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Machine Learning Prerequisites: Programming, Statistics & Mathematics

 July 14  | 0 Comments
This entry is part 3 of 5 in the series Machine Learning Questions People Also Ask Google

We produce a lot of data daily. Most of this data is not analyzed despite being useful because it is impossible for humans to analyze so much data. Hence, the need for machines to learn how to make sense of data. This blog provides insights into machine learning prerequisites. But, before that:

What is Machine Learning?

Machine learning refers to a computer’s ability to learn from data without necessarily being programmed to do so. It’s a field that combines computer science with statistics to make the use of data meaningful.

Supervised vs Unsupervised Machine Learning

Machines learn predominantly in two ways – supervised and unsupervised. In supervised learning, machines use a sample set to predict outputs for inputs. Unsupervised learning, on the other hand, requires no sample set. Computers detect hidden patterns or trends in data sets because of their efficient ways of observing them.

Demand for Machine Learning

Machine learning is huge across the globe with salaries soaring. Entry-level salaries for data scientists with machine learning skills generally range between $ 100,000-150,000. And with data being used for all sorts of purposes across industries, the demand is only expected to increase.

Sure, machine learning won’t help you crack the stock market or make you rich in a day. But, it’s a fun and exciting field to work in that offers plenty of opportunities for quick career growth.

Machine Learning Prerequisites

 So, what are machine learning prerequisites?

Firstly, you don’t have to know everything in statistics or programming to start your machine learning journey. But you must know enough to be able to apply concepts from both these fields to data and make it useful. You also need a fair understanding of mathematics. Let’s explore these machine learning prerequisites further.

Programming (Python)

When it comes to programming languages, Python seems to come out on top for data science purposes. In fact, it is one of the best languages for anyone interested in programming. Python helps you in data wrangling, building predictive models, data visualization and more. It is used across the globe and recorded one of the highest growths in demand.

The best feature? It’s simple. Many believe it is the ideal first language for a programmer as it achieves most tasks with less code. It allows you to implement solutions faster and saves you time in the process. Plus, the language has dedicated libraries for data analysis and machine learning.

Python has a vibrant data science community that offers plenty of videos to learn from. The community also shares bits of codes or solutions to common problems. Stack Overflow is another good source to brush up your Python skills or even learn it. Do you need a computer science degree to learn Python? The short answer is no.

All you need to understand is the underlying logic to make choices between functions or conditional statements, etc. No need to remember syntax. Use a top-down that is result-oriented and starts with the core concepts. Improve your knowledge of concepts as you go along and get plenty of real-world practice (Kaggle, DIY, collaborate with mentors) in the process. This will help you hone your skills better.

Focus also on data science libraries. Libraries save time by letting you import solutions. Get familiar with the Jupyter Notebook, which is a darling of data scientists. Then there is NumPy, which is great for numerics. Pandas for data structures and exploratory analysis. Matplotlib lets you plot data and visualize it. Scikit-Learn is a machine learning library with algorithms and modules that suit pre-processing, cross-validation, etc.