Statistics for AI ML Developers

Statistics for AI & ML Developers, available at $49.99, has an average rating of 3.83, with 38 lectures, 5 quizzes, based on 9 reviews, and has 180 subscribers.

You will learn about Learn the statistics required to be a successful AI & ML developer Learn the data distribution techniques used in ML Learn foundational information theory and data analysis Learn how to use these concepts to build machine learning models This course is ideal for individuals who are Anyone who wants to be an expert machine learning engineer will find this course very useful It is particularly useful for Anyone who wants to be an expert machine learning engineer will find this course very useful.

Enroll now: Statistics for AI & ML Developers

Summary

Title: Statistics for AI & ML Developers

Price: $49.99

Average Rating: 3.83

Number of Lectures: 38

Number of Quizzes: 5

Number of Published Lectures: 38

Number of Published Quizzes: 5

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the statistics required to be a successful AI & ML developer

Learn the data distribution techniques used in ML

Learn foundational information theory and data analysis

Learn how to use these concepts to build machine learning models

Who Should Attend

Anyone who wants to be an expert machine learning engineer will find this course very useful

Target Audiences

Anyone who wants to be an expert machine learning engineer will find this course very useful

Learn The Necessary Skills To Become An AI& ML Specialist!

Only Memorizing formulas or repeating the computation exercises is thing of the past! To become a complete AI specialist, learn the essential aspect of statistics. This program focuses on concepts like data visualization and practical applications. Also, this program will help you learn the tools like jupyter notebook and Google colab which enables you to code solutions and and build on popular ML models.

Through this program, you get to learn basic concepts of statistics like inferential statistics, vocabulary, hypothesis testing, and machine learning. These concepts will you learn to build valid and accurate models. This is a must learn course for serious ML developers.

Major Concepts That You’ll Learn!

Introduction to statistics for A.I.

Data distributions and introduction to inferential statistics

Inferential statistics and Hypothesis Testing

Introduction to Machine Learning

Information Theory, Data Analysis and Machine Learning Models

The field of Artificial Intelligence works on the prediction basis and patterns in structures using data. Statistics act as a foundation while analyzing and dealing with data in machine learning. This program will give you a brief knowledge of how statistics helps build and deploy AI models.

Perks Of Availing This Program!

Get Well-Structured Content

Learn From Industry Experts

Learn Trending Machine Learning Tool & Technologies

So why are you waiting? make your move to become an AI specialist now.

See You In The Class!

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Introduction

Chapter 2: Introduction to statistics for A.I.

Lecture 1: Section Overview

Lecture 2: Introduction to Jupyter notebooks and google colab

Lecture 3: Vocabulary & Descriptive Statistics

Lecture 4: Measures of spread, statistical tests, and the null hypothesis

Lecture 5: Section Summary

Chapter 3: Data distributions and introduction to inferential statistics

Lecture 1: Section Overview

Lecture 2: Introduction to data distributions

Lecture 3: Normal distribution and the cumulative distribution function

Lecture 4: Qualities, percent point function and general distribution

Lecture 5: Students T uniform and exponential distribution

Lecture 6: Binomial, Chi-squared and F distributions

Lecture 7: Introduction to Inferential Statistics

Lecture 8: Section Summary

Chapter 4: Inferential statistics and Hypothesis Testing

Lecture 1: Section Overview

Lecture 2: Inderential Statistics and Model Interpretability

Lecture 3: Parameteric Methods Introduction to Hypothesis testing

Lecture 4: Hypothesis Testing Continued Theorems and Confidence Intervals

Lecture 5: Confidence Intervals Continued

Lecture 6: Hypothsis testing

Lecture 7: Students t Test for Independent and Dependent Samples

Lecture 8: Section Summary

Chapter 5: Introduction to Machine Learning

Lecture 1: Section Overview

Lecture 2: Differences Between Machine Learning and Inferential Statistics

Lecture 3: The Statistical Basis for Machine Learning

Lecture 4: Practical Aspects of Machine Learning

Lecture 5: Unsupervised Model: Principal Component Analysis

Lecture 6: Markov Chain, Transition Matrix, and Stationarity

Lecture 7: The Bias-Variance Trade-Off

Lecture 8: Section Summary

Chapter 6: Information Theory, Data analysis and Machine Learning Models

Lecture 1: Section Overview

Lecture 2: Information Theory, Entropy, Self-Information

Lecture 3: Entropy

Lecture 4: Exploratory Data analysis, Simple Heuristic Models for prediction

Lecture 5: Data cleanup, Stochastic Gradient Descent

Lecture 6: Machine Learning Models

Lecture 7: Model Validation

Lecture 8: Section Summary

Instructors

Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 3 votes

4 stars: 3 votes

5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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