This is the code repository for Fundamentals of Machine Learning with scikit-learn [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge.

In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are: Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. In this course, you will also learn how these algorithms work and their practical implementation to resolve your problems.

To fully benefit from the coverage included in this course, you will need:
This course is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.

This course has the following software requirements:

• Python 2.7 and 3.3
  
• Scikit-learn machine learning framework
  

• Hands-On Machine Learning with Python and Scikit-Learn [Video]

• Machine Learning with scikit-learn and Tensorflow [Video]

• Introduction to ML Classification Models using scikit-learn [Video]