Mastering Data Science and Machine Learning Fundamentals: A beginner course in data science, machine learning, regression, classification, and clustering

AI Sciences

NZ$65.99

5 (1 Ratings)

Video Jan 2021 1hr 48mins 1st Edition

AI Sciences

NZ$65.99

5 (1 Ratings)

Video Jan 2021 1hr 48mins 1st Edition

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Key benefits

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

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.

Who is this book 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.

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

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