How to Learn Machine Learning With Python

You might be thinking, why should you be a part of AI or Machine Learning, when there are lots of specialists doing it?

This is where you can go wrong! Who knows what may be there for you in the future? With artificial intelligence and machine learning developing at a breakneck pace, it’s worth strengthening your knowledge on what is going on in the IT sector worldwide and you can earn lucrative income.

Be it Machine Learning, Artificial Intelligence, or Data Science! It is everywhere!! Who knows you might be able to create a personal assistant for you just like ‘Jarvis’ from Iron Man! Isn’t it fun? So are you ready to learn machine learning and land your dream job at one of the top tech companies? If yes, let’s dive into the details about machine learning.

What is Machine Learning

Machine Learning is the science of providing the system the ability to automatically learn and improve from experience without being explicitly programmed. In short, machine learning is the process of teaching a computer system how to make accurate predictions when it is being fed by data and information in the form of observations. The motive of ML is to let the computer learn automatically and without any human intervention and adjust the required actions/patterns accordingly.

Why Learn Machine Learning? 

Even if you are not an expert of math or programming, the success in this field is completely based on how you plan to get started for a machine learning career. There are plenty of reasons to build a future in machine learning. A few of them are the massive global demand all over the world by the top tech companies for AI and ML experts, rendering better job opportunities in this domain by providing job roles as data engineers, data scientists, etc, and many other reasons. In a nutshell, every industry is looking to apply machine learning in their domain to develop cutting edge machine learning applications.

How Can You Learn Machine Learning?

By following these simple steps, you will be able to understand why Python programming language is needed for machine learning and how you will be able to design ML algorithms.

Step 1: Improve Your Math Skills

If you are interested in learning AI and ML, you should know math. You don’t have to be a mathematics expert, but the basic math knowledge is needed for analysis and machine learning. As machine learning often works with high-dimensional data, you should know about scalars, vectors, matrices, etc. Mathematical analysis such as gradients and derivatives are the basics of many machine learning algorithms. One of the efficient ways to learn math for machine learning is by building a neural network. For that you will have to apply Linear Algebra to represent a network and Math Analysis to optimize it.

Step 2: Understand the Basics of Python Syntax

Well, to start over, you don’t need to know every small detail or the syntax while learning any programming language, it is just that you have to begin learning somewhere! So, if you are willing to make a career in Machine Learning or AI or even Data Science, getting started with Python programming is your first step. 

You might be thinking, how can you master Python for Machine Learning from scratch? Right? Well, there are plenty of courses, written materials, lecture videos, and lots of code exercises available for you to learn Python coding. As you implement theoretical knowledge into practical concepts, day by day you will gain the understanding of the working of algorithms. 

In today’s world, Python is being extensively utilised in data science and in designing complex machine learning algorithms, and if you are able to break in through, no doubt you will be offered with high paying jobs. The gist is, the sooner you start working on real projects, the sooner you will be able to grow! Get ready with all the basic knowledge of Python, as this can kickstart your career very well.

Step 3: Overview of the Main Data Analysis Libraries

This stage is to revise Python concepts and deal with the open source software libraries of Python which are applicable for data science as well. So, it is time to learn about the libraries to facilitate practical machine learning algorithms. Python libraries that you will need to explore are: 

NumPy- useful for multi-dimensional arrays.

Pandas- a data analysis library, including structures as data frames for creating new parameters, building queries to data, etc. 

Matplotlib- 2D library for creating graphs and visualization.

Scikit-Learn- a library of machine-learning algorithms for data analysis and mining tasks.

Step 4: Develop Your Own Projects

Once you have mastered the basic syntax for machine learning with Python and the associated libraries, you can start building your own structured projects for your learning purpose. With the help of these projects, you will be able to create your own machine learning algorithms portfolio which can further gain you remunerative jobs. There are many resources available for you to cover your topics on structured projects.

Step 5: Working on Your Projects

We are not saying that you will be able to build a classic machine learning or AI project in a day, but you will learn gradually and will be a part of a reputed company. However, before that, even if you get stuck in the development process, don’t worry, you will be able to solve the errors using StackOverflow, Python Documentation etc. Always be an active participant of any events related to Python so that you will be able to explore any new opportunity that might come your way. Try expanding new projects made by other professionals, and to do that you can use Github.


By following these steps, you will be able to build end-to-end machine learning projects. For your information, Python Developers bring the highest pay-cheques today. Hence, take advantage of online Python courses at your pace, watch video tutorials, enroll for Python machine learning, and get started with your Machine Learning career! If you want to be one of them, get started by no further delay.

 

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About the Author: Aafreen Pawar

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