How to learn Machine Learning?
Deep Tech has taken over the world. While once knowing how to develop an android app would have guaranteed you a fancy job at a much-sought-after company, that is no longer the case. Now all the big companies are on the hunt for people who have expertise in specific deep technologies. Some of these technologies are cloud computing, data science, blockchain, augmented reality, artificial intelligence & machine learning.
If you are just getting started with machine learning then you need to be slightly careful where you get your information. There are a lot of websites that promise to turn you into an ML expert but if you don’t have direction, you’ll end up becoming more confused about the whole thing than someone who hasn’t even heard the words, “Machine Learning.”
But fret not! This article is going to be your companion and tell you exactly how to go about learning ML in the most efficient and beneficial way possible.
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Before getting into that, however, let’s answer the most basic question first.
What does Machine Learning mean?
Everyone who has ever written a program knows that it will only do what it has been programmed to do, in the way it has been programmed to do, and nothing else. Well, some smart people decided to ask the question, what if we can write a program that can learn things based on its own past experiences and improves its performance by itself while also becoming capable of making decisions? This is the most basic and oversimplified version of the idea of machine learning.
Machine Learning Basics: Why Learn This Technology?
The ability of the future is machine learning. Big corporation houses, like Google and Facebook, now leverage the power of ML by integrating it with their core business models. Moreover, the need for ML experts is growing rapidly, creating a severe skill gap in the industry.
You can essentially count on having a safe and successful job in technology if you learn machine learning. You can add significant value to your workplace and increase your marketability for jobs if you have a broad set of ML skills.
Machine learning (ML) can assist in overcoming significant obstacles in the areas of personal finance and banking, healthcare diagnosis, speech and picture recognition, and fraud prevention.
People and businesses would prosper if these issues were resolved, and making such a large contribution also makes one feel very satisfied personally. ML is additionally a ton of fun, given how uniquely it integrates engineering, discovery, and business application.
It is a thriving industry with lots of room for expansion. The practical instruction and practice required to learn the machine learning basics will be enjoyable if you’re eager to take on intriguing issues and come up with creative answers.
Some prerequisites
As mentioned above, Machine Learning is a deep technology and is therefore not for someone who is just entering the world of data handling and coding. Here are some things that you already need to know before you can get started with ML.
You must have a good level of familiarity with the concepts of basic calculus and linear algebra along with a deep understanding of the theory of probability before you take your first steps into the world of machine learning.
Once you feel like you’ve met these prerequisites, let’s get right into how to learn everything you need to know about machine learning.
How to Learn Machine Learning?
First the Basics
You can’t build a skyscraper with weak, poorly defined foundations. You must already know correct and detailed answers to questions like What is Machine Learning? What is it capable of? What can be achieved by using it? What are its limitations? Why is it better than other ways of solving problems? How is it different from AI? Applications of Machine Learning?
If you have any doubts about the answers to the above questions, you need to get them all cleared. This can be done with doing thorough research online or by simply enrolling in an online basic ML course.
The Building Blocks of ML
Once you get done with the basic questions, you would realise just how wide and broad of a field of study machine learning can be—which can make learning it seems overwhelming. Thankfully people have split the basics of machine learning into blocks to make it easy to understand and learn.
These building blocks are:-
- Supervised Learning
- Unsupervised Learning
- Data Preprocessing
- Ensemble Learning
- Model Evaluation
- Sampling & Splitting
Take your time and learn about what they are and why they are used in ML.
Now it’s finally time to get to the most fun part of learning machine learning.
Skills required to Master ML
You can’t master ML without first mastering the skills that are used in it and that is what you need to learn next in your journey towards becoming an ML expert. These skills are:-
- Python Programming
Learning python and building you ML projects in it will make your life a lot easier than if you tried to do so in any other programming language—which is why most ML experts recommend it. You can learn python using the many great free or paid tutorials available on the internet.
- R Programming
While Python is the best language for writing the code involved with ML, no language is better suited to handle the insanely large amount of data that gets used in ML projects that R. Therefore, learning R will also make your journey of learning ML a lot easier. You will find a lot of good free online tutorials for R Programming.
- Data Modeling
Data Modeling is essential to ML. It is mostly used in finding patterns in data which are used in ML to make predictions and in some cases, making decisions based on those predictions. You will need to learn SQL before you can start working on data modelling but free courses are available for that online as well.
- Machine Learning Algorithms
Now we get to the heart of Machine Learning. Nothing in the world of programming can be achieved without the use of algorithms and machine learning is no different. You will need to learn all about how these special machine learning algorithms work to achieve the desired results and how you can apply them in your own ML projects.
These algorithms will the bread and butter of your career in Machine Learning— the better you know them, the easier your life will become for however long you want to work on ML.
- System Design and working with APIs
At the end of the day, you will probably want to make your ML accessible to end-users who don’t have the faintest clue about any of the things that make that project work. For this, you will have to learn how to design a system that allows other people to use your ML project and it would be a cherry on the top if you learn how to build APIs so that you can integrate your project with the work of other people and build something truly special.
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Enroll In an Online Machine Learning Course
Lastly, in this guide on how to learn machine learning step by step, we would like to emphasize the importance of an online machine learning course. Of course, you can always learn machine learning on your own, but learning ML with an online course is a more organized and progressive solution.
Numerous online courses are offered due to the industry’s high demand. When you are just getting started, taking courses might help you acquire momentum. They can also help you develop specialized knowledge in more complex subjects.
Aim to enroll in a course with a cutting-edge curriculum that emphasizes in-demand abilities. Before making a choice, evaluate additional aspects, including possibilities for capstone and portfolio projects and community and mentor assistance.
Go For an Internship
Finding an internship is the final step before submitting an application for ML jobs. Recruiters almost universally favor applicants who have experience working as ML interns. This is a chance to network, make connections, and learn insider information about the business.
How to be a Machine Learning Engineer
Machine Learning Advantages
High demand in the employment market for ML specialists. It can automate tedious procedures and enhance decision-making. Here’s why machine learning is beneficial:
- Automation: One can leverage ML algorithms to automate their organization’s decision-making procedures, all while eliminating the need for large human input.
- Enhanced Accuracy: Compared to conventional methods, machine learning algorithms may be trained on vast datasets to discover patterns and make predictions.
- Personalization: By using machine learning algorithms, experiences for users can be made more relevant to them, including personalized recommendations and adverts.
- Predictive Upkeep: Machine learning algorithms may be used to anticipate equipment failures, minimizing downtime and maintenance expenses.
- Better Healthcare: Algorithms for machine learning can be used to evaluate patient data, identify ailments, and suggest therapies, leading to better healthcare outcomes.
Machine Learning Disadvantages
- Model training may take a while. If not monitored properly, it could result in biased or unethical outcomes.
- It can be difficult to understand and complex. Also, it may cause automation to displace some
Conclusions
By mastering all these skills, you will become a pro at Machine Learning and well on your way towards scoring a high paying job at a Fortune 500 company that is on the hunt for Machine Learning experts.
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