How to Start with Machine Learning?

When most people hear Machine Learning, they picture a robot, a dependable butler or a deadly Terminator, depending on who you ask. But Machine Learning is not just a futuristic fantasy; it’s already here. It has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). Machine Learning is the science and art of programming computers so they can learn from data. But how to program computers so that they can learn from data, this is what we learn when we learn machine learning. In this article, I will take you through how you can start with Machine Learning.

Start with Machine Learning with These Steps

To Start with Machine Learning, you have to cover a big process step by step that will help you to become a Machine Learning Expert. Below is all the process to get started with machine learning. I hope you will follow the process below step by step to start with Machine Learning.

Learn Programming

Yes, the first thing you need to learn is a programming language. A programming language is a special language which is used to write instructions for the computer to execute. When I say you have to learn programming to start with machine learning, I mean to say you have to learn Python.

Python is most preferred with machine learning because the support of external libraries is very good at Python. Libraries are packages that add more functionality to improve the process of programming for a specific task.

After learning a programming language like python you have to learn some external libraries to start with Machine Learning. Now let’s go through all the external libraries we need to learn Machine Learning.


NumPy means Numerical Python, It provides an efficient interface to store and operate on dense data buffers. In some ways, NumPy arrays are like Python’s built-in list type, but NumPy arrays provide much more efficient storage and data operations as the arrays grow larger in size.

NumPy arrays form the core of nearly the entire ecosystem of Data Science and Machine Learning tools in Python, so time spent learning to use NumPy effectively will be valuable no matter what aspect of Artificial Intelligence interests you. You can learn the complete practical knowledge about this package from here.


Pandas is a newer package built on top of NumPy and provides an efficient implementation of a DataFrame. DataFrames are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data.

As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs. You can learn the complete practical knowledge of this package from here.


Matplotlib is the basic plotting library of Python programming language. It is the most prominent tool among Python visualization packages. Matplotlib is highly efficient in performing a wide range of tasks. It can produce publication-quality figures in a variety of formats. It can export visualizations to all of the common formats like PDF, SVG, JPG, PNG, BMP and GIF. 

It can create popular visualization types — line plot, scatter plot, histogram, bar chart, error charts, pie chart, box plot, and many more types of plot. Matplotlib also supports 3D plotting. Many Python libraries are built on top of Matplotlib.

For example, pandas and Seaborn are built on Matplotlib. They allow accessing Matplotlib’s methods with less code. You can learn the complete practical knowledge of this package from here.


Scikit-learn is a library in Python that provides many unsupervised and supervised learning algorithms. It’s built upon some of the technology you might already be familiar with, like NumPy, pandas, and Matplotlib. The functionality that scikit-learn provides include:

  • Regression, including Linear and Logistic Regression
  • Classification, including K-Nearest Neighbors
  • Clustering, including K-Means and K-Means++
  • Model selection
  • Preprocessing, including Min-Max Normalization

You can learn all the practical knowledge of Scikit-Learn from here.


TensorFlow is a powerful library for numerical computation, particularly well suited and fine-tuned for large–scale Machine Learning ( but you could use it for anything else that requires heavy calculations). The Google Brain team developed it, and it powers many of Google’s large-scale services, such as Google cloud speech, Google Photos, and Google Search.

It was open-sourced in November 2015, and it is now the most popular Deep Learning library ( in terms of citations in papers, adoption in companies, stars on GitHub, etc.). Countless projects use Tensorflow for all sorts of Machine Learning tasks, such as image classification, natural language processing, recommender systems, and time series forecasting. You can learn the complete knowledge of TensorFlow from here.

Start Working on Projects

Now, there are more packages and libraries that we need to learn in the process to become a Machine Learning expert. But all the above libraries are the fundamental packages to start with Machine Learning. You can learn all other libraries easily while exploring the above ones. 

After exploring all the above libraries you now need to start working on your own projects, because at the end you want to start with machine learning to set up a good career. For that, you need to build a good portfolio of your projects. I have more than 40 projects for you. You can have a look at them and start working on them so that you can also work on a project that is completely yours. Here are all the Machine Learning Projects.

I hope you like this article on How to start with Machine Learning. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Machine Learning.

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Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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