Creating my own machine learning curriculum
By: Manoj Uppala
Tags: Getting Started, Data Science, Online Courses, Learning Journey, Machine Learning

Creating my own machine learning curriculum

Unless you’re somewhere from out of this planet you definitely would have heard the word Machine learning. The definition of the term sounds more sexier: “Machines learning to learn and improve in a specific task without any explicit programming”. Isn’t that cool, yes it is. That is why i want to get started and dive deep into ML. Manoj Uppala Aug 16, 2020·3 min read

why create my own curriculum? 😕

Yes there are 100’s of machine learning courses, some free ones too(which are really good). I am really messed up which one to choose and don’t want to end up at the initial point after several months. Just search for machine learning in Google or YouTube, thousand’s of search results and videos will popup. Which one to follow? There are really good courses out there no offence, But I've been fluctuating from one course to other. In the last couple of months i have tasted 5 courses on Data science and ML, none of them successfully completed . It is not that i didn’t like the course or didn’t learn anything. I’ve eventually lost interest(as i wasn’t motivated to take a course only for certificates, but to acquire skills) in each one of those courses after starting them. Then i came across a stat that 80% of the people dropout from online courses before completing them.

So i wanted to be in that 20%, therefore create my own curriculum according to what i know little about ML and it’s prerequisites.

So what is my game plan? 📃

I watched this video of Daniel Bourke on youtube. He is a self taught machine learning engineer. I am pretty much motivated by this video.Daniel clearly explains each step in-order to gain the required skills as a beginner. And i found some other videos on getting started with machine learning and integrated them together into my own course. Below are the steps i’m going to follow through out this learning journey :

Step 1: Learning python, data science tools and machine learning concepts.

sources: Learn python -documentation, data science tools- jupyter notebooks, machine learning concepts- Elements of AI

Step 2: Learn data manipulation, analysis and visualization with pandas, NumPy, Matplotlib and math concepts required for machine learning.

sources: Learn pandas, numpy and matplotlib -Python for data analysis book, math concepts -3blue1brown youtube channel and mathematics for machine learning

Step 3: Learning machine learning with SciKit-learn and get started with Kaggle.

sources: Data School scikit-learn series, fastai machine learning course.

Step 4: Learn deep learning and neural networks.

sources: Andrew Ng’s deep learning specialization on Coursera, fastai deep learning curriculum

Step 5: Explore books and Extra curriculum

source: Not decide yet.

A little about my background📖

I am a 3rd year computer science student at lovely professional university. yes i do have a programming background and a 1 year experience in python. So i won’t be spending much time on Step 1. but from time to time i’ll be brushing up my python skills with some projects. i’ll have to spend more time on Step 2 even though i have a little experience with pandas, numpy and matplotlib i learnt in my university. To know more about programming experience see my github account.

Finally📍

I am already late so i’m getting started right now. Hope this journey is going to be fruitful. I will stick to this schedule and let you guys know if there are any minor changes in my plan. hope you enjoyed reading this article. i’ll soon be sharing my further journey.