Start Here With Machine Learning: A Beginner-Friendly Step-By-Step Procedure.
What is machine learning?
Do you want to learn machine learning faster and more effectively, too?
The truth is: machine learning isn’t hard. What makes it seems ‘hard’ is the way and manner the intending learner approach it.
What is machine learning?
According to Tom Mitchell, “The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience”
The core concept of machine learning is making the machine capable of learning on its own without being explicitly programmed.
Based on The Oxford Languages, machine learning is primarily concerned with “the use and development of computer systems that can learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.”
Machine learning has become a hot topic across the globe. Hardly will anyone discuss anything artificial intelligence without a reference to the critical role of machine learning.
Moreso, when you consider the advent of big data analytics or predictive modelling, the role of machine learning cannot be overemphasized.
Machine learning has also been adjudged to be the most “sexy” and sought-after career.
But why this?
- Machine learning has a wide application spectrum with an incredible ability to provide insights into complex problems efficiently and effectively.
- The applications of machine learning have also helped in driving business decisions with ultimate implications on business performance and key company’s bottom-line indicators.
- More importantly, one of the great benefits of the application of machine learning has been its analytical and predictive power.
- Globally, the demand for machine learning experts (developers/engineers) has surged creating a huge supply gap.
- Lastly, due to the increase in big data demand, generation, capturing and analytics, machine learning has been playing a leading role in making data intelligent.
Despite its potential, many people still find it difficult to learn machine learning.
But why is learning machine learning ‘hard‘?
- Technical requirements: a good grasp of basic computer and programming languages appreciation, coding, data handling and analytics is not easy to come by.
- Prerequisite knowledge including mathematics, statistics and probability has always been perceived as a major barrier for starters in learning machine learning.
- There is also the data gap challenge in the sense of limited data availability and application, especially in developing economies.
- Apart from the technical/hard skill requirements, being proficient in machine learning also requires some level of creativity, experimentation and tenacity on the part of learners.
- Lastly, in machine learning, no use case is the same. With a new project comes new challenges.
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To learn machine learning with ease, here are the ultimate steps you need.
Obviously, these steps are a no-brainer and that explains why starters often take them for granted.
I will advise you seriously consider these strategies as you start with machine learning. They might look simple, but you wouldn’t want to neglect them.
There is nothing as frustrating as learning machine learning without a clear and effective strategy.
If you are ready to break the ‘hard’ part of starting with machine learning and building a solid foundation for yourself within a short time, the following steps are what you have been searching for.
Let’s get started.
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Step 1: Come to terms with your “why” of starting with machine learning. Does it really worth it?
Learning is great but not everything is worth learning.
So, why are you interested in machine learning?
No, seriously, why do you want to learn machine learning?
As simple as this question looks, you will be amazed to discover how many machine learning starters still struggle with identifying their goals and the purpose of starting with machine learning.
The fact is, you may end up having a rough time with machine learning simply because you failed to articulate your ‘why.’
Ok, let me put it in this context. Let me ask you some questions:
Are you preparing to start a career as a beginner with entry position?
Are you more interested in transitioning from your current job/career to a full-blown machine learning job?
Do you want to improve your skill, capacity and capability in applying machine learning tools and framework in your current job without necessarily starting a new career path?
Lastly, are you just an enthusiast who love machine learning with no definite position regarding a career?
The ‘why‘ is all you need to understand as this will help you smoothly navigate your learning journey going forward.
Your next move will depend on your answer to these questions.
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Step 2: Develop your plan. Have your goal, timeline, requirements and targets all spelt out.
Great you have established the most important foundation which is identifying the purpose of learning machine learning.
But then, how do you move forward from here? Where do you start?
Many people jump into machine learning with no clear plan and strategy. The repercussion is shown in their inability to fudge ahead with what they started.
You need to develop a working strategy with a clear and measurable timeline, outcome and resources needed.
This strategy will also depend on your learning approach and preference.
So, choose the learning strategy that works best for you.
Ultimately, your purpose is to come up with an easy-to-execute implementation strategy that will help make your learning more efficient and smoother.
Your strategy is expected to have some details on what, when and how to learn. It should also reflect on some outcome indicators.
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Step 3: Take care of the fundamentals
I tell you, this is one of the most important moves that will help you all through your learning journey. Do take care of core concepts and fundamentals of machine learning. You can’t jump the gun.
This is your foundation.
Apart from helping you to have a firm grip on what really matters in machine learning, understanding the basics also gives you the opportunity to explore different areas of machine learning.
This can help you identify your interest or area of specialisation early.
