7 Steps To Get Started in Machine Learning | Data Science

This article will explain How To Get Started Learning Machine Learning In 7 Simple Steps. But first of all, What is machine learning?

If you’ve ever considered a career in technology, you’ve certainly seen words like machine learning, deep learning, and artificial intelligence tossed around a lot.

There are a plethora of machine learning career postings all over the place.

So you’ve been intrigued, but you’re also unsure what machine learning is and how to get started learning it.

So, in this post, we’ll explain what machine learning is.

I’ll also walk you through a basic seven-step method for getting acquainted with machine learning from the ground up and landing your first career as a machine learning engineer.

Now, Let’s start with a definition of machine learning.

What is Machine Learning?

Machine learning is a subset of artificial intelligence that allows us to create applications that can learn from experience or data on their own and enhance their performance without having to be programmed.

This is made possible by the fact that machine learning algorithms are taught to search for trends in data and use them to make decisions or forecasts without the need for human interference.

We now have a clear understanding of what machine learning is.

But first, let’s look at three reasons why you should study machine learning in 2021 before we get through the steps you need to take to get started.

What are the benefits of learning machine learning?

So, what’s the big deal, and why should you be concerned?

Here are three reasons why I believe machine learning should be on your list of essential skills to learn in 2021 if you want to work in technology.

1. Experts in Machine learning are in high demand

Machine learning is a skill in high demand right now. This is due to the fact that an increasing number of businesses are incorporating artificial intelligence into their product creation and processes.

Consider the following industries: surveillance, image recognition, pharmacy, finance, e-commerce, and video streaming services, many of which are searching for machine learning experts to create artificial intelligence solutions for their product lines.

You will open yourself up to a future with countless work openings that go unfilled for the majority of the year if you have machine learning skills.

Furthermore, several small companies have noticed how machine learning has a significant impact on market intelligence and are able to invest aggressively in it in order to remain ahead of the competition.

2. Machine learning professionals are well compensated.

If you want to work in technology and earn some of the best wages, consider machine learning. See more reasons here.

A machine learning engineer should expect to earn around USD $ 142,000 per year on average, with a machine learning analyst earning up to USD $ 195,000 per year.

Reference: 2021 IT Certifications with Highest Paying Salaries to Propel Your Career Forward

Now, if you are a professional software developer with more than ten years of experience, you would find it difficult to receive anything close to the average annual income for a machine learning engineer.

As such, if your interest in designing algorithms and neural networks isn’t high enough, how about following the money?

Related: Why Analytics Skills are in High Demand in India, UK, USA

3. Data Science is linked to Machine Learning

You’ve certainly heard of data science before you’ve heard of machine learning.

I highly advise learning machine learning if you want to learn a specialised ability that also enables you to work in the large and wider data science industry.

Since data science has been dubbed the sexiest profession in the twenty-first century, you can begin your career as a data science specialist by specialising in machine learning, since you’ll often need to communicate with your company’s data science engineers to synchronise your workflows.

So, through learning machine learning, you’ll become proficient in all of these areas, allowing you to interpret surprisingly large data sets, draw meaning from them, and then use that to provide useful insights.

I hope these three factors have piqued your interest enough to motivate you to begin learning machine learning from the ground up in 2021.

Without further ado, here’s a step-by-step guide on how to get started with machine learning right now.

And in this tutorial, I emphasise the importance of self-study. It is not a university course or a college degree. As a result, you’ll have a lot of leverage over your learning while still being able to tailor it to your preferences.

How to learn Machine Learning?

How To Get Started Learning Machine Learning In 7 Simple Steps

1. Learn Calculus

It is known that there are people who will tell you that theoretical skills are not necessary. But let me tell you, if you want to get begin with machine learning, you definitely need a course in mathematics.

So how do you go about doing that? Since I can assure you from personal experience that studying machine learning can be a nightmare, if not outright terrifying, unless you first have a gentle introduction to its prerequisites.

Now, I’m not suggesting that you beat your head against a wall before you become a skilled mathematician with exceptional calculus skills.

However, in order to get acquainted with machine learning today, you must master the fundamentals of calculus.

Most machine learning algorithms depend on understanding the underlying data distribution, which is linked to probability. If you’ve mastered this delivery, you’ll be able to complete a wide variety of activities.

As a result, mastering calculus is a must if you want to tap into the superpowers of probability and statistics.

