The MLE curriculum is built thinking about the skills needed to get the initial interview, but also to pass and succeed in the job. We will analyze different companies from MANGA to start-up and even sports team, we will learn the requirements and how to match a job description and being confident in the skill set.
All content is taken from different resources that has proven results for a lot of people from landing dream job to succesfull entrepeneurs.
The curriculum is structured in three phases in which you will learn the skills needed, we focus similar skill at the same time, the amount of hours to commit depends on your needs, but overall it required at least 2 hours at day.
There are many educators that provide amazing principles on how to learn in many different ways, here I compile a few principles that are the core principles of this curriculum, go to the link to find the most relevant information.
Overall, this principles apply to the design of the phases, the schedule and the excercises.
- Data enginnering & SQL
- Python
- Data structures and algorithms
- Deep learning for coders
Let's evaluate this calender for the first phase
| Week | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday |
|---|---|---|---|---|---|---|---|
| 1 | Data engineering | SQL | Data structures and algorithms | Deep learning for coders | Data structures and algorithms | Data engineering |
This first phase is intended to provided the building blocks of the material you need, and also the learning principles, data structures and algorithms is going at every phase, no matter how good you become, to get a job you need to pass the interviews, so it is required to have an intensive knowledge, python skills will also be a critical feature for all the curriculum, if you don’t understand python code is difficult to communicate with other developer.
We'll be using the core principles of learning how to learn, for this we use the pomodoro technique, available as a free app called pomofocus, the principle is taking 25 minutes of intense work removing any distraction and 5 minutes off, this help you to stay focus and have some tracking metric system that we incorporate into our leaderboard.
We will be using the book Foundamental of data engineering by Joe Reis, is one of the best book to learn it and a course about data engineering.
I like the SQL skill path of dataquest since provides a hands-on experience in how to build this queries. We can also work with CS50 SQL to have a free resource at hand.
We will use the neetcode course, and the CS50 only from week 2 to 5.
We need to understand our language for several reasons, first to work smoothly through python features, being able to read and understand code, and also explain and share good quality of code. For this we will be using CS50's Introduction to Programming with Python (harvard.edu), also python for coding interviews Problems (neetcode.io) and Fluent Python, 2nd Edition (oreilly.com)
Practical Deep Learning for Coders - Practical Deep Learning (fast.ai) will help you to stay on track, building top performing ML models from the initial phase, we will spent time evaluating the lectures, practical exercise and projects. Since this is the big why are we here, everything else helps to this purpose.
As you might suspect this is a wide field with a lot of different skill needed to be succesfull, there are some skills that you'll grab as you take the courses but without a specific material dedicated to work. Might suggestion if and when you have some extra time use some minutes a day to work and understand:
All of them are free interactive enviroment fun to work on and easy to follow, which will give you an edge when you work on production software
- Machine learning specialization
- Designing Machine learning systems
- Machine learning in production
- Data structures and algorithm
- Python
The second phase is designed to provide a core foundation on how to create machine learning system, from understanding the underlying principles to work in machine learning things. You'll be already exposed to machine learning since the deep learning for coders help you to build models from the begining and even deploy them, but this phase is intended to provide a more in-depth knowledge on how to build and deploy machine learning models. Also, we will be using frameworks that are still core requirements in many companies, like scikit-learn, tensorflow and pytorch, and go a bit beyond the basics, you'll be aready ahead to most people taking this course because you already built something and now you want to gain more knowledge. As you probably note, the data structures and algorithms will be keep a core component of your learning, as the main goal is to get a job, and you need to pass the interviews.
We will be using the Machine Learning Specialization (coursera.org) by Andrew Ng, this is a great course to understand the underlying principles of machine learning, and also to have a good understanding of the math behind it.
We will be using the Designing Machine Learning Systems (oreilly.com) by Chip Huyen, this is a great book to understand how to design machine learning systems, and also to have a good understanding of the best practices.
We will be using the coursera specialization about machine learning in production, this is a great course to understand how to deploy machine learning models, and also to have a good understanding of the best practices. It also provides the stack that Andrew Ng uses in his company, so it is a great course to understand how to work in a real-world scenario.
We will be using the same resources than the first phase, but taking more problems and more time to understand the underlying principles. This is a critical component since it will help you to build better understanding and also it will help you to prepare for interviews.
Let's evaluate this calender for the second phase
| Week | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday |
|---|---|---|---|---|---|---|---|
| 1 | Machine learning specialization | Designing Machine learning systems | Machine learning in production | Data structures and algorithms | Python | Machine learning specialization | Machine learning in production |