60 days of udacity

I watched the rest of lesson 3 slowly to understand the concept. Day 39: fast.ai Lesson 3 - Performance, validation and model interpretation revisited II

I watched again slowly the lesson 3 for the first half of the lesson.

I rewatched the lesson 2 to gain better understanding of random forest.

I would like to revisited the lesson 1 since I am little bit lost now.

Today I wtched the other half of the Lesson 7 of Introduction to the machine learning for coders - RF from scratch and gradient descent.

Today I watched half of the video lesson 7 Introduction to machine learning for coders - RF from scratch and gradient descent.

Today, I watched the other half of the lesson 6 Data products And Live coding

Today, I watched half of the lesson 6 Data products And Live coding.

I watched the other half of the lesson on Lesson 5 Introduction to machine learning for coders about Extrapolation and RF from scratch.

I watched half of the lesson on Lesson 5 Introduction to machine learning for coders about Extrapolation and RF from scratch.

Introduction to Machine Learning for Coders Lesson 4 - Feature importance, tree interpreter .the rest from yesterday.

This lesson is 1:40 long, so I decided to divide into 2 parts. Half today and half tomorrow. Introduction to Machine Learning for Coders Lesson 4 - Feature importance, tree interpreter .

In the middle of my busy works, preparing for new jobs and finishing current one, I want to understand once more about the basic of this course. First concept is Privacy. Hence I rewatched lesson 3 Introducing Differential Privacy. Plan to attend  #sg_study_jahm  virtual meeting

Yesterday, I chose to a have one break day due to my busy days to prepare to start my new job in the other town. In addition, I have finished all the lessons in this course. Time to revise the study again. Today, I revisited the lessons on Differential privacy for deep learning in lesson 6.

I watched lesson 9 Encrypted deep learning concepts 4 to 9. Finish the course. I will revise some material again starting tomorrow.

Watching lesson 9 encrpyted deep learning concepts 1-3.

I am still catcing up with my study in this course after a week of jobs interviews and orientations. But still, I want to spare some times for study this course, although only watching lesson 8 Securing federated learning.

Just comeback to myhome town after 7 hours train journey, my activity is only watching lesson 3 fast.ai Introduction to machine learning - Performance validation and model interpretation.

Since I got a job interview almost the whole day, my activity is only watching lesson 2 fast.ai Introduction to machine learning - Random forest deep dive

I am trying to watch lesson 1 of fast.ai Introduction to Machine Learning for Coders.  http://course18.fast.ai/lessonsml1/lesson1.html My note on todo: 1. Installing jupyter. I want to do it in my own computer later on. Create some instructions if possible. 2. Data Science vs Software Engineering....I am looking at Data Science as more like using excel to do works while Software Engineering is creating 'excel' like application to do the work. Data Science more to prototyping while Software Engineering is a producing a finish product. 3. Introduction to random forest. Another article to read,  https://towardsdatascience.com/random-forest-3a55c3aca46d