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Machine Learning Classification Bootcamp in Python

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn
(798 reviews)
7,897 students
Created by


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Platform: Udemy
Video: 11h 43m
Language: English
Next start: On Demand

Table of contents


Are you ready to master Machine Learning techniques and Kick-off your career as a Data Scientist?!
You came to the right place!
Machine Learning skill is one of the top skills to acquire in 2019 with an average salary of over $114,000 in the United States according to PayScale! The total number of ML jobs over the past two years has grown around 600 percent and expected to grow even more by 2020.
This course provides students with knowledge, hands-on experience of state-of-the-art machine learning classification techniques such as
•Logistic Regression
•Decision Trees
•Random Forest
•Naïve Bayes
•Support Vector Machines (SVM)
In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques. We are going to build 10 projects from scratch using real world dataset, here’s a sample of the projects we will be working on:
•Build an e-mail spam classifier.
•Perform sentiment analysis and analyze customer reviews for Amazon Alexa products.
•Predict the survival rates of the titanic based on the passenger features.
•Predict customer behavior towards targeted marketing ads on Facebook.
•Predicting bank client’s eligibility to retire given their features such as age and 401K savings.
•Predict cancer and Kyphosis diseases.
•Detect fraud in credit card transactions.
Key Course Highlights:
•This comprehensive machine learning course includes over 75 HD video lectures with over 11 hours of video content.
•The course contains 10 practical hands-on python coding projects that students can add to their portfolio of projects.
•No intimidating mathematics, we will cover the theory and intuition in clear, simple and easy way.
•All Jupyter noteboooks (codes) and slides are provided. 
•10+ years of experience in machine learning and deep learning in both academic and industrial settings have been compiled in this course. 
Students who enroll in this course will master machine learning classification models and can directly apply these skills to solve real world challenging problems.

You will learn

✓ Apply advanced machine learning models to perform sentiment analysis and classify customer reviews such as Amazon Alexa products reviews
✓ Understand the theory and intuition behind several machine learning algorithms such as K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
✓ Implement classification algorithms in Scikit-Learn for K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
✓ Build an e-mail spam classifier using Naive Bayes classification Technique
✓ Apply machine learning models to Healthcare applications such as Cancer and Kyphosis diseases classification
✓ Develop Models to predict customer behavior towards targeted Facebook Ads
✓ Classify data using K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
✓ Build an in-store feature to predict customer’s size using their features
✓ Develop a fraud detection classifier using Machine Learning Techniques
✓ Master Python Seaborn library for statistical plots
✓ Understand the difference between Machine Learning, Deep Learning and Artificial Intelligence
✓ Perform feature engineering and clean your training and testing data to remove outliers
✓ Master Python and Scikit-Learn for Data Science and Machine Learning
✓ Learn to use Python Matplotlib library for data Plotting


• Basic knowledge of Python Programming
• Experienced computer user

This course is for

• Data Science Enthusiasts wanting to enhance their machine learning skills
• Python programmers curious about Machine Learning and Data Science
• Programmers or developers who want to make a shift into the lucrative data science and machine learning career path
• Technologists wanting to gain an understanding of how machine learning models work
• Data analysts who want to transition into the Tech industry
Professor & Best-selling Instructor, 250K+ students
Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan’s mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. 
Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 280,000+ students globally. He has over 25 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. 
Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.
* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.

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Platform: Udemy
Video: 11h 43m
Language: English
Next start: On Demand

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