Introduction To Machine Learning With Python Chapter 1

Below is my study notes from learning the book Introduction to Machine Learning with Python. Refer to the author’s GitHub repo at https://github.com/amueller/introduction_to_ml_with_python. Used to create scientific plotting in Python Useful tool for data wragling and manipulation similar to R DataFrame and SQL. Also powerful in terms of importing data from a wide range of files. Data package prepared specific for the text book Introductino to Machine Learning with Python by Andreas C.

Müller & Sarah Guido Load iris data from scikit-learn package and explore the data. This book has two goals. The first is to provide an accessible and rigorous introduction to the fundamentals of machine learning, with equal emphasis on both accessibility and rigor. Machine learning is an exciting field that is impacting people's lives in sometimes profound ways. It's important that the field's promise and pitfalls be well and deeply understood by those who take an interest.

The second goal of this book is to be interactive and promote learning through experimentation - thus the use of Python notebooks. Scattered throughout the text is code, some of which generates graphs and tables. There is code that constitutes bare bones but highly understandable implementations of common machine learning algorithms, and there is code that demonstrates machine learning concepts that stand apart from specific algorithms. Feel free to tinker with all of it, trying different inputs or even modifying the code to see what happens. Sometimes there will be ideas for things to try within the text, but surely other ideas will come to mind that are fun and informative to explore. The science and practice of machine learning are like any other human activities, you get better with experience.

The same could be said of machines. Mitchell defined the field of machine learning as building "computer programs that automatically improve with experience" (Mitchell, 1997). This chapter explores some fundamental concepts in and about machine learning, including what machine learning is and how machines can be made to learn, starting with a thought experiment. As the experiment progresses the discussion will become more general and less grounded in a specific scenario. Imagine that you own a restaurant at the foot of the Italian Alps.1 Behind the restaurant is a field with a herd of goats, and beyond that is a forest where you pick wild... The mushrooms are featured in a number of popular dishes, but occasionally you pick ones that are poisonous and your customers get sick.

Business is hurting and something has to be done. Inspiration finally comes in the form of a goat's bleat. You can feed one mushroom from each batch to a goat. If the goat dies you throw them out. If the goat lives they wind up in a signature dish. Business is booming again, but after a few months you realize the fatal flaw with this plan.

You're about to run out of goats! What to do? You sit and stare at a box of mushrooms, one from each batch you've tested with the goats, labeled with whether the mushroom was poisonous or edible, i.e., whether the goat lived or died. It looks like this. The mushrooms have different sizes. Some are tall and thin, others are short and wide.

Though the stems all have the same color, some caps are darker shades than others. Spend a few minutes to see if there is any pattern. Are there any observable features that would allow you to figure out or predict whether a new mushroom, without the label, is poisonous or edible? Finding such a pattern would make it possible to avoid picking poisonous mushrooms in the first place and obviate the need for the goat test. In machine learning terms, you're trying to solve a binary classification problem using a training set of instances. That's a lot of new terminolgy, but the concepts are easy to understand given our thought experiment.

There was an error while loading. Please reload this page. Machine learning has revolutionized the way we approach data-driven problems, enabling computers to learn from data and make predictions or decisions without explicit programming. Python, with its rich ecosystem of libraries and tools, has become the de facto language for implementing machine learning algorithms. Whether you're new to the field or looking to expand your skills, understanding the fundamentals of machine learning and how to apply them using Python is essential. In this comprehensive guide, we will delve into the core concepts of machine learning, explore key algorithms, and learn how to implement them using popular Python libraries like NumPy, Pandas, Matplotlib, and Scikit-Learn.

By the end, you'll have the know Python has emerged as the preferred language for machine learning (ML) for several compelling reasons: Let's dive into a simple example using the famous Iris dataset to classify iris flowers based on their features. Congratulations! You've taken your first steps into the exciting world of machine learning using Python. By mastering the basics and continuously exploring new techniques and datasets, you'll unlock the potential to solve real-world problems and innovate with machine learning.

Embrace the journey of learning and stay curious!

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