Welcome to the first episode of Machine Learning Guide, or MLG, which is a podcast structured as an audio course whose intent is to teach you the high level principles of machine learning and artificial intelligence. In this podcast, I will provide you a bird's eye overview of the fundamental concepts in machine learning.
This includes things like models and algorithms. Both shallow learning models and deep learning models. Shallow learning machine learning models include things like linear and logistic regression, naive bays, and decision trees, which as a machine learning newbie you may not have heard of. But I will also cover deep learning models, which I'm sure you have heard of.
Things like neural networks, convolutional neural networks, and recurrent neural networks. I'll discuss the languages and frameworks that you want to use in machine learning. We'll talk about Python, TensorFlow psych kit, learn PyTorch, these types of topics I'll discuss at a high level the math you need to know to succeed in machine learning.
This includes calculus, statistics and linear algebra, and I will go into all these topics in as much depth as audio allows. Then I will provide you with the resources needed to deep dive any of these topics offline, to master the details that require a visual element, whether it be textbooks or videos.
This podcast is of course, intended for anybody interested in machine learning. I. There tends to be two common subscribers to the podcast. The first is managers and executives. They're interested in knowing just enough machine learning to be dangerous, whether it's to assess what technologies are available to use in their projects or at their company, or maybe they want to intelligently converse with their machine learning and data science employees.
The second are people who want to learn machine learning. Maybe they're considering pivoting from a different field into the machine learning field, machine learning, artificial intelligence and data science. I myself come from a web and mobile app development background and decided that I wanted to become a machine learning engineer and self-taught my self machine learning.
And ended up getting work in the field. I only have a bachelor's in computer science, so that shows you that it is doable to self-teach machine learning effectively enough to land work in the industry. But in order to do that, you have to be very rigorous and diligent in your self-teaching journey.
You're going to want a very structured resource guide, and that is what I'm going to provide to you. Separate from this podcast. It's as important a component of your listening experience to machine learning guide that you visit the resources section of my website on my website, oc deve.com, O-C-D-E-V-E l.com/mlg.
There is a resources tab. Click that tab and you'll get a hierarchically structured list of resources with filters. So you can filter by format, whether it be audio, book, textbook, video course, et cetera, price filters, quality filters, and so on. And this tree structured resource guide will provide you the step by step in sequential order what resources you're going to want to consume separate from this podcast.
To most efficiently manage your learning path through your journey of learning machine learning. I have spent a lot of time and effort on curating this list of resources. I'm subscribed to many machine learning and data science subreddits and RSS feeds. I frequently peruse the syllabus of various courses and the textbooks that they assign to their students.
There are, of course, just very well known machine learning resources out there, like the Andrew ing Coursera course and fast.ai. So this podcast will provide you the overview of the models and the concepts. But if you're serious about learning machine learning, you're gonna want to also spend time on the resources list where I have crafted for you a step-by-step guide to effectively learn machine learning.
Now this resources list is not only for the deep divers, there are also plenty of great resources listed on that page, including other podcasts similar to machine learning guide. Some of my favorite podcasts that I listen to out there. Various podcasts are news and interview based podcasts where they interview experts in the field and those experts discuss latest technologies and inventions and white papers, some other podcasts and audio learning resources that are similar to MLG, whose goal is to teach you machine learning.
Other fields tangential to machine learning, such as math, operations, research, and some more fun topics, more inspirational concepts in machine learning, things like the singularity and consciousness. Can robots be conscious? And what is consciousness? So after you listen to this episode, I recommend visiting oc deve.com/mlg.
There you will find the resources list, which includes other audio supplementary resources that you may want to enjoy alongside this podcast, as well as the deep dive materials that you'll need during your learning journey.
I'm gonna be breathing New Life into the old podcast. I'm gonna be dusting off old episodes, fixing up some ERA subjects, which may be considered missing in the traditional machine learning educational path. Things like decision trees and naive bays. bringing you machine learning applied for free.
Previously machine learning applied was a separate, paid podcast I had, which it discussed the applied things in machine learning, things like technology stacks, programming languages, job hunting, machine learning operations, or ML ops, and all these things. So I'm gonna be merging in these episodes into the main feed,
If I don't have acase study project handy as a talking point for that episode, I'm gonna be using my own personal project, nhi, G-N-O-T-H-I. No. OAN is ancient Greek for no thy self. NHI is an online journal, a personal journal that uses AI to provide resources and insights, things like book recommendations, therapist suggestions.
It generates themes of your journal entries, common recurring patterns. It allows you to ask questions of your journals. It allows you to generate summaries of your journal entries.
Then the project being open source, noia is open source. You can go see the code. You can see how some computer vision algorithm is programmed in Python using TensorFlow in the wild. You can see how you would program a summarization model in the deep natural language processing episodes in the future.
Note as I'm redoing the podcast, you may find some gaps in your pod catcher. It looks like maybe an episode is missing. That's normal. It's because I've taken out older material that it's no longer relevant, and it's important to bear in mind that I discuss resources in my old episodes. At the end of the episode, I discuss them in the podcast episode.
When you get to that point in the episodes, just skip ahead. Because this resources page on oc deval.com is where I dump the resources. Now, this allows me to keep things fresh and updated. As resources change, as newer, better resources become available for some specific topic, then I don't have to go back and edit an episode.
So going forward, as I revise these older episodes, I will no longer be including discussions of the resources themselves. Instead, you need to just go to the website to get the resources. Now with all that meta outta the way, let's dive into the next episode where I define machine learning and artificial intelligence, how they differ from each other compared to data science and so on.
I'll see you in the next episode.