Machine Learning Classification Bootcamp in Python
What you'll 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
- Implement classification algorithms in Scikit-Learn for K-Nearest Neighbors, (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
Requirements
- Basic knowledge of Python Programming
- Experienced computer user
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 is one of the top skills to acquire in 2022, with an average salary of over $114,000 in the United States, according to PayScale! Over the past two years, the total number of ML jobs has grown around 600 percent and is expected to grow even more by 2025.
This course provides students with the knowledge and 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)
This course will provide students with knowledge of key aspects of state-of-the-art classification techniques. We are going to build 10 projects from scratch using a 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 clients’ 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 a clear, simple, and easy way.
All Jupyter notebooks (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 challenging real-world 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
Instructors
- 4.5 Instructor Rating
- 55,981 Reviews
- 444,423 Students
- 50 Courses
Hello and welcome everyone!
I’m Dr. Ryan Ahmed. I’m a professor, educator, and founder of Stemplicity School, where we make AI and data science simple, practical, and accessible for everyone. I’m passionate about creating learning experiences that are engaging, hands-on, and designed to help people thrive in a fast-changing world.
If you're just starting out in tech or aiming to sharpen your skills in AI, data science, or cloud computing, my goal is to make those complex topics feel approachable, relevant, and easy to apply. Over the past ten years, I’ve taught more than 400,000 learners across 160 countries and built a global community of over 250,000 subscribers on my YouTube channel, Prof. Ryan Ahmed, where I share tutorials and tools to help people grow their careers.
I’ve also led corporate training sessions on AI to companies like HSBC, RBC, Discover, and Barclays in US, Canada, and the UK. Earlier in my career, I held leadership roles at GM, Samsung, and Stellantis, working on electric and autonomous vehicle technologies.
I hold a MASc, PhD, and MBA from McMaster University. I’m also a licensed Professional Engineer and a Stanford-certified program manager with over 50 published research papers in AI and battery systems. But titles aside, what matters most to me is seeing others succeed.
If you're curious, motivated, and ready to learn, I’m here to help you take that next step.
- 4.5 Instructor Rating
- 939,090 Reviews
- 3,241,339 Students
- 147 Courses
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We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
SuperDataScience Team!
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We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets and more!
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