The Complete Intro to Machine Learning
What you'll learn
- Learn the basics of data visualization and pre-processing (Python basics, Numpy, Pandas, Seaborn)
- Gain theoretical and practical experience with fundamental machine learning algorithms (Linear and Logistic Regression, K-NN, Decision Trees, Neural Networks)
- Understand advanced ML topics (encoding, ensemble learning techniques, etc.)
- Submit to your first Kaggle Machine Learning Competition
Requirements
- No programming or theoretical math prerequisites. We'll teach you everything you need to know.
Description
Interested in machine learning but confused by the jargon? If so, we made this course for you.
Machine learning is the fastest-growing field with constant groundbreaking research. If you're interested in any of the following, you'll be interested in ML:
Self-driving cars
Language processing
Market prediction
Self-playing games
And so much more!
No past knowledge is required: we'll start with the basics of Python and end with gradient-boosted decision trees and neural networks. The course will walk you through the fundamentals of machine learning, explaining mathematical foundations as well as practical implementations. By the end of our course, you'll have worked with five public data sets and have implemented all essential supervised learning models. After the course's completion, you'll be equipped to apply your skills to Kaggle data science competitions, business intelligence applications, and research projects.
We made the course quick, simple, and thorough. We know you're busy, so our curriculum cuts to the chase with every lecture. If you're interested in the field, this is a great course to start with.
Here are some of the Python libraries you'll be using:
Numpy (linear algebra)
Pandas (data manipulation)
Seaborn (data visualization)
Scikit-learn (optimized machine learning models)
Keras (neural networks)
XGBoost (gradient-boosted decision trees)
Here are the most important ML models you'll use:
Linear Regression
Logistic Regression
Random Forrest Decision Trees
Gradient-Boosted Decision Trees
Neural Networks
Not convinced yet? By taking our course, you'll also have access to sample code for all major supervised machine learning models. Use them how you please!
Start your data science journey today with The Complete Intro to Machine Learning with Python.
Who this course is for:
- Anyone interested in machine learning, data science, and artificial intelligence. No experience required.
Instructors
- 4.3 Instructor Rating
- 265 Reviews
- 27,474 Students
- 1 Course
The Student Machine Learning Coalition is a global student-led organization that aims to make ML more accessible. Comprised of college and high school students, we provide a platform for students to join workshops, engage in Kaggle Competitions, and receive guidance on projects—all for free. SMLC chapters are in countries around the world (from the US to India to Nepal), and we comprise roughly 400 students worldwide. As an organization, our members have already placed in the top 1% and top 5% in multiple Kaggle data science competitions with 20,000+ attendees. Moreover, students have qualified for silver and bronze medals in competitions sponsored by companies and organizations such as Lyft and Harvard University’s Laboratory of Innovation Science.
Through Udemy, we hope to make our resources accessible to anyone that might be interested.
- 4.3 Instructor Rating
- 265 Reviews
- 27,474 Students
- 1 Course
Michael Lutz is the Founder and President of the Student Machine Learning Coalition. Outside of SMLC, he has worked on ML research papers that will appear in IEEE conference proceedings, and he has contributed to natural language processing projects funded by NASA Ames Research Center. Michael is incredibly excited about the future of ML, and he is grateful that you chose to learn with him!
- 4.3 Instructor Rating
- 265 Reviews
- 27,474 Students
- 1 Course
Arjun Rajaram is Co-Founder and Operations and Events Director of the Student Machine Learning Coalition. Outside of SMLC, he has conducted research and published papers at research institutes such as the SIP program at UC Santa Cruz and the iResearch Institute. He has worked on research on topics such as utilizing neural networks and computer vision in biomedical image analysis and developing citizen science mobile data collection apps with integrated machine learning models. Arjun is very passionate about spreading his love for AI, machine learning, and computer science through Udemy.
- 4.3 Instructor Rating
- 265 Reviews
- 27,474 Students
- 1 Course
Hey! I'm Saurav, a student from the Bay Area super into startups, AI, and space.
- 4.3 Instructor Rating
- 265 Reviews
- 27,474 Students
- 1 Course
Aswin Surya was the former President of the Student Machine Learning Coalition. Outside of SMLC, he has conducted research and written papers at research institutes like NASA's SEES Program, the Stanford AIMI Center, and MIT's BeaverWorks Summer Institute. He is passionate about the real-world applications of computer vision, especially in healthcare, and continues to work to make machine learning education more accessible to all.
- 4.3 Instructor Rating
- 265 Reviews
- 27,474 Students
- 1 Course
Chatanya Sarin is a current senior at Bellarmine College Preparatory. He is interested in Machine Learning for sports - he is currently a data analyst for his school's football team. He is passionate about AI for sports and for social good. He has conducted research in AI - using AI to track plane emissions during flight.
- 4.3 Instructor Rating
- 265 Reviews
- 27,474 Students
- 1 Course
Aadi Chauhan is the current President of the Student Machine Learning Coalition. Outside of SMLC, he has conducted research at institutions like UT Dallas, University of Michigan, and UC Berkeley, and he has published several high-impact papers on AI at renowned international conferences. Aadi is passionate about bridging the gap between cultures and technology.
- 4.3 Instructor Rating
- 265 Reviews
- 27,474 Students
- 1 Course
Kevin Lu is a researcher and developer focused on machine learning and AI safety. He is a USA Physics Olympiad Gold Medalist with publications in IEEE and ACL. His work spans EHR cybersecurity, equitable diffusion of AI at Equiano, interpretability research at Georgia Tech, and recommendation system experiments at UC Berkeley’s Haas School of Business.