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Machines have been an integral part of our lives for years now. And now with machine learning and AI, it has become possible for machines to work independent of human intervention.  Progress in this space now allows machines to carry out tasks as diligently and intelligently as humans do, with high accuracy, if not completely perfect. We are quite literally trying to build a brain for machines, and that can be done only if machines are taught to learn the way we do. No wonder machine learning has found such a huge following in the world today, both by its creators and the users.

As with any domain, there is always a tussle and debate around the right way to build a particular software and the programming language which is the best fit for it. The domain of machine learning is not an exception either. And while the tide keeps shifting every once in a while, major support for Python has stayed in place consistently through the years.

Here’s why a Machine learning with Python course is absolutely essential for you if you’re looking to make your mark in the machine learning space and get a job that helps you make it happen.

1.Reusability of code and ease of configuration

Machine learning, in itself, is quite a complex challenge to begin with. Understanding the logic, recognizing patterns, and developing code that can help keep it all together is even more difficult than it might seem at the outset. The last thing any programmer would want is a language that makes things even more complex than they need to be.

Python comes with a good collection of libraries that serve a major purpose of code reusability. Not only are these libraries filled with instructions of how to use them in the best way, but simple tweaks in their modules can help them to be reused for different functionalities within the same model. Configuring them is as easy as breaking them down, testing them, making changes, and rebuilding them, all within a single space. Compare this with other languages and many programmers struggle with challenges like multiple structures, syntax differences, and so on.

Image by Johnson Martin from Pixabay

2.Identifying and correcting errors quickly

We all know that machine learning is not going to be a simple task. There will be multiple iterations of building the algorithm, carrying out tests, and verifying results. What could be detrimental in this aspect is if there’s no way to figure out why the results are incorrect and trace them back to the code in order to understand the area responsible for the failure. Even popular languages like R, that boast extensive application capabilities, don’t make this an easy task, requiring hours and hours spent in simply detecting a small error.

Contrast that with Python that is extremely handy with error reporting which is specific and right on point. A few clicks can take you right to the module that caused the error and allow you to get down to the root cause in a considerably shorter period of time.

3.Machine learning, and not machine solving

The goal of machine learning is to create logic and algorithms that help the machine to learn by itself. We are not looking for results on what the machine is doing and what output it brings out. That enters the realm of research in evaluating machine performance.

Python helps programmers focus on the core aspect of machine learning. It is highly reliant on real-time processing and analysis of data, which allows the machine to make changes in its behaviour. This aspect of helping a machine gain insight into its own working is deftly achieved by using Python than any other language.

4.Wider applications that support machine learning

Picking up Python as one of the primary programming languages to be proficient in has a lot of advantages that go beyond the world of machine learning, but ultimately feed right back into it. It helps you be more aware of the different logic principles that govern code functionality and make you a better programmer. This helps pick up other languages speedily and code them in an effective manner.

A lot of other resources are also essential in understanding the nuances of machine learning, and these may make use of programming components that are independent of the domain. However, most of these are also built using Python, so knowing the language beforehand can always help you understand them better.

5.A high demand for Python in major job markets

Currently, the United States is one of the biggest markets where machine learning developments are at an all-time high. A look at job portals, makes it evident that many companies are on the lookout for good Python programmers that understand the nitty-gritty of machine learning and can develop excellent solutions.

In the coming years, this trend is just going to flow over to markets all across the world. So even if you don’t have immediate plans of going to the US and applying for a job, preparing for the inevitable boom in the sector is always in your benefit.

Learning other languages like SAS or R is a good decision if you have set your mind to enter a specific retail space that makes use of them widely. However, a good Machine learning with Python training course is your best choice for staying in step with the changing times and being future ready.

Whether you’re an aspiring data scientist or you’re already a data scientist looking to expand your skill set, know that the knowledge of Python is an essential skill in the field of data science. Python has become the preferred language in data analysis over the last few years, and it’s mainly due to its ease of learning and the fact that it is hugely time saving during application, though those are not the only reasons. Listed below are five reasons Data Science with Python training is essential for every data scientist. But, before we get to the why of learning this programming language, let’s understand what Python is.

What is Python?

Python is a versatile programming language that supports functional programming, structured programming, and also object Oriented programming. The versatility of this language is what makes it a good choice for data scientists. Python enables developers to create codes in a modular style and the codes can be reused as well, since Python supports the use of modules and packages.

Image by Johnson Martin from Pixabay

Python is a simple and easy to learn programming language. It requires the use of a unique syntax which supports readability. It’s also a low-cost programming language. Python’s interpreter and standard library are available free of cost in binary and source form.

Python for Data Scientists

According to the Indeed Data Science skills report of April 2019, Python is among the top skills employers look for in data scientists, along with machine learning and SQL and Hadoop.Having these top skills not only boosts your resume but also gives you the chances of earning a higher salary. According to LinkedIn, data scientists with an updated skillset make an average of $107,000 a year.

Here are the top five reasons why you should take data science with Python training.


Python is more scalable than R, making it more and more popular across various industries. It is also faster than Matlab and Sata. YouTube is one platform that uses Python because of its flexibility in problem solving situations.

Compatible with Hadoop

Python is compatible with Hadoop, the most popular open-source big data platform. The PyDoop package offers access to HDFS API for Hadoop, allowing programmers to write Hadoop MapReduce programs and applications. HDFS API allows you to connect to HDFS installation, which makes it easy to read, write, and get information on global file system properties, directories, and files.

Easy to Learn

Python is easy to learn for non-programmers too, compared to other programming languages. Due to its readable code, large online community, and varied learning resources, Python is a good choice to start learning programming language.

Extensive data science libraries

Python has over 72,000 libraries in the Python Package Index, making it popular among aspiring candidates. These libraries are upgraded regularly. Most of the libraries in Python are available for data analysis. Here are some of them.

  • NumPy: The library has a number of high-level mathematical functions that operate on multidimensional arrays and matrices.
  • SciPy:  It works in association with NumPy arrays and has routines for numerical integration and upgradation.
  • Matplotlib: This is a 2D plotting library which contains data visualizations in the form of histograms, bar charts, and scatterplots with minimal coding lines.

Online Python Community

Python has increased its reach in the world of data science and a large number of volunteers are developing Python libraries. Due to this, it’s possible to develop advanced tools and processes in Python.

Which Skills Will You Learn?

Python is simple to learn, making it an ideal learning language for beginners. Read on to know which other skills you will learn with the data science with Python course.

  1. You will get an in-depth understanding of data science processes, like data wrangling, data exploration, data visualization, and hypotheses building & testing.
  2. You will be enabled to perform high-level mathematical calculations using the NumPy package.
  3. You will gain expertise in machine learning using the Scikit-Learn package.
  4. You will Understand how to use supervised and unsupervised learning models, such as linear regression, logistic regression, clustering, K-NN, and pipeline.
  5. You will gain the knowledge to extract useful data from the websites with the help of web scraping using Python.
  6. You will gain the ability to perform scientific and technical computing with SciPy package and its sub packages, like Integrate, Optimize, IO and Wave.

Data Science Is an Ever Expanding Field

Expanding consumerism has caused a surge in the demand for data scientists. Knowing the coding and programming tools and when to utilize their strengths to get the most out of your analysis is essential for a data scientist. Python is a popular programming language in data science, and a working knowledge of this language will give you a stepping stone to start your career as a data scientist.