How to Learn Machine Learning with Python

Learn Machine Learning with Python

Machine Learning is booming these days, with increasing applications in various industries like entertainment, healthcare, finance, and many more. So, many students and many people are turning towards learning and implementing machine learning, for their careers. If in case you are confused about how to start learning Machine learning, and that too using Python, do not worry, since we have got your back.

In this article, we are going to understand how can you Learn Machine Learning with Python. But why Python? You too might have heard a lot about Python. It is a very popular programming language, and when it comes to Machine learning, many people prefer using Python.

If you want to go through some prerequisites of Machine Learning, you can go through this article. So, let’s get into understanding how can you learn Machine Learning with Python, and guess what! It’s too easy!

How to Learn Machine Learning with Python

Step 1 – Prepare your mind, and get started with Python.

Earlier, only a particular person could learn and implement concepts related to programming languages. But nowadays, anyone can learn anything. So, that being said, you can too learn and implement Machine learning easily, using Python.

It won’t be hard, or easy, but totally depends on your consistency, and how you understand and practice concepts. It is also going to take some time if you have never coded before in your life, but it doesn’t mean that you won’t be able to do it, because it is very easy!

Python language is very easy to get started. Anyone can learn Python easily, and get to the advanced level. All you need is consistent practice, problem-solving, and the right guidance.

To learn Python, it may take anywhere from a few days to a few weeks, depending on a lot of factors, like how much time you can dedicate to learning Python, or how much you practice. But if everything goes fine, you can find yourself comfortable writing many different programs in Python, and then easily you can get going.

If you are interested in learning Python programming language, and that too in an easy way, you can definitely check out our course on Python programming language, which takes you from the very basics, and with many practical examples, to easily understand the concepts. Also, you get easy-to-understand Notes with the course, and once you complete the course, you get a worldwide shareable certificate. So, you can go through the course, and learn Python in a super easy way!

Our course will definitely help you to get started with learning Python, and after this, you can easily move to Machine Learning, Artificial Intelligence, or Data Science.

So, learning Python is going to be the first step, when you are willing to go for learning Machine Learning. When it comes to Machine learning, there are other languages as well, but Python is very simple, easy to get started, and is preferred by many people. Also, there are many libraries, and frameworks, which make things super easy for us.

Step 2 – Solve Programs and do projects(practice is a must! If you want to keep going)

Most people try to move towards Machine Learning concepts once they are finished with their Python course/concepts. But seriously, you should not be like most people! When you have learned many different concepts related to Python, it is important to practically use them in some example programs, so that you can understand them well in their working, and be more comfortable using them further in your programs.

So, you should be solving as many examples as possible. You can also try some small projects, so that you can collectively use multiple concepts from Python, to create something meaningful.

Step 3 – Move towards tools, and other libraries and frameworks

When you are comfortable with many Python concepts, which you should be in some months, you can then go with learning numpy, pandas, matplotlib, and seaborn. Also, you should start using tools like Jupyter Notebook, so that you get more comfortable using the tool further. There can be many things to learn, but you can get a broad idea of numpy, pandas, matplotlib, and seaborn, such that you can use them for different purposes in your programs.

Also, you can use Jupyter Notebook when you are learning Python at an earlier stage since this would make you familiar with the interface and working of Jupyter Notebook. The tool is so handy and useful when it comes to coding and data visualization.

Step 4 – At this point, you would began learning about Machine Learning.

When you are done with Python, and some libraries, now you can be ready to make your way toward learning Machine Learning. Learning Machine learning can be kind of considered a process, in which you would learn different things, like data collection, data preprocessing, Data Analysis, learning and applying different machine learning algorithms, learning some Maths (statistics, probability, linear algebra, calculus)

All this would be part of your learning process. But here, you will get to learn different things about data processing, and data analysis, different types of Machine learning, and different algorithms that are used in Machine Learning.

So, all this is really amazing, when the approach is right. If you kind of move in a theoretical way, there is the probability that you would get bored and feel like leaving, but when you keep a practical approach and solve problems, you would get to see the results.

Here are some of the details about the above-mentioned things, so that they won’t feel alien to you the next time you see them.

Data Collection – Well, this term is self-explanatory. Machine learning is about learning from the data, and taking some decisions, making some predictions. So, we would need the data, and it depends on where are you going to get the data. The data can be sometimes openly available, or sometimes, you would need to collect it from the clients, or through some API, or some websites.

Data Pre-processing – Once you have the data, you need to do some preprocessing. This can simply mean that we should be able to transform our data, so as to provide it to the Machine Learning models. This can involve data cleaning, feature selection, feature extraction, encoding, and handling missing data as well.

Data Analysis – Through Data Analysis, we can identify patterns in the data, and get useful insights from the data, through visualization. When you understand the data through visualization, you can get to see some patterns, which may otherwise not be visible. You can understand the relationship between the data, and you can understand what features contribute to how much.

So, learning about Data Analysis is going to be much helpful in understanding data. You can use NumPy, Pandas, Matplotlib, Seaborn, and Plotly for this.

Machine learning algorithms – This is where things get interesting. Till now, you were just playing with the data, getting it ready to be fed to some model, so that the model can learn from the data, and be ready to make predictions. There are many types of Machine learning, like Supervised Learning, Unsupervised learning, Semi–Supervised learning, and Reinforcement Learning. You can learn about them, and explore and try to implement different algorithms, solving different problems.

Even when you understand the process, you can get towards the implementation of the algorithm, for solving different problems. Later on, when you feel this, you can get into the depth of how the algorithms work.

If you wish to know more about Machine Learning, you can consider our articles on Machine Learning, which are easy to follow and understand.

Step 5 – Getting into the depth of Machine learning.

Well, up to this point, you should be familiar with different types of Machine Learning, and different algorithms, and you should be also done with some simple projects. Now, if you feel, you can get into the depth of how the Machine learning algorithms work, and understand the underlying Maths behind the things. Although you would need to learn certain Maths when exploring the algorithms so that you can get the basic working of the algorithms at times. But here as well, you can get into more depth.

Step 6 – Learning Deep Learning.

When you are done learning about different types of Machine Learning, and doing some projects, you might want to learn Deep learning next. Well, what is Deep learning? Deep learning is a subset of Machine learning, which is kind of inspired by the human brain, and teaches computer computers to process data in a similar way.

Step 7 – Projects, and a lot of Projects.

Well, when you have learned a lot of different concepts, you should do some good Machine Learning projects, so that you can apply what you learn, and get to understand the uses of algorithms in a practical way.

When you do some projects, you also get to see the overall end-to-end process, right from data collection, to model deployment. So, you can become familiar with the process, and it is very important.


Learning Machine Learning is going to be a long journey, so you should be power packed most of the time, and at different points, try to solve some programs, so that you can get familiar and used to the concepts. Machine learning is an amazing thing to learn for your career because then you can have a lot of opportunities.

If you are interested to learn more about Machine Learning, you can simply read our other articles related to Machine Learning, and Python, and keep learning!

Q: Can you learn Machine Learning with Python?

Ans: Yes, Python is a very simple language, and using Python, you can learn and implement machine learning, in a very simple way.

Q: What is Machine learning?

Ans: Machine learning can be considered as something, that enables machines to learn from the data, and make predictions, or take decisions, without being explicitly programmed.

Q: Which language can be better for Machine learning?

Ans: While there are many programming languages for learning and implementing Machine learning, you can simply get started, and get going with Python programming language, since it is very easy to learn, and there are many libraries and frameworks available, which help a lot.