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 bner to professional.Frequently Asked Questionshow do I obtain a certificateEach 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 videosThe 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 YouTubeThis 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 bner to starting a high-paying career.How will I practice to ensure I'm learningWith 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 .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 Teate 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

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