Published 8/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.37 GB | Duration: 6h 58m
There’s NO better way to spend your free time than to learn how to build practical projects
What you’ll learn
Combinatorics For Probability
Calculate Center Of Distribution
Distributions in Machine Learning
Machine Learning Optimization
Requirements
No experience necessary
Description
Buff your skills to keep your job and get a raise in ANY economic climate. This course BUNDLE keeps your skills sharp and your paycheque up!Probability and Statistics for Machine LearningDistributions in Machine LearningMachine Learning OptimizationThis masterclass is without a doubt the most comprehensive course available anywhere online. Even if you have zero experience, this course will take you from beginner to professional.Frequently Asked QuestionsHow do I obtain a certificate?Each certificate in this bundle is only awarded after you have completed every lecture of the course.Many of our students post their Mammoth Interactive certifications on LinkedIn. Not only that, but you will have projects to show employers on top of the certification.Is this an eBook or videos?The majority of this course bundle will be video tutorials (screencasts of practical coding projects step by step.) We will also have several PDFs and all source code.Can’t I just learn via Google or YouTube?This bundle is much more streamlined and efficient than learning via Google or YouTube. We have curated a massive 5-course curriculum to take you from absolute beginner to starting a high-paying career.How will I practice to ensure I’m learning?With each section there will be a project, so if you can build the project along with us you are succeeding. There is also a challenge at the end of each section that you can take on to add more features to the project and advance the project in your own time.Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvard’s edX, Business Insider and more. Founder and CEO John Bura has been programming since 1997 and teaching since 2002. John has created top-selling applications for iOS, Xbox and more. John also runs SaaS company Devonian Apps, building efficiency-minded software for technology workers like you. Try a course today.
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: 01 (Prerequisite) Introduction to Machine Learning
Lecture 2 00A What Is Machine Learning
Lecture 3 00B Types Of Machine Learning Models
Lecture 4 00C What Is Supervised Learning
Lecture 5 01 How Does A Machine Learning Agent Learn
Lecture 6 02 What Is Inductive Learning
Section 3: 02 (Prerequisite) Introduction to Python
Lecture 7 00. Introduction
Section 4: 02 – 01 Code Python on the Web
Lecture 8 02.01 What is Google Colab
Lecture 9 02.02 What If I Get Errors
Lecture 10 02.03 How Do I Terminate a Session
Section 5: 02 – 02 Python Language Fundamentals
Lecture 11 02. Variables
Lecture 12 03. Type Conversion Examples
Lecture 13 04. Operators
Lecture 14 05. Operators Examples
Lecture 15 06. Collections
Lecture 16 07. Lists
Lecture 17 08. Multidimensional List Examples
Lecture 18 09. Tuples Examples
Lecture 19 10. Dictionaries Examples
Lecture 20 11. Ranges Examples
Lecture 21 12. Conditionals
Lecture 22 13. If Statement Examples
Lecture 23 14. If Statement Variants Examples
Lecture 24 15. Loops
Lecture 25 16. While Loops Examples
Lecture 26 17. For Loops Examples
Lecture 27 18. Functions
Lecture 28 19. Functions Examples
Lecture 29 20. Parameters And Return Values Examples
Lecture 30 21. Classes And Objects
Lecture 31 22. Classes Example
Lecture 32 23. Objects Examples
Lecture 33 24. Inheritance Examples
Lecture 34 25. Static Members Example
Lecture 35 26. Summary And Outro
Section 6: 03 Probability and Statistics for Machine Learning
Lecture 36 01 Probability And Information Theory Overview
Lecture 37 02 Combinatorics For Probability
Lecture 38 03 Law Of Large Numbers
Lecture 39 04 Calculate Center Of Distribution
Section 7: 04 Distributions in Machine Learning
Lecture 40 01 Uniform Distribution
Lecture 41 02 Gaussian Distribution
Lecture 42 03 Log-Normal Distribution
Lecture 43 04 Exponential Distribution
Lecture 44 05 Laplace Distribution
Lecture 45 06 Binomial Distribution
Lecture 46 07 Multinomial Distribution
Lecture 47 08 Poisson Distribution
Section 8: 05 Machine Learning Optimization
Lecture 48 01 Calculate Error Of Machine Learning Model
Anyone who needs to learn the fundamentals required for machine learning
Homepage
https://www.udemy.com/course/probability-and-statistics-for-machine-learning/
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