Udemy

Platform

English

Language

Data Science

Category

Machine Learning Real World projects in Python

Build 3 Practical Real World Projects and become a master in Machine Learning

4.52 (21 reviews)

Machine Learning Real World projects in Python

13194

Students

总共 10 小时

Content

Feb 2021

Last Update
$19.99
Regular Price

What you will learn

Machine Learning Practical Applications

How to apply Machine learning in Real Life Challenges


Description

Machine Learning is one of the hottest technology field in the world right now! This field is exploding with opportunities and career prospects. Machine Learning techniques are widely used in several sectors now a days such as banking, healthcare, finance, education transportation and technology.


This course covers several technique in a practical manner, the projects include coding sessions as well as Algorithm Intuition:
So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because in a practical life, machine learning seems to be complex and tough,thats why we’ve designed a course to help break it down into real world use-cases that are easier to understand.

1.Task #1 @Predicting the Hotel booking  : Predict Whether booking  is going to cancel or not

3.Task #2 @Predict Whether Person has a Chronic Disease or not: Develop a Machine learning  Model that predicts whether person has kidney disease or not

2.Task #3 @Predict the Prices of Flight: Predict the prices of Flght using Regression & Ensemble Algorithms..



The course covers a number of different machine learning algorithms such as Regression and Classification algorithms. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 3 brand new projects that can help you experience the power of Machine Learning using real-world examples!



Content

Intro to this course

Introduction & Course Benefits

How to follow this course-Must Watch

Installation of Anaconda Navigator

Regarding Healthcare Project

Project 1-->> Predict status of Hotel Booking

Introduction to Business Problem & Dataset

Datasets & Resources

Prepare your data for Analysis & Modelling

Analysing Home country of guests

Analysing Prices of Hotels across year

Analysing Demand Of hotels

Select Important features using Machine learning

How to extract Derived features from data

How to handle Categorical data

How to Handle Outliers

Applying Techniques of Feature Importance

Intuition behind Logistic Regression --part 1

Intuition behind Logistic Regression --part 2

Idea Behind Cross Validation- Part 1

Idea Behind Cross Validation- Part 2

Applying logistic regression on data & cross-validate it

Intuition Behind Decision Tree- Part 1

Intuition Behind Decision Tree- Part 2

Intuition Behind Decision Tree- Part 3

Intuition Behind Decision Tree- Part 4

Intuition Behind Decision Tree- Part 5

Intuition Behind Decision Tree- Part 6

Intuition Behind Random Forest Part-1

Intuition Behind Random Forest Part-2

Intuition Behind KNN- Part 1

Intuition Behind KNN- Part 2

Intuition Behind KNN- Part 3

Intuition Behind KNN- Part 4

Applying Multiple algorithms on data

Project 3-->> Predict Prices of Airline Tickets

Introduction to Business Problem & Dataset

Datasets & Resources

Understand your data

How to extract Derived features from data

Perform Data Pre-processing

Handle Categorical Data & Feature Encoding

How to Perform Label Encoding on dataset

Outliers Detection in Data

Select best Features using Feature Selection Technique

Apply Random Forest on Data & Automate your predictions

Intuition Behind Linear Regression- Part 1

Intuition Behind Linear Regression- Part 2

Intuition Behind Linear Regression- Part 3

Play with multiple Algorithms & dumping your model

How to Cross Validate your model




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3833504

Udemy ID

2/8/2021

Course created date

2/12/2021

Course Indexed date
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