Python Skills You NEED Before Machine Learning

Key Takeaways

If you want to become a machine learning engineer than you need to master Python. Before machine learning, there's a whole other set of skills within Python that you need to know. And that's why I'm making this video. I'm going to give you a complete road map that breaks down all of the skills that you need to master, the order in which you should master them, and why they're important for machine learning. So, stick around. Okay. So, let's get into our first skill here, which is simply Python fundamentals. Seems obvious, but if you want to get into machine learning, you need to be really good at writing code in Python. That means you need to understand variables, for loops, functions, classes, object-oriented programming, and ideally some of the more advanced features as well. Things like lambdas, decorators, various built-in methods and how those work, as well as context managers, and maybe even things like metaclasses. You don't need to go crazy here, but generally speaking, you should be really comfortable writing code in Python. And before you even think about getting into machine learning, you should build some basic Python projects. Try to build a basic terminal application. Build something like tic-tac-toe, make sure that you can write a few hundred lines of Python code and be comfortable solving problems with the language. This is a big mistake I see a lot of people make. They skip right into the machine learning stuff. And while that's more exciting, it's a lot better to have a strong foundation and know how to code in the language before you go and start trying to train machine learning models. Okay, that's step one. Python fundamentals. I'd recommend you spend at least one or two months here and really make sure this is solid. Now, after you've mastered the Python fundamentals, you need to get into data handling and analysis. Now, as much as everyone wants to reach right for deep learning and go into building, you know, neural networks and LLMs, it's actually really important that you have a solid foundation in dealing with data. That's because most of your job as a machine learning engineer isn't actually working directly with the models. It's really collecting, cleaning, and parsing through massive amounts of data. So in terms of learning that within Python, obviously you need to be good at Python first, which is why we have those fundamental skills. But then you need to start looking at modules like NumPy for example for numerical computing. Pandas for things like data wrangling and loading, map plot, lib, and seabour. This is for visualization and graphing and being able to actually see the trends in your data. And then lastly looking at things like Jupyter notebooks and understanding how to create workflows there and how to interact in that type of environment. Now within each of these modules there's a ton of different topics that you can dive into but generally speaking you should be really comfortable with numpy pandas mapplot lib and seabboard. If you can get those four down you're going to be in really good shape to move into the next step. Now one thing I'll mention at this point is that when you're learning these topics and python specifically you want to be a little bit careful. Research shows that when you just passively watch tutorials or read articles, you only retain about 20% of the information. But with active learning, where you're actually working on projects, coding, and building stuff out as you learn, you can retain 75 to 95% of the stuff that you study. Now, that's why interactive learning is absolutely crucial, especially if you want to master this topic. And honestly, that's why I recommend Data Camp. I've worked with Data Camp for a very long time now, and they have fantastic courses that cover almost everything I'm going to go over in this video. For example, they have the Python data fundamentals course, which I'd recommend that you start with, which covers core Python skills, data manipulation with pandas, visualization with Seboard, and much more like I already talked about. Next, they have the machine learning scientist course in Python. And this covers more advanced skills, things like supervised learning with Scikitlearn, unsupervised learning, clustering, and a lot more items as I scroll through here. Overall, Data Camp is fantastic. I've worked with them for a very long time. And right now, they're offering 25% off either of these courses using my link from the description. Massive thanks to Data Camp for sponsoring this video. Now, let's continue the road map. So the next topic on my list isn't super specific to machine learning, but it's definitely something that's important to understand if you're going to work with Python, and that's some basic software engineer tools. So for example, I'd recommend learning about virtual environments and dependency management. So what is pip? What is UV? How do you create virtual environments? And how do you isolate your various dependencies? I also highly suggest learning about Git and GitHub because you're going to use that in pretty much any project or workplace you're at. And I also suggest having some basic fluency with some bash and terminal commands. Things like changing directories, searching for files, using things like the GP command for example, and just being comfortable in that environment. That's something that's going to be very important, especially when you get to some of the later topics in this list. Moving on from that, I am going to suggest, but this is optional, that you do learn some math and statistics. While you don't need to do this, the more math that you know, the deeper your understanding is going to be. And even learning the basics can really help you understand how these algorithms work on a lower level which I think just makes you a better engineer. So I would suggest looking at some linear algebra. So for example