Learn Machine Learning Maths Behind

Learn Machine Learning Maths Behind, available at $39.99, has an average rating of 4.05, with 31 lectures, 1 quizzes, based on 18 reviews, and has 3257 subscribers.

You will learn about You will Learn Machine Learning Concept which are used in Enterprise World Learn Theory and Practical of Implementing Custom ML Algorithm This course is ideal for individuals who are Associate Software Consultant or Software Consultant or Senior Consultant or Architect and Solution Architect It is particularly useful for Associate Software Consultant or Software Consultant or Senior Consultant or Architect and Solution Architect.

Enroll now: Learn Machine Learning Maths Behind

Summary

Title: Learn Machine Learning Maths Behind

Price: $39.99

Average Rating: 4.05

Number of Lectures: 31

Number of Quizzes: 1

Number of Published Lectures: 31

Number of Published Quizzes: 1

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

You will Learn Machine Learning Concept which are used in Enterprise World

Learn Theory and Practical of Implementing Custom ML Algorithm

Who Should Attend

Associate Software Consultant

Software Consultant

Senior Consultant

Architect and Solution Architect

Target Audiences

Associate Software Consultant

Software Consultant

Senior Consultant

Architect and Solution Architect

Machine learning and the world of artificial intelligence (AI) are no longer science fiction. They’re here!
Get started with the new breed of software that is able to learn without being explicitly programmed, machine learning can access, analyze, and find patterns in Big Data in a way that is beyond human capabilities. The business advantages are huge, and the market is expected to be worth $47 billion and more by 2020.
In this course, you will implement your own custom algorithm on top of SAP?’s HANA? Database, which is an In-Memory database capable of Performing huge calculation over a large set of Data. We are going to use Native SQL to write the algorithm of Naive Bayes.? Naive Bayes is a classical ML algorithm, which is capable of providing surprising result, it is based out of the probabilistic model and can outperform even complex ML algorithm.

In this course are going to start from basics and move slowly to the implementation of the ML algorithm. We are not using any third party libraries but will be writing the steps in the Native SQL, so our code can take advantage of HANA? DB in-memory capabilities to run faster even when Data Set grows large.

Course Curriculum

Chapter 1: Prerequisite : Machine Learning Basic and Introduction With Naive Bayes

Lecture 1: Machine Learning ( Algorithms) Types – Part 1

Lecture 2: Machine Learning ( Algorithms) Types – Part 2

Lecture 3: 5 Types of Different Problems Which Can be Solved With Machine Learning

Lecture 4: Machine Learning Algorithms and Why It Matters

Lecture 5: Rating Machine Learning Algorithms

Lecture 6: Algorithms We are Going to Cover and What They Can Do

Lecture 7: Starting With Naive Bayes

Lecture 8: How Naive Bayes Works and Proof

Lecture 9: How We Can Say Naive Bayes is Better

Lecture 10: Naive Bayes Graphical Proof

Lecture 11: Expert System With Naive Bayes

Chapter 2: Sprint 4.2 – Machine Learning Model Maths and Implementation on GCP

Lecture 1: General Machine Learning Algorithm Steps – Part 1

Lecture 2: General Machine Learning Algorithm Steps – Part 2

Lecture 3: Common Queries Your Have Regarding ML – Part 1

Lecture 4: Common Queries Your Have Regarding ML – Part 2

Lecture 5: Understanding Maths Side of ML With Example – P, Q and K matrix

Lecture 6: Maths Side of ML With Example – R and R^ Matrix

Lecture 7: Maths Side of ML With Example – Error for R^

Lecture 8: Maths Side of ML With Example – Error Minimization

Lecture 9: Maths Side of ML With Example – Error Minimization

Lecture 10: Maths Side of ML With Example – Final Formula to Reach to Minimum Error

Lecture 11: Maths Side of ML With Example-Graph to Show What is Happening in Iteration Part1

Lecture 12: Maths Side of ML With Example-Graph to Show What is Happening in Iteration Part2

Lecture 13: Over View of the Hands-on Part of ML

Lecture 14: Hand-Book and Spinning Up Spark Cluster

Lecture 15: Uploading the Product Rater File For Upload

Lecture 16: Upload the Product Rater Files to VM and Hadoop

Lecture 17: Upload, Transform and Divide Data into Test and Training

Lecture 18: Starting the Machine Learning Process With ALS Algo

Lecture 19: Evaluate the Root Mean Square Error With Test Data

Lecture 20: Summary of the Machine Learning Section

Instructors

CS PRO
Computer Science for Professional

Rating Distribution

1 stars: 3 votes

2 stars: 0 votes

3 stars: 3 votes

4 stars: 3 votes

5 stars: 9 votes

Frequently Asked Questions

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