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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
4.7
☆☆☆☆☆ 4.7/5
(798 reviews)
7,897 students
Created by
Dr. Ryan Ahmed, Ph.D., MBA

9.8

Classbaze Grade®

10.0

Freshness

9.0

Popularity

9.7

Material

Detailed Analysis »

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn
Go to course »
Platform: Udemy
Price: check current price
Video: 11h 43m
Language: English
Next start: On Demand

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Classbaze Rating

Classbaze Grade®

9.8 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness

10.0 / 10
This course was last updated on 6/2022.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Popularity

9.0 / 10
We analyzed factors such as the rating (4.7/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Material

9.7 / 10
Video Score: 9.4 / 10
The course includes 11h 43m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 5 hours 48 minutes of 749 Machine Learning courses on Udemy.
Detail Score: 9.7 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.9 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

4 articles.
1 resources.
0 exercise.
0 test.

In this page

About this course

  The course will teach you this
  What you need to START the course
  Who can start this course?
General questions
  Coupons & Discounts
  Guarantees & money back
  Financial aid
Teacher
Classbaze hand picked recommendations

About the course

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.

What can you learn from this course?

✓ 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

What you need to start the course?

• Basic knowledge of Python Programming
• Experienced computer user

Who is this course is made 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

Are there coupons or discounts for Machine Learning Classification Bootcamp in Python ? What is the current price?

The course costs $17.99. And currently there is a 82% discount on the original price of the course, which was $99.99. So you save $82 if you enroll the course now.
The average price is $13.6 of 749 Machine Learning courses. So this course is 32% more expensive than the average Machine Learning course on Udemy.

Will I be refunded if I'm not satisfied with the Machine Learning Classification Bootcamp in Python course?

YES, Machine Learning Classification Bootcamp in Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any financial aid for this course?

Currently we could not find a scholarship for the Machine Learning Classification Bootcamp in Python course, but there is a $82 discount from the original price ($99.99). So the current price is just $17.99.

Who will teach this course? Can I trust Dr. Ryan Ahmed, Ph.D., MBA?

Dr. Ryan Ahmed, Ph.D., MBA has created 44 courses that got 27,789 reviews which are generally positive. Dr. Ryan Ahmed, Ph.D., MBA has taught 295,730 students and received a 4.6 average review out of 27,789 reviews. Depending on the information available, we think that Dr. Ryan Ahmed, Ph.D., MBA is an instructor that you can trust.

Learn everything about

Dr. Ryan Ahmed, Ph.D., MBA

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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9.8

Classbaze Grade®

10.0

Freshness

9.0

Popularity

9.7

Material

Detailed Analysis »

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn
Go to course »
Platform: Udemy
Price: check current price
Video: 11h 43m
Language: English
Next start: On Demand

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