And there are a plethora of free videos available online to help you get started. When you’re at it, make sure you’re still working on the practise challenges. You won’t understand much if you just nod your head in agreement with the teacher.

2. Learn Algebra

When it comes to learning machine learning in 2021, I think the next theoretical ability you can master is algebra.

And in this case, I’m referring to linear algebra, which is specifically designed for machine learning students.

When it comes to algebra, the trick is to pick up as much theory as possible by practising with the sample problems provided by most tutorials and courses.

You may be tempted to skip this section in order to get your hands dirty with coding as soon as possible, just as you did with the previous portion on learning calculus, but I highly advise you to do so.

What is the reason for this?

Since most machine learning implementation requires applying principles from statistics and computer science to results, once you’ve completed the specific prerequisites or these criteria, the rest will be quite simple.

Although some might recommend skipping calculus and algebra entirely and winging your way through machine learning by trial and error, I ask again: why?

Why take the long road when you can cut corners by starting with the fundamentals and save yourself the hassle later?

3. Learn Python

When it comes to mastering machine learning, the next move is to learn to code.

No, seriously? If you’re serious about a future in machine learning, there’s no way around this one. You can see the 10 Best Reasons to Learn Python Programming Language.

And for this purpose, I highly advise you to begin by learning Python programming. Now, I realise that this may seem to be a little skewed, as there are a slew of other languages that are equally appropriate for machine learning, but bear with me as I clarify.

Although machine learning can be done in a variety of languages, Python is the gold standard when it comes to machine learning.

It’s extremely simple to learn, and there are a plethora of ready-to-use machine learning libraries just waiting for you to tap into their ability and use them to build artificial intelligence applications.

You’ll be able to incorporate sound programming principles, write modular and readable code, write proper unit tests, and manage errors with Python.

Isn’t that true in other languages as well?

Any developer, I suppose, has a prejudice against their prefered programming language, and you shouldn’t be punished for it.

To launch a new cold war on programming languages, I’d like to emphasise that if you’re already proficient in another programming language, such as Java, you can use it to learn ML.

Otherwise, see my other post from 2021 on the 10 best programming languages for data science.

If you’re learning to code from scratch, my recommendation is to avoid memorising any of the programming language’s commands by heart. You can’t and you can’t.

Simply becoming an expert at answering questions on stackoverflow.com and then sifting through the answers to see what may be important. Check this out: Future Learn Data Science on Microsoft Azure Using Python Programming Course

4. Learn Machine Learning

Enough of the laying of the foundation dance. Now is the time to get straight into machine learning theory and practise.

And by this, I mean diving headfirst into machine learning abstractions. Learning how to apply them to data, models, and algorithms will help you make better decisions.

You should research many of the subjects that underpin machine learning approaches, as well as a realistic guide to getting started with machine learning. This is the most enjoyable part. This are the capabilities you’ll need in self-driving vehicles, AI helpers, fraud detection, and spam prevention, among other things.

Again, at this point of machine learning, it’s more about picking up as much science and experience as possible.

The general rule is to study as much philosophy as is necessary to get started and avoid getting lost. After that, mastery comes with time, and you go back and forth between theory and reality. And I believe that this Starford University ML course on YouTube would be ideal for this.

While there are excellent books available for studying machine learning from the ground up, I prefer videos to books because they are more immersive and easier to follow.

However, I do have some reference books on hand, such as Stanford University’s Elements of Statistical Learning.

It might seem that we are only talking about theory at this stage, but that is not the case.

It might seem that we are only talking about theory at this stage, but that is not the case.

This machine learning tools will provide you with a plethora of practise problems to solve. And you will study theory, put it into practise, learn more theory, and practise before the completion of the course.

After that, you’re able to refine your talents by diving into your own personal deliberate ventures. Check out the next step in learning machine learning from the ground up for more details.

5. Build Projects

Now that you’ve read and researched, it’s time to apply what you’ve learned.

So you’ve mastered the fundamentals. However, when it comes to getting started with machine learning, reading and training will only get you so far.

To demonstrate your real machine learning skills, you must incorporate them into a project that you can show off in front of a group of interviewers, such as while applying for a position.

So, after studying all of the necessary tools and theory, it’s time to get your hands dirty with some actual data and put your models into action. And looking for Python machine learning assignments for beginners is the easiest way to get underway.

When I say you create your own projects, I don’t think you go out and implement the most common and well-known algorithms to well-known datasets.

