Machine Learning Classes Chicago, IL

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 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.

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 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 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 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 machine learning course teaches the fundamentals of how to use Python programming for machine learning functions. Students will learn about clustering algorithms, recurrent neural networks, natural language processing, and regression and classification techniques. Completion of this program will give students the ability to understand and even solve real-world machine learning problems.

This course shows students how to use scientific methods, procedures, algorithms, and systems necessary for Data Scientists and Machine Learning Engineers. Instructors demonstrate how to build complex quantitative algorithms, synthesize large amounts of information, and gain technical communication skills for reporting results.

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.