Machine learning goes beyond normal coding, which requires step-by-step instructions, by using algorithms that can independently learn patterns and make decisions. 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.
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Frequently Asked Questions
Do you need to come in with any prior math or programming knowledge?
Yes—a student must take our 5-day Python for Data Science bootcamp or be comfortable with Python and its data science libraries (NumPy and Pandas). All of the mathematical components of this class will be explained during the session.
How is this class structured?
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.
How many students are in a given class?
Noble's typical class ranges from 8-12 students, but we allow up to 20 students to register for our course.
How does this class prepare me for the job market?
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.
Is there mandatory work outside of the classroom?
Students are not required to complete any work outside of class. However, we provide students with bonus materials if they would like extra practice.
What tangible skills do students leave with after the class?
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.
Do you offer discounts or a payment plan for this course?
10% Alumni Discount: Get 10% off this course if you’ve previously taken any 12+ hour course.
$100 Individuals Discount: Take $100 off this course if you’re an individual paying for yourself (you’re not being reimbursed by a company).
Discounts are applied at checkout (no promo code required) and will be verified after you place your order. Discounts are subject to change. Read our discount policies for more details.
This course is not eligible for a payment plan, which is only available for programs priced at $2,495 and above. Read our Payment Plan FAQ for more details.
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