I would like to learn more about machine learning and build something interesting in my free time. Problem is I am so done with the online courses, because those classes requires you to take the bundle of courses and to learn something I might have known before. As a result, I looked up the discussions from reddit and found a curriculum here The goal of the curriculum is to learn how to build and deploy a ML application in the end. Since most of them are reading materials, I could pick up what I want and tailer those resource into my own learning path.

My goal:

• I would like to learn more about machine learning, deep learning, and then GenAI.
• Other than building projects in the journey, I would like to deploy them on the web.
• Also, I am interested in visualize the data and explain what the model tells us.

My background:

• I’ve learned general AI and machine learning concepts in grad school, but I did not have more in-depth knowledges in deep learning.
• When I was taking machine learning in signal processing, I’ve learned PCA, ICA to reduce dimension. So, I’ll skip them.

Routine:

The schedule each week would be as followed. Day 1 - 4: Reading material, lesson and exercise implementation Day 5 - 6: Assignment implementation Day 7: Document, share and self-reflection on my plan/goal.

Adjusted resource:

For month 1: Machine Learning

Since I’ve gained knowledges in python and mathematics, I will start from week 3 from LearnML and extend my learning from kaggle learn

week 1 - 4: Data visualization

Some topics that interst me are:

• Intermediate Machine Learning: est. 4 hours
• Data Visualization: est. 1 hours
  • I just want to review different method to explain the data. So, I only need to read the tutorial in summarized tutorial
• Feature Engineering: est. 5 hours
• Machine Learning Explainability: est. 4 hours

For month 2: Deep Learning

The overlapped materials people talks about are:

• fastAI’s course Practical Deep Learning
• a text book Dive into Deep Learning. FastAI’s course looks more interesting to me, so I will explore FastAI’s course and plan to learn more from this material.

Week 1: Neural Networks

• Practical Deep Learning(Part 1): est. 14 hours
• Dive into Deep Learning(Chapter 3. - Chapter 9.): est. 10 hours
• Assignment: on github.

Week 2: Transformers(HuggingFace)

• Follow the NLP course in hugginface: est 10 hours lesson and 20+ hours implementation
• Assignment: on github.

Week 3: GenAI - Diffusion(FastAI)

• Follow all courses in Part 2: 15 hours lesson and 20+ hours implementation
• Assignment: on github.

Week 4: Deep Reinforcement Learning(Simonini Thomas)

• Follow syllabus Deep Reinforcement Learning Course(8 Units): est. 16 hours lesson and 20+ hours implementation
• Assignment: on github.

For month 3: GenAI/MLOps (est. TBD)

• Kaggle 5-Day Gen AI Intensive Course

reference resources:

• LearnML

Bao-Jhih Shao

A software engineer writing something to keep the memory.

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