vectors, matrices, dotproducts and how you represent data in multiple dimensions. Then looking at some basic probability and statistics and also learning about things like gradient descent, cost functions and various optimization concepts. No need to be an expert here, but learning the basics is really a smart decision and something I'm glad I did back when I was in my computer science degree. Next, we move on to machine learning foundations. Now, from here, this is where you can start getting into some machine learning algorithms. But before you dive into things like deep learning, you want to start with the basics. So, first look at a module called scikitlearn. From here, you can start looking at some supervised learning algorithms, things like regression and classification. You should train a few models, do a few examples, run through some tutorials, and play around with this on your own. I then suggest that you get into unsupervised learning. So looking at things like clustering, whether it's K means clustering, K nearest neighbors, and looking at things like dimensionality reductions, maybe random forests, and again things that are unsupervised. Now, while you do this, you should learn about model evaluation. So the various metrics, validation, overfitting, and other topics to understand if your models are actually performing the way that you expect them to. Moving on, we have deep learning. Once you've learned the basic algorithms and you understand kind of those core foundations of machine learning, you can start getting into some of the more fun stuff, which is deep learning. Now, this involves training neural networks. And to do this, you can pick one of two modules, TensorFlow or PyTorch. Don't need to learn them both. Just pick one. Either of them are totally fine. Now, from here, I would suggest building and training simple neural networks, understanding the architecture, and looking a little bit at the math. That means understanding things like activation functions, loss functions, and optimizers. Once you've got that down and you've built a few examples, you can start looking at convolutional neural networks and recurrent neural networks, which are used for more advanced and specific tasks. For example, convolutional neural networks are used for image processing or video processing. Now once you've got a taste of deep learning, hopefully you've built a few projects, you've played around with it, and you feel comfortable, you can start looking into some more realworld machine learning workflows. At the end of the day, most of your work as machine learning engineer is not going to be training the best new model. It's actually going to be working with data. So that's why it's important to really understand how to perform data prep-processing and data cleaning and what a data pipeline actually looks like. After that, I also suggest looking into things like feature engineering as well as model tuning and cross validation. Now, going a step further here within this topic, you'll also have things like deployment, right? So, you know, in a real world ML workflow, you need to deploy your applications out. In order to do that, I suggest learning the basics of modules like flask, fast API, and streamlin. Flask and fast API would be for the backend API to actually allow access to your machine learning models and for people to call them. And streamllet is a really simple front-end module that you can use in Python that allows you to build dashboards, do visualizations, and actually, you know, visually represent your Python data. Now, after that, there's a few optional topics that I would recommend looking into. And this is really where we get into LLMs. Now, while LLMs are interesting, they're definitely, you know, the most hyped thing as a machine learning engineer, not a pure AI engineer, you're not going to be touching them a ton. you're probably not going to be training your own LLM from scratch. So, while it's important to understand them, keep in mind that a lot of the core machine learning skills are still extremely important and you shouldn't throw those out. So, within the LLM section, I would recommend looking at things like the transformers architecture and understanding the various APIs that these LLMs have available to them and how you can run them, things like open- source LLMs, which LLM you should choose for which task, and how you can integrate them into your projects. And with me saying projects, that leads me to the last section, which is projects and portfolio. Now look, if you want to be a machine learning engineer, you need to demonstrate evidence that you actually have these skills. You can do that through real on the job experience, but before you have that, you need to have some projects. Great places to get some project ideas are places like Kaggle. You can do challenges there. You can download example data sets. And I highly suggest that you build at least one personal project that you're proud of that has a data pipeline, some kind of model, and that you deploy out and have some kind of like infrastructure and UI around. So like a simple website that you could go to, you could put some information in, it uses your machine learning models and gives you something back. That's the absolute basics. And obviously, the better your project is, the better chance you're going to have of getting employment. Now, while there are definitely a lot of other skills that I could cover on this road map, this is meant to be very specific to Python, and I think it's covered most of the modules and frameworks that you're going to want to look at. Of course, as you get more advanced and you learn more, there's a lot of other areas that you can dive into, but generally speaking, this will cover the fundamentals and set you up really well. So, with that said, I am going to end the video here. I hope that you found this helpful. If you did, make sure to leave a like, subscribe, and I will see you in the next one.