Machine Learning Real-World Projects in Python
Self-paced video training — 3 modules, 45 lessons. Work through it at your own pace — every lesson is real recorded training, not slide-reading.
▶INSIDE THE COURSE · 45 LESSONSABOUT THIS COURSE
In this advanced course, students will work on real-world projects to apply machine learning techniques using Python. They will learn to analyze datasets related to hotel bookings and airline ticket pricing, preparing data for modeling, handling categorical data, and extracting features. By completing these projects, students will gain practical experience in solving business problems through data analysis and predictive modeling.
CURRICULUM
3 modules, in order.
01Intro to this course3 LESSONS
learn about the benefits of taking this machine learning course.
discover essential tips on how to effectively follow the course.
set up Anaconda Navigator for your machine learning projects.
02Project 1-- Predict status of Hotel Booking28 LESSONS
introduction to the business problem and dataset for hotel booking.
prepare your dataset for analysis and modeling in this project.
analyze the home country distribution of hotel guests.
examine hotel pricing trends over the years.
analyze the demand for hotels based on various factors.
identify important features using machine learning techniques.
learn how to extract derived features from your dataset.
understand methods for handling categorical data in your analysis.
explore techniques for handling outliers in your dataset.
apply feature importance techniques to enhance your model.
gain insight into the basics of logistic regression.
delve deeper into the concepts of logistic regression.
03Project 2-- Predict Prices of Airline Tickets14 LESSONS
get introduced to the business problem dataset for predicting airline ticket prices.
learn how to analyze and understand your dataset effectively.
discover methods to extract derived features from your dataset.
perform necessary data pre-processing steps for your airline ticket dataset.
manage categorical data by applying feature encoding techniques.
learn how to perform label encoding on your dataset.
detect outliers in your data to improve model accuracy.
use feature selection techniques to identify the best features for your model.
automate predictions by applying random forest to your dataset.
understand the fundamentals of linear regression in machine learning.
explore more concepts behind linear regression for a deeper understanding.
gain further insight into linear regression with additional discussions.
CURATED BY
Selected from experienced educators' training libraries and kept whole — modules, worked examples and walkthroughs exactly as taught.
GOOD TO KNOW
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Watch on two devices at once — phone, tablet or desktop.
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$79 · ONE PAYMENT
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