How to start machine learning. Best guide.

How to start machine learning. Best guide. Hello, guys, welcome back and today I’m going to be talking about how to learned machine learning. If you’re in the field of computer science then machine learning is an extremely important skill for you to have. But even if you are not in the field of computer science, it is also an important skill because it’s going to play such a big part in our future.

Machine learning is a field of computer science that allows computers to learn without being explicitly told to do so. Most of you have probably experienced machine learning in your day to day lives. If you guys have personal assistants at home like Alexa or Google Home, even when it comes to doing Google search and getting better search results. Besides that, machine learning is predicted to disrupt nearly every single sector in our world.

So safe to say it’s a really important skill to learn. So let’s get into how you can learn machine learning. First of all, there is no one size fits all approach because everyone has a different way of learning. Here is just my friend take on it. And also a big factor in how you go about your own learning is what you hope to gain from it. Some of you might be hoping to start careers

  • To get a job in machine learning.
  • Others might want to start a startup in machine learning
  • or just learning it for fun.

So it really depends on what your end goal. For me personally, I learned machine learning when I was doing my project on machine learning. The very first thing that you need when you’re learning machine learning is

How to start machine learning. Best guide.


Now, I know there are two kinds of arguments for this one which says math is actually not really important because there are so many good libraries that take care of the math, like cancer floor and Pytorch and whatnot. And the other argument which says, you know, math is extremely important because when you run into problems down the line, you will know how to solve it.

For me personally, I believe that, yeah, you do need to learn math, but don’t restrain yourself and tell yourself that, OK, I’m going to finish learning math, and then I’ll continue on to the next step. I think you should take it on as, OK, I’m going to be learning this math throughout my entire journey as a machine learning engineer.

Online Course

The next step is to actually take a basic machine learning course, which teaches you the basic machine learning algorithms. I personally did, a course in Coursera. But there are many other ones out there which are just as great. Andrew uncaused Coursera actually was very heavily focused on math, but that worked for me personally. But there are many other ones out there which are not that much focused on math, which might work for other people.

If you want some free course by read our article on free courses by clicking here.

Learning Python

The third step is to learn Python. There are actually many languages that are used in machine learning, but Python has kind of become the x-factor language. Now, just like with math, you don’t have to master Python before moving on to the next step, take it as a learning journey as well. My recommendation is learning basic python is really helpful and you are going to need that. There are many free online resources that are really great to learn Python. I personally learned using data camp.

Data preparation.

This is a step that actually a lot of people overlook. But once you do your first couple of machine learning projects, you’ll realize that you probably spend about 60 to 80 percent of your analytical pipeline just doing data preparation. Data preparation is something that can be fully automated. So that is the reason why it takes up such a huge amount of time. Not just that, but doing good data preparation actually results in higher accuracy of your machine learning algorithms. So this is why it plays such an important role.

Deep Learning Library

The fifth thing that you guys need to do is actually familiarize yourself with the Deep Learning Library. I would recommend for beginners to start out with psychic learning because it has all the classical machine learning algorithms. And when you decide to move on to maybe deep learning, you can move on to TensorFlow, because it has a lot of support for deep learning.


The last step is practice, practice, practice. You have to reiterate what you’ve been learning in order to be good at it. A really good way to do that is by joining competitions on websites like Kaggle, where you’re able to compare the effectiveness of your machine learning algorithm with other people. And that’s a really, really good gauge of your own skills.

One of which is a really, really important thing and which I have to learn personally, is to be patient with yourself when it comes to learning machine learning because it’s not necessarily the easiest thing to do, but it’s something that anyone can achieve if they put their time and mind to it. And another thing is to not have any zero days.

No Zero Days

What I mean by that is not necessarily that you have to do an entire project every single day, but spend time every day to actually learn something new that you haven’t learned before or just to reiterate what you’ve already learned at the beginning. I mentioned knowing what your own end goal is to learning machine learning. Whether it is a career or to start something for yourself or is just a hobby. So I’m going to be looking at it from starting a career in machine learning.

Machine learning in terms of Job

So what I’m going to be doing right now. Let’s look at what the requirements for a machine learning engineering job are. So let’s take a look at this machine learning engineering position in Apple, and let’s see what they’re actually looking for in a candidate. They want someone who has

  • In-depth expertise in deep learning. Deep learning is actually a subfield of machine learning, and it makes use of neural networks.
  • And they also want someone who is extremely experienced in machine and reinforcement learning, reinforcement learning is just like deep learning, is actually a type of machine learning.
  • Next, they want someone who is really familiar with what is new and what is actually happening in deep learning, someone who is really current with all the new kinds of technologies.
  • Should also be able to train and debug deep learning systems, defined metrics, and data. This is what were the data preparation part actually comes in when we’re talking about defining metrics and data sets, performing error analysis, training models in a modern deep learning framework,
  • Next, they also want someone with experience with hardware, specific optimization of modules and deployment, and also excellent programming skills in Python, C, and C++.

This is just to give you guys an idea of what these top tier companies actually look for in machine learning engineers. For those of you who are applying for jobs in the machine learning field, you might realize that a lot of them ask for candidates to have either a masters in computer science or a Ph.D. And I urge you guys not to be discouraged when you see this, because the machine learning sector, first of all, is changing daily, because I believe that the most important thing is to have the knowledge and also a lot of useful side projects related to the job that you’re applying to.

So that is what’s going to help you stand out from the rest of the candidates which are applying to the same job as you in the machine learning field.

Well, guys, that was how I personally got started into machine learning. And if it was like comment and leave, any questions that you guys might still have. And I’ll get back to you on that and see you next time.

2 thoughts on “How to start machine learning. Best guide.

  • January 1, 2021 at 3:55 pm

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