Machine Learning Classes NYC

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 Python machine learning course teaches machine learning methods and modules with the Python code designed to implement them. The units of this course explain simple and complex linear regressions, classification methods for logistic regression, discriminant analysis, and naïve Bayes, support vector machines and tree-based methods, regularization strategies, and how to use clustering algorithms. Completion of this 20-week course will prepare students to use machine learning algorithms to analyze complex datasets and make logical predictions.

This course will begin with advanced Python and statistic topics such as object-oriented programming and regression models. After learning this first module, students will learn how to apply these concepts using real-world financial data by building a predictive returns model using regression. The next section of the course will introduce students to important financial statements and ratios. After students learn these financial concepts, they will be introduced on how to pull data from these statements and compute these important financial ratios using Python.

In this course, high school students work with NYU instructors to learn machine learning and artificial intelligence. As they work through the curriculum, students discover important concepts like regressions, models, and neural networks.

This 18-hour Machine Learning & Automation for Finance course teaches advanced skills in machine learning in Python. Students in this course will develop advanced skills in the Python language, learn how to use object-oriented programming and regression models, and apply these concepts to a real world problem. They will also learn how to use Python to build predictive financial models and to create budgets that may be exported.

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.

In this course, students discover the essentials of machine learning. By learning to work in Python, students gain familiarity with object-oriented programming and neural networks.