Our Applied Machine Learning Course is designed to get you up and running with real-world machine learning skills, fast. With a focus on practical applications, you'll learn by doing, not just reading about it.
Our expert instructors have years of experience in the field and will guide you through the course, providing personalized support and feedback every step of the way. They'll also share their own experiences and insights to help you navigate the course material.
We've structured the course to be flexible and adaptable to your needs, with online and offline options available. This means you can learn at your own pace, whether that's in a quiet coffee shop or in a busy office.
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Course Content
The applied machine learning course is a comprehensive program that covers a wide range of topics. It includes 557 lessons, which is a significant amount of material to cover.
The course is designed to provide a broad overview of the field of machine learning, including generative models, support vector machines, tree-based algorithms, neural networks, and gradient boosting. You'll learn how to implement these algorithms from scratch in Python using standard libraries such as NumPy, SciPy, or scikit-learn.
One of the key takeaways from the course is the understanding of how machine learning and data science are disrupting multiple industries today. You'll also learn about various machine learning algorithms, including linear, logistic regression, decision tree, and random forest.
The course covers both supervised and unsupervised machine learning techniques, including classification, regression, clustering, and dimensionality reduction. You'll learn how to evaluate your machine learning models and improve them through feature engineering.
Some of the specific topics covered in the course include:
- Linear, logistic regression, decision tree, and random forest algorithms
- Ensemble modeling techniques like bagging, boosting, support vector machines (SVM), and kernel tricks
- Dimensionality reduction techniques like principal component analysis (PCA) and t-SNE
- Unsupervised machine learning techniques like k-means clustering and hierarchical clustering
The course also covers the machine learning lifecycle, including exploratory analysis, data preparation, model training, and model evaluation. You'll learn how to perform NLP sentiment analysis and implement deep learning models.
Overall, the applied machine learning course provides a comprehensive education in machine learning and data science, covering both theoretical and practical aspects of the field.
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Instructors and Support
The instructors behind our applied machine learning course are a talented group of experts. They include Volodymyr Kuleshov, an Assistant Professor at Cornell Tech, and Nathan Kallus, an Associate Professor at Cornell Tech.
These professors, along with Serge Belongie, a Professor at the University of Copenhagen, have developed the course materials used in CS5785 Applied Machine Learning at Cornell University and Cornell Tech. Hongjun Wu has also produced the open online version of the course and its accompanying lectures.
We're grateful to the team of over a dozen teaching assistants who have helped with drafts of these lecture notes, making this course a collaborative effort.
AI-Powered
AI-powered learning can transform your programs with personalized learning, driving 15x engagement.
Real-time feedback is a key feature of AI-powered learning, helping learners stay on track and understand their progress.
The support for learners when they need it is also crucial, providing a safety net for those who struggle with difficult concepts.
This approach has been shown to be highly effective, with many learners benefiting from the extra help and guidance.
Instructors
The instructors behind CS5785 Applied Machine Learning at Cornell University and Cornell Tech are a talented group of experts. Volodymyr Kuleshov, an Assistant Professor of Computer Science at Cornell Tech, has played a key role in developing the course materials.
The open online version of the course has been produced by Hongjun Wu, who has put in a lot of effort to make the course accessible to a wider audience.
Several professors have contributed to the development of the course materials, including Nathan Kallus, an Associate Professor of Operations Research at Cornell Tech, and Serge Belongie, a Professor of Computer Science at the University of Copenhagen.
We're also grateful to over a dozen teaching assistants who have helped with drafts of these lecture notes.
Student Testimonials
Our students are raving about the Applied Machine Learning course, and for good reason. The course is designed to be beginner-friendly, making it easy to follow and understand the material.
Many students have praised the course for its practical approach, learning them practical dimensionality reduction and regularisation techniques. The course is also great for those new to the Data Science field, with students feeling confident in their abilities after just a few lessons.
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The instructors are experienced professionals who have proven track records in their domains, providing valuable insights and mentorship to students. In fact, one student mentioned that the instructor's ability to simplify complicated concepts was pivotal in giving them the confidence to propose solutions at work.
The course is also praised for its supportive community, with students feeling encouraged to put in the time and effort required to succeed. One student even dropped their grad class to focus on the Applied Machine Learning course, a testament to its quality and impact.
Here are some student testimonials that highlight the course's strengths:
- Beginner-friendly and easy to follow
- Practical approach to learning
- Experienced instructors with proven track records
- Supportive community and prompt responses to queries
- Valuable insights and mentorship
- Confidence-building and career-boosting
Learning Outcomes
You can expect to learn a vast number of skills in the Applied Machine Learning course, with a total of 557 lessons to cover.
The course is designed to be practical and hands-on, with six real-life projects from industry that you'll work on to apply your knowledge.
You'll learn to program in Python using popular libraries like NumPy, Scikit-Learn, and PyTorch.
You'll also learn to train, validate, tune, and test predictive models, including linear and neural models.
Some of the key machine learning performance metrics you'll learn about include accuracy, precision, recall, receiver operator characteristic curve, and cross entropy.
A key aspect of the course is designing and training artificial neural networks (ANNs) using back propagation and stochastic gradient descent.
You'll also learn to recognize and classify images using convolutional neural networks (CNNs).
Another important topic covered in the course is applying transformer architectures to generate text using large language models.
You'll learn a policy for controlling behavior using reinforcement learning with deep neural network function approximation.
The course also covers recent research advances in machine learning and how to interpret and explain them.
To give you a better idea of the learning objectives, here are some of the key skills you'll acquire:
- Program in Python using NumPy, Scikit-Learn, and PyTorch
- Train, validate, tune, and test a predictive models including linear and neural models
- Understand, measure, and explain common machine learning performance metrics
- Design and train an artificial neural network (ANN) using back propagation and stochastic gradient descent
- Recognize and classify images using convolutional neural networks (CNNs)
- Apply transformer architectures to generate text using large language models
- Learn a policy for controlling behavior using reinforcement learning with deep neural network function approximation
- Interpret and explain recent research advances in machine learning
- Consider the integrated questions of interpretation, bias, transparency, and fairness in machine learning
Sources
- open issue (github.com)
- repository (github.com)
- Serge Belongie (sergebelongie.github.io)
- Applied Machine Learning – Beginner to Professional (analyticsvidhya.com)
- Applied Machine Learning (uplimit.com)
- Applied Machine Learning and AI - eCornell - Cornell University (cornell.edu)
- freely view RLM code examples here (github.com)
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