning never stops, use this to help you create a learning path for your whole career.
1. Beginner Stage
Courses
- Mathematics for Machine Learning Specialization
This specialization has three courses that cover the 3 basic math areas in ML. It is available on Coursera and is easy to understand.
- Feature Engineering from Google 🧑🏿💻
Link: (Analytics Vidya and IBM Coursera)
- Machine Learning Specialization (Andrew Ng) 🧑🏿💻
Link: (Coursera)
- Machine Learning Engineering for Production (MLOps) 🧑🏿💻 (Optional)
Link: (Coursera)
Additional skills to elevate yourself(Especially if you want to become a Machine Learning Engineer:
- MLOps Principles and Tools
- Hugging Face
- Docker and Kubernetes
- Weights and Biases
- MLFlow
- DVC.
- Get Career Advice from Professionals in the field.
- Pretraining LLMs (Short courses)📘👨🏾💻 (Optional)
Course : DeepLearning.AI
Generative AI with LLMs:
Course: Coursera
Resources:
- 📚: "Hands-on Machine Learning" by Aurelien Geron
- 📚: "The 100-page ML "
- OneDrive folder for ML Cheatsheet
Advice:
- Focus on understanding fundamental concepts
- Practice implementing basic algorithms
- Start small projects to apply what you're learning
- Don't be afraid to ask questions and seek help
2. Intermediate Stage:
Courses:
- Learn Hadoop and Spark. 🧑🏿💻
- Deep Learning with PyTorch 🧑🏿💻
Link: (Zero to Mastery)
- TensorFlow Developer Professional Certificate Specialization 🧑🏿💻
Link: (Coursera)
- Deep Learning Specialization (Andrew Ng)
Course 1 : Neural Networks and Deep Learning ☑️
Link: (Coursera)
- Data Structures and Alrogithms.
- Get Career Advice from Professionals in the field.
Resources:
- 📚: "Deep Learning with Python" by François Chollet
- DeepLearning.AI AI Notes: https://www.deeplearning.ai/ai-notes/index.html
- Learn Parallel Programming with CUDA
- Data Version Control (DVC)
Advice:
- Start reading research papers in your areas of interest
- Participate in Kaggle competitions
- Build a portfolio of personal projects
- Consider teaching or talking about ML to solidify your knowledge
Now that you are getting into an advanced stage, think about this image below:
3. Advanced Stage:
Courses:
- TensorFlow Advanced Techniques Specialization 🧑🏿💻
Link: (Coursera)
- Generative Adversarial Networks
Link: (Coursera)
- Choose one specialization based on your interests:
- Robotics
Resources:
- 📚: "Introduction to Machine Learning Interviews"
Link: https://huyenchip.com/ml-interviews-📚/
- Fast API for building APIs
- GitHub repositories of implemented research papers
- AWS Machine Learning Specialty certification
Advice:
- Implement research papers to deepen understanding
- Contribute to open-source projects
- Network with professionals in your chosen field
- Consider pursuing a master's degree for deeper specialization
Projects:
Learn some Business Management and Administration to diversify your skillset. This allows you to learn integrate the Business perspective which is important in your growth and applications of ML incase your are in industry.
Additional Resources Throughout The Journey
Online platforms
- Learning LangChain - DeepLearning AI
- Understandind Deep Learning Book
- Deep Mind Resources list (Check LinkedIn)
- Nvidia Lessons, Training and Courses.
Books
- Buy Stat quest Machine Learning Illustrated.
- Buy Deep Learning Illustrated by John Krohn.
- Math for ML (In Library)
AI Roadmaps and Guides:
- TensorFlow Guide: https://www.tensorflow.org/resources/learn-ml
- What's AI Guide: https://aman.ai/
- DeepMind Resource List (check for link)
YouTube Channels:
- 3Blue1Brown for refreshing math concepts: https://youtube.com/@3blue1brown
Certifications
- AWS Machine Learning Specialty
- Google Developers Expert (Machine Learning)
Cloud Platforms:
- AWS Machine Learning: (check for specific link)
- Google Cloud AI Learning Path: (check for specific link)
Remember to regularly check your progress against your learning goals, and don't hesitate to adjust your own plan as needed. Stay curious, keep practicing, and always strive to apply what you learn to real-world problems. Your journey in AI and Machine Learning is exciting and full of potential. Embrace the challenges, celebrate your progress, and keep your faith strong as you work towards your goals.
For those still in University, or recent Graduates: Here is some great career advice from Mark Andreesen
Thank you for reaching this far! Remember, you are the one who creates your own journey. You know the best ways for you to learn and I hope you enjoy learning. I wish you the best in your career. Remember, learning never stops!
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