Overview

Machine learning and Artificial Intelligence are buzzwords of the modern day, but it is also important before we dive into this to distinguish between the two. Machine Learning can be seen as the core of what this discipline is; where a machine uses statistics, models and algorithms to adaptively solve problems. Artificial Intelligence can be seen as a layer above in which we include aspects such as speech, vision, and more senses to detect more complex patterns. The following lays out a pathway for ML/AI mastery.

Path to Mastery

Prerequisite: Python

Majority of AI/Machine Learning approaches are predominantly done in Python (Some are in C++ too for more performance critical cases). As a result learning Python should be a primary focus on this path. There are a vast amount of resources online to learn Python, but I recommend thenewboston.

In terms of frameworks there are two primary players in this field and that is Tensorflow and PyTorch. The path I recommend although starts in Tensorflow I believe the skills directly are transferable to PyTorch as really the underlying concepts are what are most important over the framework.

Prerequisites: Mathematics

You don’t need to be an expert in these starting out but these are necessary to understand to make Machine Learning. For math courses I recommend Krista King on Udemy for gaining those foundations:

It is important to note that Udemy frequently runs sales that will make courses significantly cheaper so it’s wise to always wait for a sale before purchasing.

Machine Learning Specialization:

One of the most useful places I feel to start is hands down the Machine Learning Specialization given by Andrew Ng from DeepLearning.ai that can be found through Coursera: Specialization link here.

This gives you the foundational tools to understand and apply Machine Learning algorithms. Which will act as the foundation for more complex concepts such as Deep Learning.

Deep Learning Specialization:

The next step in this path I recommend in this path is the Deep Learning Specialization (also offered by DeepLearning.ai) through Coursera: Specialization link here.

By gaining this knowledge in Deep Learning you have a much more formidable position to be considered for AI Engineering. Many new use cases for AI also become available at your disposal through this specialization and therefore you can branch out into a variety of industries.

Deeplearning.ai & Branching Out

Once the foundations are set with those two specializations there are many different directions you can go. Here are some recommended paths:

Deeplearning.ai

Deeplearning.ai offers a multitude of courses from various different sources you can utilize to develop a more specialized skill set.

Tensorflow Specialization

This course series reinforces what you learn in Deep Learning and is more hands on with Tensorflow which is good for reinforcing what you have previous learned as well as applying everything thoroughly.

Kaggle

Kaggle is an excellent resource for learning Data Science as well as putting your skills to the test. You can do various things here from publishing/finding datasets, learning more concepts, discovering models, doing challenges, competing in competitions (some with cash prizes too.) This is a great platform to contribute to and get your name out there.

Alternate Routes

Here are some other resources that can be utilized to accelerate your career in this field

IBM AI Engineer Specialization

While the other Deep Learning Path focused on Tensorflow, this path focuses more on PyTorch which is an alternative to Tensorflow. Note this is a course that jumps right into Deep Learning so if you want a foundation of understanding the Machine Learning Specialization should still be done first. A benefit of this specialization as well is that it is an IBM Certification.

Udemy AI A-Z Courses

These are some extensive additional courses that can be utilized as well,

Machine Learning A-Z
- Provides learning for R
- Extensive Machine Learning coverage
Deep Learning A-Z
- Covers several Deep Learning subjects
- Short course (23 hours)
AI + LLM A-Z
- Offers resources for teaching LLM Implementation
- Short course (15 hours)