Build 3 Practical Real World Projects and become a master in Machine Learning
Machine Learning Practical Applications
How to apply Machine learning in Real Life Challenges
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!
English
Language
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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