Your Guide to Getting Ahead with Python! Today, several commercial apps and research projects make use of machine learning, but this field is not only meant for big companies with extensive research teams, a beginner can get started, too.
Machine Learning came into prominence in the 1990s, when researchers and scientists started highlighting it as a sub-field of Artificial Intelligence (AI), detailing how such techniques borrow concepts from AI, probability, and statistics, which perform far better when compared to fixed rule-based models requiring a lot of manual time and effort.
Of course, as we have pointed out earlier, Machine Learning didn’t just come out of nowhere in the 1990s. It is a multi-disciplinary field that has gradually evolved over time and is still evolving as we speak.
Meanwhile, Python is growing and becoming a dominant platform to use with machine learning. The primary reason for adopting Python is because it is a general purpose programming language which can be used both for R&D and in production.
Python Machine Learning for Beginners dives into the basics of machine learning using an approachable, well-known programming language, Python.
In this guide, we will review the purpose of Machine Learning and where it applies to the real world and will also cover popular Machine Learning topics, such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
The following are some of the major topics covered in Python Machine Learning for Beginners:
Understanding The Basics of Machine Learning Machine Learning as a Multi-Disciplinary Field The Different Types of Machine Learning Python Ecosystem for Machine Learning Getting Familiar with Python and SciPy Loading Machine Learning Data Understanding Your Data with Descriptive Statistics Understanding Your Data with Visualization Preparing Your Data for Machine Learning Real-World Applications of Machine Learning Best Practices to Follow The Python Ecosystem for Machine Learning discussed inside of Python Machine Learning for Beginners includes SciPy, NumPy, Matplotlib, Pandas, and scikit learn—these provide virtually all of the Machine Learning algorithms.
This book you’re about to read isn’t just your guide to practicing Machine Learning with Python, but also (and most importantly), will show you how to setup your Python Ecosystem for Machine Learning and what versions to use.