Machine Learning Classes Near Me

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.

The Advanced Data Analytics class meets for 12 weeks and teaches students how to use Python for data analytics, including drawing data from multiple sources, visualizing data, and optimizing decision-making. