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A Beginner-Friendly Introduction to Machine Learning With Python

Learn how to use Python for your first machine learning program, and how to retrieve or manually generate training data
Course from BitDegree
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Machine learning basics with Python and the Scikit-learn module
How to make your own machine learning programs
How to predict prices with algorithms
How to classify data automatically
How to apply ML algorithms to solve problems

Machine learning is a phenomenon that can revolutionize the technology industry. In fact, it is already making the impossible possible in many fields. For instance, doctors can now use the computer-aided interpretation of medical images to detect tumors or diseases. Machine learning with Python means that you use Python to work on AI projects. 

Python is commonly chosen because it fulfills the requirements for the development of AI software. For instance, researchers indicate that developers should select languages that are stable, flexible, and have great resources in the form of frameworks or libraries. Python is all that and more. Besides its wide variety of modules, machine learning with Python is also highly beginner-friendly with its syntax, resembling the standard English language. 

What is machine learning? 

In the earlier days, computers were regarded as machines that perform a task they are programmed to and nothing more. For instance, if a computer is designed as a calculator, it won’t play chess with you, and vice versa. After the introduction of machine learning basics, developers saw a new pattern: letting computers learn as humans. An official definition of machine learning would probably sound something like this: a programming type that focuses on letting computers learn automatically from data provided to it. 

Why Python for machine learning? 

The decision to conduct machine learning with Python has merit. From the primary development phase, programmers will notice the advantages of Python. First of all, machine learning is a complicated procedure that might get really confusing. Therefore, it is important to use a language that favors simpler syntax conventions. 

Additionally, machine learning with Python is easier due to the wide range of libraries. For instance, TensorFlow is one of the best candidates, providing advanced numerical computations for your projects. Furthermore, you can choose Keras: a library for building neural networks. With this strong foundation, it is no wonder why so many programmers create projects of machine learning with Python. 

What will you find in this machine learning Python tutorial? 

In this Python machine learning tutorial, I start from the machine learning basics. I will explain the meaning of machine learning and its benefits. Additionally, I provide some materials for the course, lecture notes, and exercise sheets for practice. 

We will start this machine learning with Python tutorial by making our first machine learning program. Furthermore, you need to know that mostly all AI-inspired projects need to have training data. For beginners, this rule is probably one of the most important: every AI-inspired project has the training phase. Since machines learn from examples, I will be showing you how to use a small amount of data to create a machine learning program. 

While the first program we created in this Python machine learning tutorial was based on a manually-written data set, the second one will focus on a larger amount of data. For this purpose, you can use the Scikit-learn module that provides many useful data sets you can use in your projects.

As we go deeper into the machine learning with Python tutorial, we will discuss such concepts as classification, regression, and clustering. All of this theoretical material will be supported with examples that should help you visualize these concepts more clearly.

A Beginner-Friendly Introduction to Machine Learning With Python
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