How to Start with Machine Learning?

Career opportunities in machine learning and data science have rapidly increased in recent years. World Economic Forum, according to a study conducted by the World Economic Forum, the demand for artificial intelligence and machine learning experts is expected to increase by 40% is projected. This increase emphasizes the importance for both professionals and enthusiasts to step into the world of machine learning…

If data, automation, and algorithms excite you, machine learning could be a good career choice for you. All you need to start machine learning is research skills, basic statistics and programming languages.

In this article, we explain how to start machine learning, from the basic skills and technologies you need to learn, to the machine learning books you should read. 👇🧐 

What Is Machine Learning?

Machine learning is a field of computer science that involves teaching computers to analyze data. In machine learning, an engineer instructs a computer to collect and interpret data using algorithms. 

Subsequently, the computer makes data abstractions based on this data to make predictions. Data abstraction means reducing data to its basic characteristics and hiding unnecessary details. 

Machine learning is a type of artificial intelligence, but there are differences between them. If you want to read more about the differences between artificial intelligence, machine learning, and deep learning in more detail, we have an article on this. Click the link to read it.

Why Should You Learn Machine Learning?

According to the World Economic Forum, the demand for artificial intelligence and machine learning experts will increase by 40% from 2023 to 2027. Considering the exponentially increasing growth in data generation and the need for data-driven decision-making across all sectors, this is not surprising at all. 

In short, machine learning is the skill of the future. Today's leading companies have made machine learning a central part of their operations. With the rapidly increasing demand for machine learning professionals, the need for professionals who will work in this field is also growing.

Understanding machine learning can offer you a secure and profitable career in the technology industry. The increase in job opportunities in this field also means that you will have freedom to work in the sector of your choice. According to research by the World Economic Forum, artificial intelligence, machine learning, and automation are expected to contribute to the creation of 97 million new jobs by 2025.

What Does a Machine Learning Engineer Do?

An ML engineer designs, develops, tests, and implements machine learning algorithms on a daily basis. A machine learning engineer uses programming languages such as Python, Scala and Java to perform tasks and automate processes. They use their statistical and programming skills to build and train machine learning models. 

The main responsibilities of a machine learning engineer may include the following 👇:

• ✅ Examines complex computer architectures, data structures, and algorithms,
• ✅ Collaborates with data engineers to design machine learning systems,
• ✅ Manages infrastructure, data, and model pipelines,
• ✅ Ensures the accuracy of datasets,
• ✅ Analyzes large, complex datasets to identify models and gain insights,
• ✅ Develops algorithms based on statistical modeling procedures,
• ✅ Ensures end-to-end understanding of data modeling and evaluation strategies.

Starting Machine Learning from Scratch

Starting machine learning may seem complex, but with a clear plan and the right resources, this journey becomes more manageable. Here's a guide to learning machine learning from scratch:

1. Review the Prerequisites

Before starting machine learning, it's important to build a solid foundation in some basic areas.

• Basic Mathematics Knowledge 👉 Machine learning is based on mathematical principles. In the learning process, machines use branches of mathematics such as linear algebra, calculus, and statistics. For example, matrix operations and linear transformations in linear algebra are quite important for understanding machine learning and deep learning algorithms.
• Basic Statistics Knowledge 👉 Understanding statistical principles enhances your ability to analyze data in machine learning and make meaningful inferences. Knowing concepts such as statistical significance, distributions, regression analysis, and probability estimates is essential for effectively applying various machine learning techniques.
• Willingness to Learn 👉 Machine learning is a discipline that constantly evolves with new innovations, methods, and technologies. Therefore, a forward-looking attitude and a desire for continuous learning are important for anyone who wants to advance or succeed in the field of machine learning.

The level of expertise and proficiency in these fundamental areas varies depending on the machine learning role you are targeting. For example, a data scientist does not need to have an in-depth knowledge of every mathematical principle, while someone focused on developing new machine learning models may require a deeper understanding of mathematics.

In conclusion, you should shape your learning journey according to your career goals and adjust the intensity of your studies in different areas accordingly.

2. Start Developing Your Machine Learning Skills

Statistics 

Statistics is crucial in machine learning for analyzing data, making predictions, and drawing conclusions. Understanding data distributions, statistical tests, and probability theories is fundamental for building and evaluating machine learning models.

