6 Steps to Become a Machine Learning Expert
Everything you need to know to become a machine learning expert.
Machine learning finds patterns in data to make a suitable decision. Deep learning is a branch of machine learning that uses neural networks with many layers. Today, problems such as computer vision, natural language processing, and driverless cars can be solved with deep learning.
To be able to do deep learning projects, I recommend you to learn machine learning first. Universities do not have a separate machine learning department. But fortunately, there are many free resources and training videos on the Internet.
Whether you are a student, an employee who wants to change careers, or someone who wants to use machine learning in your business, you can easily learn machine learning from open sources.
In this post, I’m going to cover the following topics:
- What is machine learning?
- Programming languages for machine learning
- Libraries for Python and R
- Tools you should know for machine learning
- Disciplines you need to learn
- Algorithms for machine learning
- Resources and YouTube channels for machine learning
Before getting started, please don’t forget to subscribe to my youtube channel where I create content about ai, data science, machine learning, and deep learning.
What is Machine Learning?
As you know, the amount of data produced has increased with the development of the internet and social media. If AI is today’s electricity, data is today’s oil. Companies such as Google, Facebook, and Amazon became huge companies because they evaluated the data they obtained. To use oil, you have to process it right? To extract meaningful information from data, you need to process it.
Machine learning is a subfield of AI that aims at automatically building models using data.
You may ask how machine learning came about. Let me explain this briefly. Previously, rules were entered into the machine, and output was produced for inputs according to these rules.
In this technique, there were fixed rules control by people and it wasn’t a very flexible approach. Later, the question emerged whether machines could learn like humans. To investigate this, inputs and outputs were given to the machine, and it turned out that machines can learn the rules.
Let me show you simply how machines learn. For example, you want to teach multiplication to the machine. You give the program a lot of data like 2 and 3 equal 6, 4 and 5 equal 20. The program learns multiplication from inputs and outputs. Then, when you ask the program to multiply any two numbers, the program returns you the answer.
Machine learning is widely used in many fields such as finance, education, biology, and medicine. Let’s take a look at the 6 steps to becoming a machine learning expert.
Step 1. Programming Language
An important part of machine learning is programming. You need to know a programming language to preprocess data and build a model. The most used programming languages for machine learning are Python and R. Python is preferred more because it is a general programming language with easy syntax.
With Python, you can both do data-based projects and work in many areas such as web programming or game development. Python is the most used language in machine learning and deep learning. On the other hand, R is a powerful language for machine learning. R also has a lot of libraries for machine learning. R is mostly used for statistical analysis.
Now let’s take a look at the libraries you need to know about machine learning.
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Step 2. Libraries for Machine Learning
You can build a machine learning model from scratch. But, there is no need to reinvent the wheel. You can build faster and more practical models using libraries such as scikit learn.
Libraries make your work easier when working on machine learning projects. Let’s dive into the most used libraries for machine learning.. First of all, I’ll explain Python and then R libraries.
In machine learning, you do a lot of matrix and array operations. The library you need to know for multidimensional array operations is NumPy.
Another important library is Pandas. Real world datasets are dirty. To clean these dirty datasets, data preprocessing are required. To do this, you can use Pandas.
Matplotlib and Seaborn
It is important to explore the data before building the model. Data visualization is the easiest way to explore data. Matplotlib and seaborn libraries are mostly used for data visualization.
Matplotlib is a powerful library and you can create great visualizations with this library. For statistical graphs, seaborn library is king..
The main purpose of machine learning is to build a good model. You can use the scikit-learn library to build a model. You can find many supervised and unsupervised learning algorithms in the scikit-learn library.
TensorFlow and Keras
Another important library for making machine learning projects is TensorFlow. With TensorFlow, you can build end-to-end machine learning projects. In other words, with TensorFlow, you can do every step from data preprocessing to deploying the model. Also, Keras API has been added for easier analysis with TensorFlow. Keras is a widely used API, especially for deep learning.
Many more libraries can be described for machine learning. Here I’ve mentioned the most basic libraries that you should know.
Machine Learning Libraries for R
Although Python libraries are used very often in machine learning, R also has very powerful packages. I can tell you the libraries you need to know for R programming, ggplot2 for data visualization, dplyr, and date.frame for data manipulation, caret and mlr for building machine learning models, and shiny for making interactive web applications.
Now let’s look at the tools you can use in machine learning.
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Step 3. Tools You Need to Know for Machine Learning
There are many tools you can use for machine learning. Let’s take a look at the tools you need to know for machine learning.
