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Development Data Science Machine Learning

Machine Learning Real World projects in Python

Build 3 Practical Real World Projects and become a master in Machine Learning
Rating: 4.5 out of 54.5 (131 ratings)
29,704 students
Created by Shan Singh
Last updated 3/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Machine Learning Practical Applications
  • How to apply Machine learning in Real Life Challenges

Course content

5 sections • 61 lectures • 10h 21m total length

  • Preview04:03
  • How to follow this course-Must Watch
    02:06
  • Installation of Anaconda Navigator
    03:46
  • Quick Summary of Jupyter Notebook
    05:47

  • Life Cycle of Machine Learning Project
    15:27

  • Introduction to Business Problem & Dataset
    01:53
  • Datasets & Resources
    00:01
  • Preview10:18
  • Preview14:13
  • Preview12:49
  • Analysing Demand Of hotels
    07:50
  • Select Important features using Machine learning
    13:09
  • How to extract Derived features from data
    07:49
  • Preview10:14
  • How to Handle Outliers
    07:32
  • Applying Techniques of Feature Importance
    10:29
  • Intuition behind Logistic Regression --part 1
    13:52
  • Intuition behind Logistic Regression --part 2
    14:42
  • Idea Behind Cross Validation- Part 1
    10:45
  • Idea Behind Cross Validation- Part 2
    13:01
  • Applying logistic regression on data & cross-validate it
    09:40
  • Intuition Behind Decision Tree- Part 1
    10:02
  • Intuition Behind Decision Tree- Part 2
    17:29
  • Intuition Behind Decision Tree- Part 3
    14:58
  • Intuition Behind Decision Tree- Part 4
    17:56
  • Intuition Behind Decision Tree- Part 5
    15:27
  • Intuition Behind Decision Tree- Part 6
    10:41
  • Intuition Behind Random Forest Part-1
    20:39
  • Intuition Behind Random Forest Part-2
    11:38
  • Intuition Behind KNN- Part 1
    12:34
  • Intuition Behind KNN- Part 2
    08:18
  • Intuition Behind KNN- Part 3
    07:28
  • Intuition Behind KNN- Part 4
    07:56
  • Applying Multiple algorithms on data
    10:40

  • Datasets & Resources
    00:01
  • Prepare your data for Analysis & Modelling
    11:46
  • How to clean your data
    10:12
  • Analysing Distributions of your data
    09:28
  • How to check co-relation in data
    09:31
  • How to Automate your Analysis
    10:03
  • Perform Exploratory Data Analysis on data..
    09:56
  • How to come across with missing values in data
    14:31
  • Clean your missing values using Random Value Imputation
    06:28
  • Applying feature Encoding on data
    05:35
  • How to Select best features for your model
    12:10
  • Building a Cross-validated Model & checking its accuracy
    16:54

  • Introduction to Business Problem & Dataset
    01:24
  • Datasets & Resources
    00:01
  • Understand your data
    11:06
  • How to extract Derived features from data
    10:59
  • Perform Data Pre-processing
    08:04
  • Handle Categorical Data & Feature Encoding
    12:09
  • How to Perform Label Encoding on dataset
    14:26
  • Outliers Detection in Data
    07:53
  • Select best Features using Feature Selection Technique
    04:57
  • Apply Random Forest on Data & Automate your predictions
    10:40
  • Intuition Behind Linear Regression- Part 1
    11:33
  • Intuition Behind Linear Regression- Part 2
    09:50
  • Intuition Behind Linear Regression- Part 3
    20:16
  • Play with multiple Algorithms & dumping your model
    07:56
  • How to Cross Validate your model
    18:04

Requirements

  • Basic knowledge of Python programming is recommended.

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!

Who this course is for:

  • Data Scientists who want to apply their knowledge on Real World Case Studies
  • Machine Learning Enthusiasts who look to add more projects to their Portfolio

Instructor

Shan Singh
Data Scientist
Shan Singh
  • 4.4 Instructor Rating
  • 1,321 Reviews
  • 124,608 Students
  • 10 Courses

Professionally, I am a Data Scientist having experience of 6 years in finance, retail and transport.From my courses you will straight away notice how I combine my own experience to deliver content in a easiest fashion. To sum up, I am absolutely passionate about Data Analytics and I am looking forward to sharing my own knowledge with you!

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