Mastering Data Science and Machine Learning Fundamentals

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This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

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26 on-demand videos & exercises
Level: All Levels
1hrs 48mins
Access on mobile, web and TV

What to know about this course

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's this course 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'll 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.

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.

Course Curriculum

About the Author

AI Sciences

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.

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