With a user base of more than 20 million, Anaconda is a fantastic platform and it includes many libraries and tools. You can use many libraries at the same time without error. I highly recommend you to use Anaconda for machine learning projects. Also, you can work with many different projects to create a virtual environment in Anaconda.
An editor or IDE allows you to easily write codes. The jupyter notebook is perhaps the most used tool for writing code when analyzing data. Jupyter notebook is web-based and a good tool for visualizing data. Jupyter notebook also has cells where you can write texts.
You can use it as an editor in PyCharm and VS Code, but I highly recommend you to use Jupyter Notebook where you can both analyze and present.
Another fantastic tool you can use for machine learning is Colab. Colab is a free cloud service provided by Google. Colab comes loaded with many libraries such as TensorFlow. Colab’s interface is similar to the jupyter notebook.
Git and Github
An important tool you can use for teamwork is git. Git is a version control system. Using Git, you can collaborate with your teammates on your files and codes. You can store your project free on GitHub. With GitHub, you can also present your project to the world and share your codes.
You can use Notion tool to manage your projects. Notion is a web-based tool where you can easily keep your notes.
Music is said to be food for the soul. You can use Spotify tool to focus on your project. You can choose any playlist on Spotify according to your mood.
Step 4. Disciplines for Machine Learning
You can do machine learning projects using libraries. But to understand the machine learning steps, you need to know mathematics, probability, statistics, and linear algebra. Don’t be afraid when you see these disciplines. To learn machine learning, it is enough to know these disciplines at a basic level.
It is also important that you know the field you’re working on. For example, to do projects in the field of bioinformatics, you also need to know the disciplines of biology and genetics. If you don’t know the field, you can work with someone who is an expert in that field or get support.
Step 5. Algorithms for Machine Learning
Data quality is very important for a machine learning project to be successful. Another important point is to use an algorithm suitable for the data. There are many algorithms you can use for machine learning. We can divide these algorithms into supervised learning and unsupervised learning.
If your data has labels, you can use supervised algorithms such as regression and classification, and if your data has no labels, you can use unsupervised algorithms such as k-means, and PCA. If your dataset is large, you can use algorithms such as convolutional neural networks, recurrent neural networks, or LSTM.
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Now let’s look at the free websites you can use for machine learning.
Step 6. Websites for Machine Learning
There are many sites you can use for machine learning. Kaggle comes first among these sites.
Kaggle is one of the world’s largest data science and machine learning platforms. There are more than 1 million registered users on this platform. Some of the world’s top data scientists actively use this site.
On this platform, you can find many datasets and notebooks to inspire you. You can also use a notebook like Google Colab where Kaggle offers free GPU or TPU service. Libraries such as Pandas, NumPy, and TensorFlow are installed in this notebook.
You can earn scores by participating in Kaggle. Some companies can offer jobs to people with high scores on this site.
You can add your achievements to your portfolio by increasing your score on this platform. On Kaggle you can find many trainings such as data science, machine learning, and deep learning If you complete these training, you can even obtain certificates.
Another site I would recommend is Medium. You can find a lot of blog posts about machine learning and data science on this website. You can read these blog posts to keep yourself updated.
The best learning technique is to tell, right? You can also create an account on Medium for free and share what you learn on this platform. You can even earn money from this platform. You can also expand your network and get more recognition by writing a blog post. This way, you can enrich your portfolio and find a job more easily.
Machine Learning Mastery
Another fantastic site I would recommend is the machine learning mastery website. This site has some great articles on machine learning and deep learning, both theoretical and practical.
Nowadays, video lectures are very popular. You can find many free machine learning courses on Coursera or Udemy.
Another platform where you can find video tutorials is youtube., which we all use very often. Most people use YouTube for entertainment, but many channels provide educational content. Let’s take a look at youtube channels.
The first, channel I can recommend is FreeCodeCamp. In this channel, you can find tutorials in 1 video provided by different instructors in many fields, Another channel is Machine Learning University which was founded by Amazon. You can watch machine learning tutorials on this channel. Edureka and Simplilearn are fantastic channels. They also provide certified training. Tech with Team is a great youtube channel. He produces both programming and machine learning content. Other channels related to machine learning are Codebasics and Krisnaik. In these channels, you can find basic library tutorials and projects for machine learning, as well as interviews with those working in the field of data science
As you know, machine learning isn’t learned just by watching. For machine learning, you have to get your hands dirty by doing projects with real-world data.
That’s it. In this post, I’ve mentioned a roadmap and talked about the 6 steps to becoming a machine learning expert. I hope you enjoy it.
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