Machine Learning Classification Bootcamp in Python (Udemy.com)
Created by: Dr. Ryan Ahmed
Produced in 2022
Overall Score : 0 / 100
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- Logistic Regression
- Decision Trees
- Random Forest
- NaAve Bayes
- Support Vector Machines (SVM)
- 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.
- 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.
- 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
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Dr. Ryan Ahmed
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 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 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 Engineering, Science, Technology and Mathematics to over 40,000+ students globally. He has over 15 published journal and conference research papers on AI, Machine learning and EV controls. He is the 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,
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