In this article, I will take you through a complete roadmap for Machine Learning. If you don’t know about me, I was from a commerce background even I followed the same roadmap from learning a programming language to mastering the concepts of Machine Learning.

Before moving further with the complete roadmap on Machine Learning if you are not from a science background and still think about how you can master machine learning, what skills you need, what difficulties you may face, and what degree you need? I have already shared my complete journey from commerce to machine learning that you can read **here.**

Now, I will take you through the complete roadmap for machine learning. Before exploring each step in the roadmap let’s have a quick look at the complete roadmap for machine learning below.

## The Complete Roadmap for Machine Learning

Steps | Machine Learning Roadmap |
---|---|

1. | Learn A Programming Language (Python) |

2. | Learn Mathematics for Machine Learning |

3. | Learn The Basic Libraries for Mathematics and Data Handling |

4. | Learn Data Visualization |

5. | Learn Machine Learning Algorithms (Classification, Regression, Clustering, Dimensionality Reduction) |

6. | Learn Deep Learning |

7. | Start Working on Projects |

The above roadmap is all you need to learn machine learning. Now let’s go through each step in the above process to understand what to learn, from where to learn and how to learn.

#### Learn a Programming Language:

The first step in the roadmap for Machine Learning is to learn a programming language. Now if you wanted to learn how to develop Operating systems, or learn the art of competitive coding or even if you were looking for an industry-standard language then I would always suggest you learn C++.

But you are here for Machine Learning, so I will always suggest you to learn Python for machine learning as the support of libraries for Machine Learning is good with Python.

Now if you are new to coding or let’s say you are from a commerce background and want to learn machine learning then I will suggest you learn C++ first and then start learning Python. Now if your end goal is machine learning then you don’t need to learn everything in C++ before learning Python, just learn the concepts of Data Structures and algorithms using C++ and then you can easily shift to Python.

Learning C++ will not waste your time if you think that you don’t have anything to do with C++, instead, it will help you to learn Python and machine learning every easily.

#### Learn Mathematics for Machine Learning:

The second step in the roadmap for machine learning is to learn mathematics for machine learning. Now you don’t need to learn everything in mathematics, there are some of the topics that you need to learn if you are aiming for machine learning.

But why mathematics for machine learning? Without learning mathematics you can learn machine learning but after a certain level, you will feel that you need to grow more in your career. With that being said when you perform machine learning tasks you are using the algorithms that are already made by some popular mathematician like Linear Regression.

Now, when you want to grow your career in machine learning the only way is by designing your algorithms instead of using a pre-defined algorithm. So without the proper mathematical knowledge and the intuition behind an algorithm, you will never be able to learn how to design and build your algorithms.

The topics of mathematics you need to learn for machine learning can be explored from **here**.

#### Learn Basic Libraries for Mathematics and Data Handling:

The third step in the roadmap for machine learning is to learn the basic Python libraries for Mathematics and Data Handling. You need to spend a lot of time to learn Pandas and NumPy.

Pandas and NumPy are the most important libraries in Python. Pandas is for Data handling and data manipulation and NumPy is for numerical Python. As machine learning is all about finding patterns in the data so NumPy and Pandas and really must learn libraries for a beginner.

You can learn about the practical implementation of NumPy and Pandas for Machine Learning from below:

#### Learn Data Visualization:

The fourth step in the roadmap for machine learning is to learn the art of data visualization. Data Visualization is one of the most important step in machine learning tasks, as to extract patterns in the data it is very important to visualize the features, past behavior, and patterns in the data.

Now data visualization is also done by using libraries in Python. There are mainly three important Python libraries for Data Visualization. Matplotlib and Seaborn for static visualizations and Plotly for interactive visualizations.

You can learn about the practical implementation of Matplotlib, Seaborn and Plotly for Data Visualization in Machine Learning from below:

#### Learn Machine Learning Algorithms:

The fifth step in the roadmap for machine learning is to learn about machine learning algorithms. Machine Learning algorithms can be classified into many types that you can explore from **here**.

Without the knowledge of Machine Learning algorithms, you cannot create machine learning models to classify, detect or any other task that you want to do with machine learning. Now there are a lot of machine learning algorithms some are very important and some are one of those that you may hardly ever need to use.

So you must know which algorithm you need to learn and which not. You can learn about the implementation of all the essential machine learning algorithms from **here**.

#### Learn Deep Learning:

The sixth step in the roadmap for machine learning is to learn the concepts of deep learning. Deep learning is very different from algorithms that you will learn in the previous step. Deep learning is all about Neural Networks.

Neural networks are computational algorithms that have been inspired by the brain of humans. By understanding how a neural network works you can easily learn the concepts of deep learning. Deep learning is even more than just neural networks. But to get started learning Neural networks will be enough.

You can learn about neural networks from **here**. After understanding how a neural network works you can explore more about it by working on projects based on Neural networks that you can easily find **here**.

#### Start Working on Projects:

The above roadmap is like a syllabus that you need to follow to learn all the necessary concepts of Machine Learning. But all the topics will only give you the knowledge of theoretical concepts and practical implementations. No step in the above machine learning roadmap will give you a practical experience.

So this is the reason why you need to start working on projects when you will realize that now you know about most of the topics of machine learning and you are ready to get some hands-on experience in Machine Learning.

Working on Machine Learning projects is really important. It will give you a practical experience and also it will help you to build a portfolio of your projects that you can show to any employer if you are aiming for a job.

You can get more than 100 solved and explained machine learning projects from **here**.

#### Summary:

You can learn all the stuff mentioned above on your own without enrolling yourself into a course also. You just need to improve your research skills. If you need some pdf resources to learn all the topics that you need to cover in the above roadmap for machine learning then you can mention me in the comments section below.

I hope you liked this article on the complete roadmap for machine learning. Feel free to ask your valuable questions in the comments section below.