Machine Learning Real World projects in Python
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- Curriculum
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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!
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Introduction to Business Problem & Dataset -
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Datasets & Resources -
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Prepare your data for Analysis & Modelling
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8
Analysing Home country of guests
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Analysing Prices of Hotels across year
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10
Analysing Demand Of hotels
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11
Select Important features using Machine learning
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12
How to extract Derived features from data
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13
How to handle Categorical data
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14
How to Handle Outliers
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15
Applying Techniques of Feature Importance
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16
Intuition behind Logistic Regression --part 1
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Intuition behind Logistic Regression --part 2
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18
Idea Behind Cross Validation- Part 1
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Idea Behind Cross Validation- Part 2
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20
Applying logistic regression on data & cross-validate it
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Intuition Behind Decision Tree- Part 1
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Intuition Behind Decision Tree- Part 2
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Intuition Behind Decision Tree- Part 3
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Intuition Behind Decision Tree- Part 4
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Intuition Behind Decision Tree- Part 5
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Intuition Behind Decision Tree- Part 6
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27
Intuition Behind Random Forest Part-1
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Intuition Behind Random Forest Part-2
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29
Intuition Behind KNN- Part 1
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30
Intuition Behind KNN- Part 2
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Intuition Behind KNN- Part 3
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Intuition Behind KNN- Part 4
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33
Applying Multiple algorithms on data
