Machine learning is the new electricity of the IT industry. It is the science of getting things done with the help of intelligent machines. It is a concept wherein computers uses a set of algorithms and techniques to make decisions and predictions from available data.
Machine learning is changing the world. It is being used in a number of sectors such as science, healthcare, production, retail, telecom, etc.
Let’s quickly check the best way to get started with machine learning.
Step 1: Understand the basics
Spend a couple of weeks enhancing your “general knowledge” about the field of data science and machine learning. You may already have ideas and some sort of understanding about what the field is, but if you want to become an expert, you need to understand the finer details to a point where you can explain it in simple terms to just about anyone.
Step 2: Learn some Statistics
I have a confession to make. Even though I feel like a machine learning expert, I do not feel that I have any level of expertise in statistics. Which should be good news for people who struggle with concepts in statistics as much as I do, as it proves that you can be a data scientist without being a statistician. Having said that, you cannot ignore statistical concepts – not in machine learning and data science!
So what you need to do is to understand certain concepts and know when they may be applied or used. If you can also completely understand the theory behind these concepts, give yourself a few good pats on your back.
Step 3: Learn Python or R for data analysis
Programming turned out to be easier to learn, more fun and more rewarding in terms of the things it made possible, than I had ever imagined. While mastering a programming language could be an eternal quest, at this stage, you need to get familiar with the process of learning a language and that is not too difficult.
Both Python and R are very popular and mastering one can make it quite easy to learn the other. I started with R and have slowly started using Python for doing similar tasks as well.
Step 4: Complete an Exploratory Data Analysis Project
In the first cricket test match ever played, Australian Charles Bannerman scored 67.35% (165 out of 245) of his team’s total score, in the very first innings of cricket’s history. This remains a record in cricket at the time of writing, for the highest share of the total score by a batsman in an innings of a test match.
What makes the innings even more remarkable is that the other 43 innings in that test match had an average of only 10.8 runs an innings, with only about 40% of all batsmen registering a score of ten or more runs. In fact, the second highest score by an Australian in the match was 20 runs. Given that Australia won the match by 45 runs, we can say with conviction that Bannerman’s innings was the most important contributor to Australia’s win.
Just like we were able to build this story from the scorecard of the test match, exploratory data analysis is about studying data to understand the story that is hidden beneath it, and then sharing the story with everyone.
Personally, I find this phase of a data project the most interesting, which is a good thing as quite a lot of the time in a typical project could be expected to be taken up by exploratory data analysis.
Step 5: Create unsupervised learning models
Let’s say we had data for all the countries in the world across many parameters ranging from population, to income, to health, to major industries and more. Now suppose we wanted to find out which countries are similar to each other across all these parameters. How do we go about doing this, when we have to compare each country with all the others, across over 50 different parameters?
That is where unsupervised machine learning algorithms come in. This is not the time to bore you with details about what these are all about, but the good news is that once you reach this stage, you have moved on into the world of machine learning and are already in elite company.
Step 6: Create supervised learning models
If you had data about millions of loan applicants and their repayment history from the past, could you identify an applicant who is likely to default on payments, even before the loan is approved?
Given enough prior data, could you predict which users are more likely to respond to a digital advertising campaign? Could you identify if someone is more likely to develop a certain disease later in their life based on their current lifestyle and habits?
Supervised learning algorithms help solve all these problems and a lot more. While there are a plethora of algorithms to understand and master, just getting started with some of the most popular ones will open up a world of new possibilities for you and the ways in which you can make data useful for an organization.
Step 7: Understand Big Data Technologies
Many of the machine learning models in use today have been around for decades. The reason why these algorithms are only finding applications now, is that we finally have access to sufficiently large amounts of data, that can be supplied to these algorithms for them to be able to come up with useful outputs.
Data engineering and architecture is a field of specialization in itself, but every machine learning expert must know how to deal with big data systems, irrespective of their specialization within the industry.
Understanding how large amounts of data can be stored, accessed and processed efficiently is important to being able to create solutions that can be implemented in practice and are not just theoretical exercises.
I had approached this step with a real lack of conviction, but as I soon found out, it was driven more by the fear of the unknown in the form of Linux interfaces than any real complexity in finding my way around a Hadoop system.
Step 8: Explore Deep Learning Models
Deep learning models are helping companies like Apple and Google create solutions like Siri or the Google Assistant. They are helping global giants test driverless cars and suggesting best courses of treatment to doctors.
Machines are able to see, listen, read, write and speak thanks to deep learning models that are going to transform the world in many ways, including significantly changing the skills required for people to be useful to organizations.
Getting started with creating a model that can tell the image of a flower from a fruit may not immediately help you start building your own driverless car, but it will certainly help you start seeing the path to getting there.
Step 9. Undertake and Complete a Data Project
By now you are almost ready to unleash yourself to the world as a machine learning pro, but you need to showcase all that you have learnt before anyone else will be willing to agree with you.
The internet presents glorious opportunities to find such projects. If you have been diligent about the previous eight steps, chances are that you would already know how to find a project that will excite you, be useful to someone, as well as help demonstrate your knowledge and skills.
Step 9.
Now you are expert👍😎
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