Since everybody has always done so.

These algorithms and datasets are now familiar to all recruiting managers. You won’t understand how to do anything else outside of these datasets if you draw on these. You won’t be able to force yourself to be imaginative and you won’t be able to push yourself.

Here are three suggestions for developing your portfolio when working on personal projects:

1. Try to practise the whole machine learning workflow in your programmes. This means starting with data collection, washing, and preprocessing and working the way up to model construction, tuning, and assessment.
2. Incorporate the usage of actual datasets rather than dummy datasets in your ventures. You’ll be able to develop your own experience around the types of models that are appropriate for specific problems if you do this repeatedly.
3. Investigate specific subjects in greater depth. You learned about specialised methods like grouping and dimensionality reduction in the previous step on ML theory. Now experiment with various clustering algorithms on the datasets to see which one does the best.

As a result, build projects that teach you how to plan your own data as well as machine learning DevOps.

You’ll learn how to export the templates so they can work in the cloud with DevOps, which is an important skill to have. Get to know the fundamentals of cloud computing and computer learning applications.

This will help you stand out from the crowd and land the prized ML place. Read more below;

• Containers & DevOps: Benefits, Metrics, Challenges & Risk Management
• DevOps RoadMap & Courses to Becoming a Certified Engineer
• DevOps Kubernetes Course Content [Beginner’s Guide]
• [Udemy] Learn DevOps 2021: The Complete Kubernetes Course

6. Network & Network

Isn’t your primary goal of getting started with machine learning to land a respectable career with a respectable company?

Then you must remember this at all times during the learning journey.

Unless you’re only doing it for fun, in which case you should skip this section.

The most common obstacle for newcomers to machine learning is finding their first real work after learning the fundamentals of the field and doing a few personal tasks.

After mastering these skills, they believe it would be simple to land the coveted machine learning job.

Unfortunately, this is how things are today.

In the natural world, you could interact with others. If you use technical networking platforms like LinkedIn, you can communicate with other machine learning experts because this is one of the best ways to get your name in front of the right people.

But, most importantly, if you want to hear more about machine learning, go to one of the meetings. Take part in tournaments such as those hosted by Kaggle.

This frequently provide you with chances to showcase your incredible ML abilities, such as giving speeches, competing in tournaments, winning prizes, and so on.

7. Ask For Help

Now it’s time to take the next step towards a future in machine learning.

As a budding machine learning programmer, one crucial lesson I learned the hard way is when to ask for assistance.

Though this may have been obvious from my previous measures, I decided to make it a separate segment so you couldn’t overlook it.

In order to learn machine learning in 2021, you must know where to seek assistance.

To establish yourself as a brilliant data scientist, you don’t need to know any of the algorithms off the top of your head or memorise any of the Python commands. I’ve been stuck for days trying to patch a single algorithm flaw on my own.

I later found that I could have spared myself a lot of time and effort by simply Googling the issue. There was already a response. What do you think?

Join machine learning forums, StackOverflow channels, and other places where you can engage with other machine learning engineers and get answers to your questions. It will spare you a lot of time and aggravation.

“Psst…“

This is all the professionals do as well.

Conclusion

I am hoping that this tutorial on how to get started with machine learning has given you a better understanding of the steps you would take to learn machine learning.

In 2021, machine learning will be one of the most common career areas in technology, with opportunities to work with some incredible businesses and on some incredible ventures.

To summarise, here are three reasons why I believe you should begin learning machine learning right now.

• It’s a specialty that’s in high demand. Many businesses that are seeking to remain competitive today include machine learning engineers. As a result, getting the first real job would be relatively easy.
• It offers some of the most competitive compensation in the technology sector. When comparing the wages of machine learning professionals and software developers, the disparity is night and day. You’d like to join the machine learning team.
• It trains you for a data scientist career. Since machine learning is a subset of computer science, learning machine learning skills immediately leads to a career as a data scientist… the sexiest job in the twenty-first century.

If you’re looking for a career in technology that will provide you with a lot of fulfilment, you should start learning machine learning. That isn’t to suggest that it’s a straightforward area to get into…

Or that machine learning is simple to grasp. By a long way, no!

Something worth its salt necessitates a certain amount of upfront effort and commitment.

Are you a specialist in machine learning or are you interested in learning anything about it?

What are some additional tools for learning machine learning that I didn’t include in this list yet believe are essential?

And let us know what you think in the comments section below.