Machine Learning Algorithms, Tutorial

Machine Learning Algorithms, Tutorial, available at $19.99, has an average rating of 3.95, with 69 lectures, based on 56 reviews, and has 12882 subscribers.

You will learn about Applications of Machine Learning to various data, Unsupervised Learning, Supervised Learning This course is ideal for individuals who are python programmers, C/C++ programmers, working of scripting (like javascript), fresh developers and intermediate level programmers who want to learn Machine Learning It is particularly useful for python programmers, C/C++ programmers, working of scripting (like javascript), fresh developers and intermediate level programmers who want to learn Machine Learning.

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Summary

Title: Machine Learning Algorithms, Tutorial

Price: $19.99

Average Rating: 3.95

Number of Lectures: 69

Number of Published Lectures: 69

Number of Curriculum Items: 69

Number of Published Curriculum Objects: 69

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

Applications of Machine Learning to various data, Unsupervised Learning, Supervised Learning

Who Should Attend

python programmers, C/C++ programmers, working of scripting (like javascript), fresh developers and intermediate level programmers who want to learn Machine Learning

Target Audiences

python programmers, C/C++ programmers, working of scripting (like javascript), fresh developers and intermediate level programmers who want to learn Machine Learning

The course covers Machine Learning in exhaustive way. The presentations and hands-on practical are made such that it’s made easy. The knowledge gained through this tutorial series can be applied to various real world scenarios.

UnSupervised learning does not require to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data. The machine is forced to build a compact internal representation of its world and then generate imaginative content.

Supervised learning deals with providing input data as well as correct output data to the machine learning model. The goal of a supervised learning algorithm is to find a mapping function to map the input with the output. It infers a function from labeled training data consisting of a set of training examples.

UnSupervised Learning and Supervised Learning are dealt in-detail with lots of bonus topics.

The course contents are given below:

Introduction to Machine Learning
Introductions to Deep Learning
Installations
Unsupervised Learning
Clustering, Association
Agglomerative, Hands-on
(PCA: Principal Component Analysis)
DBSCAN, Hands-on
Mean Shift, Hands-on
K Means, Hands-on
Association Rules, Hands-on
Supervised Learning
Regression, Classification
Train Test Split, Hands-on
k Nearest Neighbors, Hands-on
kNN Algo Implementation
Support Vector Machine (SVM), Hands-on
Support Vector Regression (SVR), Hands-on
SVM (non linear svm params), Hands-on
SVM kernel trick, Hands-on
SVM mathematics
Linear Regression, Hands-on
Gradient Descent overview
One Hot Encoding (Dummy vars)
One Hot Encoding with Linear Regr, Hands-on
Naive Bayes Overview
Bayes’ Concept , Hands-on
Naive Bayes’ Classifier, Hands-on
Logistic Regression Overview
Binary Classification Logistic Regression
Multiclass Classification Logistic Regression
Decision Tree
ID3 Algorithm – Classifier
ID3 Algorithm – Regression
Info about Datasets

Course Curriculum

Chapter 1: Introduction

Lecture 1: Machine Learning Course Contents

Lecture 2: Contents update

Lecture 3: Machine Learning Introduction

Lecture 4: Deep Learning Introduction

Lecture 5: Prerequisite-Installations

Chapter 2: Python & NumPy

Lecture 1: Python contents

Lecture 2: Development Environment and Installation

Lecture 3: Variables and Numbers in Python (with Practical)

Lecture 4: Strings in Python (with Practical)

Lecture 5: Lists in Python (with Practical)

Lecture 6: Conditional Execution (with Practical)

Lecture 7: Loops (with Practical)

Lecture 8: Functions (with Practical)

Lecture 9: Dictionaries in Python (with Practical)

Lecture 10: Tuples in Python (with Practical)

Lecture 11: Exceptions and it's Handling

Lecture 12: Exceptions and it's Handling (with Practical)

Lecture 13: Iterators (with Strings, List, Dictionary, Tuple)

Lecture 14: Iterators Practical (with Strings, List, Dictionary, Tuple)

Lecture 15: File Support (with Practical) – part 1

Lecture 16: File Support (with Practical) – part 2

Lecture 17: JSON support (with Practical)

Lecture 18: NumPy with Practical (part 1)

Lecture 19: NumPy with Practical (part 2)

Chapter 3: UnSupervised Machine Learning

Lecture 1: Unsupervised Machine Learning – Overview

Lecture 2: Hierarchical Clustering : Agglomerative Clustering

Lecture 3: Agglomerative Clustering (Demo1, Practical)

Lecture 4: Agglomerative Clustering (Demo2, Practical)

Lecture 5: DBSCAN: Density based method

Lecture 6: DBSCAN- How to select eps (with Practical)

Lecture 7: DBSCAN- Algorithm (with Practical)

Lecture 8: Mean Shift Algorithm

Lecture 9: Mean Shift Algorithm with Practical (1)

Lecture 10: Mean Shift Algorithm with Practical (2)

Lecture 11: K Means Algorithm

Lecture 12: K Means Algorithm with Practical (1)

Lecture 13: K Means Algorithm with Practical (2)

Lecture 14: Association Rules

Lecture 15: Association Rules (with Practical)

Chapter 4: Supervised Machine Learning

Lecture 1: Supervised Machine Learning – Overview

Lecture 2: Generating Training and Test Data (Train Test Split)

Lecture 3: K Nearest Neighbors (kNN) Algorithm

Lecture 4: kNN Algorithm with Practical

Lecture 5: kNN : Nearest Neighbors Implementation with Practical

Lecture 6: Support Vector Machine (SVM)

Lecture 7: SVM – Practical (linear)

Lecture 8: SVM – Support Vector Regression (SVR) with Practical

Lecture 9: SVM – Mathematics (Hyperplane)

Lecture 10: Non Linear SVM parameters (with Practical)

Lecture 11: SVM kernel trick for not linearly separable data (with Practical)

Lecture 12: Linear Regression

Lecture 13: Linear Regression (with Practical)

Lecture 14: Gradient Descent Overview

Lecture 15: One Hot Encoding (Dummy Variables)

Lecture 16: One Hot Encoding (with Practical)

Lecture 17: Naive Bayes' – Overview

Lecture 18: Naive Bayes' Concept – Demo

Lecture 19: Naive Bayes' – Demo (1)

Lecture 20: Naive Bayes' – Demo (2)

Lecture 21: Naive Bayes' – Assignment

Lecture 22: Logistic Regression – Overview

Lecture 23: Binary Classification, Logistic Regression – Demo

Lecture 24: Multiclass Classification, Logistic Regression – Demo

Lecture 25: ID3 Algorithm – Overview

Lecture 26: ID3 Algo Classifier – Demo

Lecture 27: ID3 Algo Regressor – Demo

Lecture 28: Decision Tree – Overview

Lecture 29: Decision Tree – Demo

Lecture 30: Information about DataSets

Instructors

Shrirang Korde
Technologist

Rating Distribution

1 stars: 1 votes
2 stars: 2 votes
3 stars: 11 votes
4 stars: 18 votes
5 stars: 24 votes

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