Statistics for AI & ML Developers

Name: Statistics for AI & ML Developers
Rating: 3.9 (10 reviews)

The complete course to master fundamental statistical concepts for machine learning developers

Created byEduonix Learning Solutions

Last updated 4/2022

English

What you'll 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

Course content

6 sections • 38 lectures • 5h 22m total length

Introduction2:03

Section Overview0:42
Introduction to Jupyter notebooks and google colab10:26
Vocabulary & Descriptive Statistics10:31
Explore descriptive statistics to summarize data using central tendency (mean, median, mode) and spread (range, skewness, variability), clarifying population versus sample.
Measures of spread, statistical tests, and the null hypothesis7:59
Examine variance and standard deviation to measure data spread, quartiles to partition data, and how null hypotheses and tests like z, t, chi-square, and p-values guide decisions.
Section Summary1:27
Quiz 1

Section Overview1:58
Introduction to data distributions10:36
Normal distribution and the cumulative distribution function10:10
Qualities, percent point function and general distribution9:29
Student's T uniform and exponential distribution10:47
Binomial, Chi-squared and F distributions10:41
Introduction to Inferential Statistics10:36
Section Summary1:51
Explore probability mass and density functions, normalization, discrete versus continuous data; study distributions, cdfs, and quantiles, extend to t, uniform, exponential, chi-squared, and f distributions, with inference and confidence intervals.
Quiz 2

Section Overview2:16
Inderential Statistics and Model Interpretability11:28
Parameteric Methods Introduction to Hypothesis testing10:33
Hypothesis Testing Continued Theorems and Confidence Intervals12:06
Analyze hypothesis testing with levels of significance and p values. Explore the Shibuya Shivs Inequality Theorem, the Central Limit theorem, and confidence intervals.
Confidence Intervals Continued10:28
Hypothsis testing14:50
Students t Test for Independent and Dependent Samples12:53
Section Summary2:08
Quiz 3

Section Overview1:31
Differences Between Machine Learning and Inferential Statistics10:35
The Statistical Basis for Machine Learning11:10
Practical Aspects of Machine Learning12:15
Unsupervised Model: Principal Component Analysis10:05
Markov Chain, Transition Matrix, and Stationarity11:36
Explore Markov chains, their state transitions and transition matrices, and understand stationary distribution as the equilibrium, time-invariant probabilities governing long-run behavior.
The Bias-Variance Trade-Off9:27
Section Summary1:24
Quiz 4

Section Overview2:45
Explore information theory concepts—entropy, joint entropy, conditional entropy, relative entropy, and cross entropy—and compare models such as random forests, k-nearest neighbors, perceptron, SVM, and Naive Bayes to predict Titanic survivors.
Information Theory, Entropy, Self-Information11:44
Entropy12:18
Exploratory Data analysis, Simple Heuristic Models for prediction11:04
Data cleanup, Stochastic Gradient Descent13:21
Machine Learning Models11:30
Model Validation13:02
Section Summary2:46
Quiz 5

Requirements

Basic knowledge of mathematics and python is required to complete this course

Description

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!

Who this course is for:

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

Statistics for AI & ML Developers

What you'll learn

Explore related topics

Course content

Course Introduction1 lecture • 2min

Introduction to statistics for A.I.5 lectures • 31min

Data distributions and introduction to inferential statistics8 lectures • 1hr 6min

Inferential statistics and Hypothesis Testing8 lectures • 1hr 17min

Introduction to Machine Learning8 lectures • 1hr 8min

Information Theory, Data analysis and Machine Learning Models8 lectures • 1hr 19min

Requirements

Description

Who this course is for: