Machine Learning with Java and Weka
Description
This is the bite size course to learn Java Programming for Machine Learning and Statistical Learning with Weka library. In CRISP DM data mining process, machine learning is at the modeling and evaluation stage.
You will need to know some Java programming, and you can learn Java programming from my "Create Your Calculator: Learn Java Programming Basics Fast" course. You will learn Java Programming for machine learning and you will be able to train your own prediction models with naive bayes, decision tree, knn, neural network, linear regression, and evaluate your models very soon after learning the course.
Basic knowledge
Computer Knowledge
Basic coding knowledge
What will you learn
Content
Introduction
Getting Started
Getting Started 2
Getting Started 3
Data Mining Process
Data set
Split Training and Testing dataset
CReate Java Application using Netbeans with Weka Jar
Simple Linear Regression
LInear Regression using Weka and Java
LInear Regression using Weka and Java 2
LInear Regression using Weka and Java 3
KMeans Clustering
KMeans Clustering in Weka and Java
Agglomeration Clustering
Agglomeration Clustering in Weka and Java
Decision Tree ID3 ALgorithm
Decision Tree in Weka and Java
KNN Classification
KNN in Weka and Java
Naive Bayes Classification
Naive Bayes in Weka and Java
Neural Network Classification
Neural Network in Weka and Java
What Algorithm to Use?
Model Evaluation
Model Evaluation in Weka and Java
CReate a Data Mining Software
CReate a Data Mining Software 2
To learn more:
This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.
Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial
It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.
Below topics are explained in this Machine Learning course for beginners:
Basics of Machine Learning - 01:46
Why Machine Learning - 09:18
What is Machine Learning - 13:25
Types of Machine Learning - 18:32
Supervised Learning - 18:44
Reinforcement Learning - 21:06
Supervised VS Unsupervised - 22:26
Linear Regression - 23:38
Introduction to Machine Learning - 25:08
Application of Linear Regression - 26:40
Understanding Linear Regression - 27:19
Regression Equation - 28:00
Multiple Linear Regression - 35:57
Logistic Regression - 55:45
What is Logistic Regression - 56:04
What is Linear Regression - 59:35
Comparing Linear & Logistic Regression - 01:05:28
What is K-Means Clustering - 01:26:20
How does K-Means Clustering work - 01:38:00
What is Decision Tree - 02:15:15
How does Decision Tree work - 02:25:15
Random Forest Tutorial - 02:39:56
Why Random Forest - 02:41:52
What is Random Forest - 02:43:21
How does Decision Tree work- 02:52:02
K-Nearest Neighbors Algorithm Tutorial - 03:22:02
Why KNN - 03:24:11
What is KNN - 03:24:24
How do we choose 'K' - 03:25:38
When do we use KNN - 03:27:37
Applications of Support Vector Machine - 03:48:31
Why Support Vector Machine - 03:48:55
What Support Vector Machine - 03:50:34
Advantages of Support Vector Machine - 03:54:54
What is Naive Bayes - 04:13:06
Where is Naive Bayes used - 04:17:45
Top 10 Application of Machine Learning - 04:54:48
How to become a Machine Learning Engineer - 04:59:46
Machine Learning Interview Questions - 05:09:03
This Machine Learning tutorial for beginners will enable you to learn Machine Learning algorithms with python examples. Become a pro in Machine Learning.
Mastering the Machine Learning Course would easily develop one's career. This is the reason why studying Machine Learning Tutorial becomes so important in the career of a particular student.
Making a part of the machine learning course would enact and studying the Machine Learning Tutorial would make one carve out a new niche.
Machine learning is changing the dimensions of business in many industries. A report projects that the value added by machine learning systems shall reach up to $3.9 Trillion by 2022.Machine lear...
Machine learning is proving it's worth in many industries like manufacturing, financial services, healthcare, and retail, to name a few. We hope that we have dispelled some of the myths associated with Machine Learning. It wouldn't be MLan incorrect to say that we have both overestimated and underestimated the potential of Machine learning systems.