[2021] Machine Learning Classification Bootcamp in Python (Udemy.com)

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn

( 99 Reviews )

Created by: Dr. Ryan Ahmed

Produced in 2021

What 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

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 84 / 100

Course Description

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 2021.
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
Nave 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.Who 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

Time: 11.5h

Instructor Details

Dr. Ryan Ahmed

4.2 Rating
99 Reviews

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 50,000+ students globally. He has over 15 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 con

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Reviews

4.2

99 total reviews

5 star 4 star 3 star 2 star 1 star

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By Niranjan Singh

well explained in a very simplified manner. great course

By Rezgui Sofien

not at all

a lot of repetition in many lectures

By Henri Tuthill

Only through the first 5 lessons for this review. Instructor provides excellent recap of lecture material. Explanations are detailed and clearly articulated. Look forward to the rest of the course.

By Nikhil Rao

I have just finished the logistic regression section and I am somewhat disappointed. I had hoped for a more in-depth coverage of the algorithm using scikit-learn, but it turned out that the instructor just types out the specific commands and does not really go into the details of what's happening under the hood. Knowing what to type is not the real challenge; it's knowing what the model is doing which really counts. There are so many parameters that can be used/tweaked for a particular model, but none of those things are covered. So, the essence of the course appears to be missing.

While this course is fine to get introduced to the topics, I believe the coverage should have been stronger. Since this course is dedicated only towards classification problems, providing in-depth coverage should have been the primary focus. A shallow coverage is understandable if the course is broad, but not for courses dedicated only towards a particular topic.

So far in the logistic regression section, I have not really seen any specific methods to the tune the models or implement any robust feature engineering to really make an impact. I will go through the remaining modules of the course and consider revising the rating if the content coverage does improve. But my sincere request to the instructors is to update the course material in the near future to develop supplementary videos so that the students can gain real value from this course.

By Bill Hayes

Good match.

I knew some of this so it's moving too slowly for me.

By Mano

So far going good.. let me continue...

By Igbokwe Anthony

Nice he explained the concepts gave examples and gave projects. I love your lectures

By Ramkumar Iyer

The course is very helpful and provides details on the classification algorithm with real life examples

The instructor is well versed and expert to provide emphasis on key areas.

Very helpful course and recommend to people who want to focus on classification techniques.

By danutaish leash

The professor delivers information with great abilities and passion, learning about AI surely becomes an easy task. I will give this course 6 stars and not five, thank you professor and Udemy.

By Isabelle Petoud

At the beginning I really enjoyed the course. But after a while, the basic was explained again and again and the most important stuff was quickly overviewed.

I asked a question and the reaction was fast but did not answer the question.

The challenges were almost all the same. So the first one is interesting and after that they are annoying.

By William Patterson