Who this course is for:

• Beginners seeking to understand the fundamentals of statistics.
• Developers interested in statistics and probability for machine learning.
• Individuals looking for a probability course for machine learning.
• Those who want to grasp the concepts of statistics and probability for data science.
• Enthusiasts interested in the intersection of machine learning and probability.
• Developers eager to explore probability in machine learning.
• Anyone looking to enhance their skills with probability for machine learning.
• Individuals interested in statistics for machine learning.

What you’ll learn:

• Essential concepts in the fundamentals of statistics course.
• Crucial subjects required for statistics and probability for machine learning.
• Distributions and their significance.
• The foundational role of entropy in all Machine Learning.
• Introduction to Bayesian Inference.
• Application of these concepts through coding.

Requirements:

• Basic coding knowledge.
• No extensive mathematical background required (beyond basic arithmetic).
• A Python crash course is provided in the course content.

In today’s world, everyone wants to excel in computer science, machine learning and data science, and for good reason. Data has become the new oil, and the ability to work with it is crucial. However, becoming proficient in the field can be challenging, as the latest and most advanced models may seem overly complex.

But here’s the key: with a solid grasp of probability and statistics, these models become much more accessible. Moreover, probability is a fundamental concept across various areas of computer science, including simulation, vision, game development, and AI. Therefore, building a strong foundation in this subject can open many doors in your career.

This course is designed with the goal of providing you the robust foundation necessary to excel in all areas of computer science, particularly data science and machine learning. The challenge with many probability and statistics courses is that they tend to be overly theoretical, delving deep into mathematics without highlighting the practical applications.

In this course, we take a code-oriented approach, emphasizing the application of all concepts through coding. We avoid the unnecessary theories that aren’t relevant to computer science and instead focus on concepts vital for data science, machine learning, and other computer science fields.

For instance, many probability courses often skip over Bayesian inference, a concept of immense importance. In our course, we address this concept promptly and give it the attention it deserves, as it’s considered the future of analysis.

This approach allows you to grasp the most crucial concepts in this subject in the shortest time possible, without getting bogged down by less relevant details. Once you have developed an intuitive understanding of the essential topics, you’ll be better equipped to explore the latest and most advanced models, even on your own.

Our Promise to You

By the end of this course, you will have a thorough understanding of probability and statistics.

10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about foundations needed to excel in all areas of computer science.