English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3.5 Hours | 3.59 GB
Learn and Implement Your Own Custom Machine Learning Algorithm on Top of SAP®’s HANA® In Memory System
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.
What Will I Learn?
- You will Learn Machine Learning Concept which are used in Enterprise World
]Learn Theory and Practical of Implementing Custom ML Algorithm
Table of Contents
Prerequisite Machine Learning Basic and Introduction With Naive Bayes
1 Machine Learning ( Algorithms) Types – Part 1
2 Machine Learning ( Algorithms) Types – Part 2
3 Types of Different Problems Which Can be Solved With Machine Learning
4 Machine Learning Algorithms and Why It Matters
5 Rating Machine Learning Algorithms
6 Algorithms We are Going to Cover and What They Can Do
7 Starting With Naive Bayes
8 How Naive Bayes Works and Proof
9 How We Can Say Naive Bayes is Better
10 Naive Bayes Graphical Proof
11 Expert System With Naive Bayes
Sprint 4.2 – Machine Learning Model Maths and Implementation on GCP
12 General Machine Learning Algorithm Steps – Part 1
13 General Machine Learning Algorithm Steps – Part 2
14 Common Queries Your Have Regarding ML – Part 1
15 Common Queries Your Have Regarding ML – Part 2
16 Understanding Maths Side of ML With Example – P, Q and K matrix
17 Maths Side of ML With Example – R and R^ Matrix
18 Maths Side of ML With Example – Error for R^
19 Maths Side of ML With Example – Error Minimization
20 Maths Side of ML With Example – Error Minimization
21 Maths Side of ML With Example – Final Formula to Reach to Minimum Error
22 Maths Side of ML With Example-Graph to Show What is Happening in Iteration Part1
23 Maths Side of ML With Example-Graph to Show What is Happening in Iteration Part2
24 Over View of the Hands-on Part of ML
25 Hand-Book and Spinning Up Spark Cluster
26 Uploading the Product Rater File For Upload
27 Upload the Product Rater Files to VM and Hadoop
28 Upload, Transform and Divide Data into Test and Training
29 Starting the Machine Learning Process With ALS Algo
30 Evaluate the Root Mean Square Error With Test Data
31 Summary of the Machine Learning Section
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