This skillset is in high demand, as machine learning algorithms now run the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix.
This course will begin with linear and logistic regression, the most time-tested and reliable tools for approaching a machine learning problem. The course will then progress to algorithms with a very different theoretical basis, such as k-nearest neighbors, decision trees, and random forest. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. You’ll also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms.
The course will be focused on the practical skills needed to solve real-world problems with machine learning. The mathematical foundations for each machine learning algorithm will be explained visually, but there will not be a formal math component. Entering students are expected to be comfortable with writing Python programs, as well as the Numpy and Pandas libraries.
This course does require students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp before taking this course.
This course requires students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp before taking this course.
From our hands-on training style to world-class instructors and custom-crafted curriculum, we deliver results our alumni are proud of.
Learn the concepts and skills covered in this course or your tuition is on us. See details and terms & conditions.
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Refresh the materials for free within one year
Attend this course in-person in NYC or Live Online via Zoom. Engage with expert instructors, ask questions, and get feedback on your exercises and projects. Switch between live-online and in-person as needed, even after the course has started.
Unlike other providers, these are not mass open-enrollment classes. At Noble Desktop, you’ll learn in small groups (typically 8-15 students) and receive personalized attention.
Get face-to-face interaction with an instructor and other students when you learn at our NYC campus. Courses are hands-on with a computer and software provided.
Get the same interactivity and access to the instructor as in-person students. There are no extra fees and we’ll work with you to ensure your remote setup is perfect.
Our instructors are verterans of their field and experienced educators, driven to help you succeed.
We want you to leverage the skills you’ll learn. In addition to 30 hours of hands-on, small group training, you’ll receive one 1-on-1 training session where you can:
Upon completion of this course, you’ll receive an official certificate testifying to your mastery of the curriculum. We’ll send you a link where you can download your certificate, share it online with your friends, post it to your professional network on LinkedIn, and view all your earned certificates. Congratulations on your achievement!
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We offer a single free retake of the the class within a year.
You can also access recordings of each session in your student portal within one business day of the session’s end. Recordings are available for one month after the session.
This class is an 18-hour class that starts by teaching forms of regression analysis and moves onto more industry-used algorithms such as k-nearest neighbors, decision trees, and random forest. Additionally, students will learn how to determine the accuracy of a predictive model.
Noble's typical class ranges from 8-12 students, but we allow up to 20 students to register for our course.
The classes will allow students to learn advanced topics in data science used by the most cutting edge companies such as Google, Facebook, and more. These topics will allow students to build, evaluate, and reassess forecasting models on all forms of data.
Students are not required to complete any work outside of class. However, we provide students with bonus materials if they would like extra practice.
Students will leave with the ability to learn how to build a model from start to finish. Students will learn how to clean and balance data, apply a form of learning algorithm on the data, perform a bias test, and finally evaluate the accuracy of your model.
This course requires students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp before taking this course.
This course does not qualify for payments plans or student financing. See our Payment Plan FAQ to find related programs that qualify.
You may attend this training virtually (online) at the scheduled time the course is offered (New York, Eastern Time).
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