Machine Learning Road Map in 2022 – DataDrivenInvestor

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Artificial Intelligence is not all about Robots or the man who was sent from the future.

Reference

On the other hand, most people think about it like that.

Actually, if we open what AI is for the developers, it all started with the notion of Machine Learning.

So what is Machine Learning?

There are much different explanation exists, let us see IBM’s explanation;

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Actually, the term Machine Learning was first stated by Arthur Samuel in 1959.

The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.[11][12] Also the synonym self-teaching computers were used in this time period.

These definitions look really fancy.

In recent years, learning AI- Machine Learning becomes really easy.

I learn to program with R Programming, on the other hand, I like using Python as a programming language, I explained here why.

Enough with the Introduction, let’s start, with how you can learn Machine Learning, and what to learn, to be a Master in Machine Learning and therefore AI.

  • Programming
  • Statistics
  • Calculus
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Programming Language- Python

As per to SlasData’s research made in Q3 2021, Python has reached over 11.3 million developers and its popularity has passed Java.

Reference: Slashdata

It was the second-fastest-growing language community according to research that was made in 2021.

For me, one of the main reasons behind Python’s increasing fame is its easy-to-use syntax.

You can see from the graph, Python is the most popular in DS/ML- Data Science and Machine Learning.

And the most popular language, Javascript, is the least popular in DS/ML.

So, it can be easily understood even by beginner developers who are just starting out, Python is a popular programming language choice in the growing fields of Data Science and Machine Learning.

That’s why I choose to code in Python.

How you can be proficient in a Programming language?

Like learning in new Language, you have to build a neural path in your mind and you should daily exercise.

One of the ways to do that, opening an account in Hackerrank.

There are really good exercise and interview questions exists, which helps you to improve your coding skills zero to here and Free.

Of course, there are other options that exist, you can read my article to find one for you.

If you want to apply the Machine Learning model, you should know Sci-kit learn library. But what about the prior libraries for Sci-kit Learn?

  • Numpy– For numerical Analysis
  • Pandas– For Dataframe analysis
  • Matplotlib/Seaborn– For Data Visualization

You can find tutorials on Youtube. There are outstanding pieces of stuff that exist there and are totally free.

On the other hand, just watching these tutorials is not enough to build a learning neural path in your mind.

You should try to write these codes in Jupyter Notebook.

Statistics

One of the other things, you should be proficient in to learn Machine Learning is Statistics.

There are a lot of methods that exist to learn Statistics.

I learn this after taking a course on Coursera.

You can apply for Financial aid to any course on Coursera however be careful to find a course regarding your Programming Language.

I made a mistake by Learning Statistics in R. R Programming is a really good programming language however, after Learning Machine Learning I decided to use Python. If I made research back then, I did take a course, which will explain Statistics in Python.

On the other hand, if you choose R as a programming language, I highly recommend that course.

The instructors really explained the concepts with various different visualization and the concepts are mathematically explained very well.

Also, Khan academy, really simply and detailly explained like they always did,

In addition, if you had prior knowledge about Statistics, you forgot concepts, but you do not know how to make a repetition, here are my two stories, that explained Statistics A-Z for Machine Learning.

Calculus

Actually, as a result of my previous education, I was originally a Mechanical Engineer, so I did not need to take a Calculus course in the learning phase. Thanks to Andrew NG, the things that I forgot as a result of the time passed from my graduation, remembered.

He really explained very well, in all of his courses on Coursera.

If you still do not know Andrew NG, make a little bit of research.

The courses he presents are extremely helpful and updated.

Here is one of his courses about Machine Learning, which you can apply for Financial aid and take this course Free.

If you do not want to learn the Calculus side of Machine Learning, there are many Data Scientists exist, who do not know the calculus behind the algorithms and are extremely proficient in their explanation and interpretation.

On the other hand, if you want to know, here is my recommendation for you

This course explained calculus and linear algebra along with the good advanced topics, which you should learn.

Also, these youtube videos really well explain Linear Algebra visually.

Also at this channel, there are many video series about Calculus, Deep Learning, which are explained very well.

If you are a person, who needs visual presentation, these courses if for you and are free.

And here is my article about Linear Algebra for a quick reminder for you.

  • Choose carefully your Programming language and take courses according to your choice.
  • Take courses by doing research first.
  • Be consistent and write your own codes along with the way, not just watch the video series.

After a little bit of struggle, you would become master in Machine Learning. So if you want to learn AI and earn really good accordingly, the path is like that.

Do you know what selected as “The sexiest job of the 21st Century”?

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Thanks for reading my article.

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