Almost every business, big or small, today uses machine learning somewhere in their applications. Today, every business is in dire need of hiring machine learning experts, regardless of their educational background. So if you want to become an expert in machine learning, you have to learn it correctly step by step. In this article, I will walk you through how to learn machine learning step by step.
Steps to Learn Machine Learning
Below are all the steps to learn machine learning:
- Learn Mathematics
- Learn Python Libraries
- Learn the Fundamentals of Machine Learning
- Learn Machine Learning Algorithms
- Learn Deep Learning
Let’s go through all of these steps one by one to understand how to learn machine learning.
Learn Mathematics:
Learning math is very important for machine learning. This helps to understand how an algorithm works so that you can build your algorithms when needed. Many machine learning concepts are very dependent on or related to mathematics. You don’t need to be extremely proficient in math, you just need to explore the basics of the topics mentioned below:
- Linear Algebra
- Statistics
- Probability Theory
- Calculus
There are very few resources for learning math for machine learning. So you can follow this book to learn everything about all topics of mathematics for machine learning.
Learn Python Libraries:
Python is the most preferred programming language for machine learning because of the support of libraries it has to work with data. So the second step to learn machine learning is to learn the fundamentals of Python and then some of the Python libraries that you need to work with data. So below are the Python libraries that you need to learn after the fundamentals of Python for machine learning:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
Fundamentals of Machine Learning:
Now the next step you need for learning machine learning is to start with the fundamentals of machine learning. Here you have to explore all the necessary topics that you need to understand when and why you should use machine learning and in what kind of problem which machine learning approach should be used. So in this step, you need to explore all the topics mentioned below:
- What is machine learning?
- When do you need to use machine learning?
- Supervised and Unsupervised Learning
- Classification
- Regression
- Clustering
- Performance evaluation metrics
- The life cycle of a machine learning model
Machine Learning Algorithms:
This is the most important step in becoming an expert in machine learning. You have to spend a lot of time learning machine learning algorithms. Below are the most important machine learning algorithms you should know for learning machine learning:
- Linear Regression
- Logistic Regression
- Naive Bayes
- Decision Trees
- Random Forests
- K Nearest Neighbors
- k Means
- DBSCAN
Besides the algorithms mentioned above, you can explore other machine learning algorithms from here that you can use depending on the problem you are working on.
Deep Learning:
The last step is to learn Deep Learning. Deep learning is a subset of machine learning, which means working on a lot of data with a lot of important features that cannot be handled by traditional machine learning algorithms. In these kinds of problems, you have to use neural networks that are inspired by the functioning of the human brain. So, the Python libraries you need to learn for implementing deep learning are:
- Tensorflow
- Keras
- PyTorch
After going through all the steps mentioned above, you need to work on projects and case studies as this is the only way to improve your problem-solving skills using machine learning. You can find some of the best machine learning projects and case studies solved and explained using Python from here.
Summary
So this was the complete roadmap that you can follow for learning machine learning step by step. Learning machine learning is very important for you as almost every business, big or small, today uses machine learning somewhere in their applications. I hope you liked this article on how to learn machine learning step by step. Feel free to ask your valuable questions in the comments section below.
