Machine Learning Classes Winston-Salem, NC

In this course, students expand their Python programming skills into machine learning and algorithms that can independently learn patterns and make decisions. The course begins with linear and logistic regressions, the most time-tested and reliable tools for approaching a machine learning problem. Students then progress to algorithms with a 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. Participants also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms.

This course in machine learning with R introduces novice R programmers a foundation in machine learning theory. The course covers the foundations of statistics and simple linear regressions, multiple linear regression and generalized linear models, kNN and Naïve Bayes, the curse of dimensionality, tree models, and SVMs. Students who complete this course will understand the mathematics behind machine learning algorithms and be able to use them in real-world situations.

This machine learning course is meant for learners looking to master the concepts needed for machine learning. Students will learn how machine learning can be used for decision-making, the different machine learning techniques available, and how to tackle machine learning projects. The course covers various techniques in machine learning, including regression, classification, k-means, neural networks, and more.

This Python and machine learning course is a 2-day bootcamp designed for individuals with some familiarity with Python programming looking to advance their skill set. In this hands-on bootcamp, students will learn how to use some Python data science libraries and various machine learning techniques. Day one of the bootcamp focuses on introducing students to two data science libraries (NumPy and Pandas), best practices for preparing data, an intro to machine learning, and regression analysis. Day two starts off with cross validation and regularization of data, concludes with classification, and brings everything learned in days one and two together.

This 72-week Machine Learning program was designed to teach students the skills they need to launch careers in the fields of computer science, software engineering, and machine learning. This program is split into six trimesters and covers a wide range of computer science and machine learning tools and concepts. The first three trimesters focus on computer science and software engineering, with a focus on Linux, data structures, algorithms, various programming languages, databases, and more. The final three trimesters are very math-heavy, and focus on computer vision, natural language processing, autonomous driving, working with Pandas, Numpy, Tensorflow, and more. Throughout the program, students will also learn important soft skills to enhance their job readiness, with an emphasis around technical writing, collaboration, whiteboarding, as well as the opportunity to participate in mock interviews.