I know how tempting starting with machine learning can be. Today you are learning the concept, tomorrow you are already on a Deep Learning course. Don’t let this be your story.
Those who can learn machine learning productively and within the specified timeframe are those who give priority to learning the fundamentals.
Specifically, you should spend your time and other resources to understand the following areas:
- Fundamental ideas, benefits and usefulness of machine learning
- Basic concepts and frameworks of machine learning
- Basic tools and resources
Step 4: Delve into a general overview of machine learning and have fun
Give yourself some liberty to ‘play’ around machine learning.
Scan through anything machine learning. Enjoy anything about machine learning that comes your way.
Free your mind. Spread your wing wide.
Be a generalist.
Have a fair idea about many things about artificial intelligence, machine learning, deep learning, etc. Have some understanding of different applications and domain of machine learning.
Learn like you are just having a fun.
Devote your time to understanding machine learning categories and how they are linked.
Try and read about some basic machine learning application questions and issues.
Register for general online courses and complete them.
Read and follow blogs on machine learning.
Satisfy your basic curiosity about machine learning concepts, scope and applications.
Just keep having a fun time!
Step 5: Narrow down. Go deeper
Having gained a general idea of machine learning, it’s time to decide which area you really want to devote your time and resources to.
So far, you have seen the relevance of different areas of machine learning. You have even handled various projects. Now it’s time to dig deeper into that field you are most passionate about.
In clear terms, your plan should include details such as the following:
What area of machine learning am I interested in? Supervised, unsupervised, deep learning, etc.? This isn’t to say you have to hyper-niche at the beginning. But you don’t have to hyper-generalize either.
When you decide on your area of specialisation early, it helps you to concentrate your effort and focus on what really matters to your goal so you don’t get distracted easily.
Finalising this part of your plan helps you streamline the choice of online courses, books, etc. you really need.
Sure, a general idea of the overview of other areas will be helpful to you, but you have to focus your attention on where you really need it. So, you can start broad but maintain your core focus.
You don’t have to learn machine learning forever!
Step 6: Experiment with different specific projects and data in your domain
One way to learn machine learning faster is through handling practical projects.
One of the benefits of engaging with use case projects is that it allows you to have an understanding of the complete overview of the machine learning project pipeline.
You can actually start by understanding the general overview of the project such as data handling, data analysis, model specification and turning, model estimation, etc.
You don’t have to be distracted or discouraged by the Python codes (or any other programming software used). All you need to do is gain some insights on how and why those codes are used.
In fact, I will advise you to start with this early in your learning process. You can’t go wrong with it.
There are different channels through which you can run through complete machine learning projects.
- First, you can take on some free online courses that are focused on some medium start-to-finish practical machine learning projects.
- Second, you can explore some interesting blogs and other online resources where the emphasis is on practical machine learning projects.
- Lastly, you can also join some interesting online platforms such as Kaggle, GitHub, etc.
If you follow this step, you will gain a lot within a short time.
Step 7: Build a wining machine learning portfolio
Building your machine learning portfolio even as a beginner is one of the fantastic moves you can ever make.
Announce your arrival in the community.
Share your works.
Teach others about what you know.
Showcase your skills and quality all through the machine learning project pipeline.
Sharing your portfolio with others opens up great opportunities for you.
At this point, your interest should be in developing a winning portfolio that tells the story about you and your capability in handling the machine learning role.
Step 8: It’s time to go all out. Explore. Grow. And keep growing
This is where the real work and success resides.
To get complete and all-around exposure to the machine learning field, I would advise you either to start with an internship or get the job.
By all means, go full time!
Also, you need to constantly network your way up. Start building your network. Share your great and fantastic projects. Follow others in your community.
Aspire to constantly update your skill to the current state of the art in your field. The race to learning machine learning isn’t a sprint. It’s a marathon.
New things and inventions are coming up every hour.
You have to keep keeping yourself up to date.
Let me hear from you. Keep learning. Keep growing. Go for it.
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- How to Learn Machine Learning The Self-Starter Way: Complete Guide.
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- A Complete Guide To Learning Machine Learning In Your Spare Time: An enthusiast’s approach
- 5 Key Most In-Demand Soft Skill Sets You Need to Develop as a Machine Learning Career Starter
- How To Build A Compelling And Winning Machine Learning Portfolio That Will Get You That Job.
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Hi, there! I’m Olusegun.
I help ambitious machine learning starters develop a working strategy to learn, build projects, develop a winning portfolio and start a career in machine learning with ease. Dreaming of building a successful career in machine learning? Let's do it together.
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