Master the basic concepts of data science and machine learning and learn how to implement these concepts successfully in the real-world
Key Features
- Learn the fundamentals of data science, machine learning, and data mining
- Learn interesting techniques to evaluate a machine learning model
- Discover the best practices to solve real-world problems using machine learning
Book Description
Machine learning is the key to development in many areas, such as IT, security, marketing, automation, and even medicine. Without machine learning, it is impossible to build intelligent applications and devices, such as Alexa, Siri, and Google Assistant. This course will help to get familiar with data science and machine learning.
The course starts with an introduction to data science, explaining different terms associated with it. You will also become familiar with machine learning and data science modeling and explore the key differences between model parameters and hyperparameters. Next, you will become familiar with the concepts of machine learning models, such as linear regression, decision trees, random forests, neural networks, and clustering techniques. Towards the end, you will learn how to evaluate machine learning models and learn the best practices to succeed in your data scientist role.
By the end of this course, you will have a solid understanding of data science and machine learning fundamentals.
What you will learn
- Become familiar with data science and machine learning terms
- Distinguish between model parameters and hyperparameters
- Distinguish between supervised and unsupervised learning
- Discover how decision trees, bagging, and random forest works
- Understand the importance of the k-nearest neighbors (KNN) algorithm in machine learning
- Learn about neural networks and clustering techniques
- Evaluate the performance of machine learning models
Who this book is for
This course is designed for students and beginners who want to understand the concepts, statistics, and math behind machine learning algorithms and for those who are curious to solve real-world problems using machine learning and data science. Everything is taught from scratch; hence, there are no prerequisites to get started with this course.
