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.
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