Mathematics

Branches of mathematics such as linear algebra, calculus, and probability are necessary to understand the fundamental principles of machine learning algorithms. These mathematical concepts are directly applied in various ML techniques, from optimizing algorithms to understanding the structure of neural networks.

Programming

Programming is the tool that brings machine learning models to life. Python is the most common language due to its simplicity and extensive libraries for machine learning (NumPy, Pandas, Scikit-learn).

Data Structures

Data structures are necessary for efficiently storing, accessing, and processing data. Understanding data structures such as arrays, lists, trees, and graphs can optimize the performance of ML models.

Data Manipulation

Data manipulation is the process of cleaning, transforming, and preparing data for analysis. This skill is vital for machine learning because the quality and format of data directly affect the performance of models.

Machine Learning

Understanding the principles, algorithms, and applications of machine learning is indispensable in this field.

Deep Learning

Deep learning involves training deep neural networks. This skill is especially important for complex tasks such as image and speech recognition and natural language processing.

Software Engineering

Software engineering skills are essential when deploying machine learning models in a production environment. This includes understanding Git version control systems, continuous integration and delivery (CI/CD) practices, Docker container technologies, and Kubernetes orchestration tools.

3. Learn Basic Machine Learning Tools and Packages

There are various tools and packages that facilitate data processing, model building, and algorithm implementation. Python and R, are the most important technologies, each serving different purposes in the ML workflow. Here's a list of Python and R packages you can learn: 

✅ Pandas

✅ Numpy

✅ Scikit-learn

✅ Tensorflow

✅ Keras

✅ PyTorch

✅ Tidymodels

✅ Caret

✅ randomForest

✅ XGBoost

If you want to thoroughly explore the most popular Python libraries, you can also read our article by clicking this link. 

4. Work on Projects

Theory is very important, but nothing can replace hands-on experience. Start with small projects to apply what you've learned, then gradually work on more complex projects. 

There are numerous projects to develop your machine learning skills. Sentiment analysis of product reviews, stock price prediction, object detection, and even music and movie recommendations are great examples of beginner-friendly machine learning projects. 

Start by choosing a suitable project according to your proficiency level and use the right tools like Python and its comprehensive libraries for your projects. Understanding and preparing your data is crucial as it directly affects the effectiveness of your models.

Below are some machine learning project ideas that can help you on your path to ML expertise.

1️⃣ Image Classification

Project Idea: Develop a model that can identify different animal species.

Dataset: Animal images dataset available on Kaggle.

2️⃣ Natural Language Processing (NLP)

Project Idea: Create a model that can perform sentiment analysis on text.

Dataset: Twitter sentiment analysis dataset.

3️⃣ Recommendation Systems

Project Idea: Design a system that offers movie recommendations based on users' preferences.

Dataset: MovieLens dataset.

5. Study ML Algorithms from Scratch

At this stage, you will have the expertise needed to grasp the general application of machine learning algorithms. However, in machine learning, before truly mastering it, you still need to thoroughly study the workings of individual algorithms. 

By the end of your studies, you will understand the machine learning techniques used to load and prepare data and evaluate model capabilities. 

6. Join a Community

The machine learning community is extensive and incredibly supportive. Join forums, meetups, and hackathons to connect with professionals in this field. 

These communities offer invaluable resources, mentorship, and project collaboration opportunities. Engaging with the community accelerates learning, keeps you motivated, and opens up new personal and professional development opportunities. 

You can check out our Discord channel to connect with machine learning professionals, ask your questions, and share your thoughts. 

7. Stay Updated

Machine learning is a constantly evolving field. Therefore, continuous learning is very important. You need to be knowledgeable about the latest research, news, and innovations in the industry. You can check out our newsletter, Codebrew to stay updated.

8. Read Machine Learning Books

Books provide in-depth knowledge and insights from experts in the field. Some of the most effective books you may find useful in machine learning include:

• Machine Learning for Absolute Beginners - Oliver Theobald
• The Hundred-Page Machine Learning Book - Andriy Burkov
• Machine Learning for Dummies - John Paul Mueller, Luca Massaron
• Introduction to Machine Learning with Python: A Guide for Data Scientists - Andreas C. Müller, Sarah Guido
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - Aurélien Géron

For more information, don't forget to check out our article on books you should read in the field of machine learning.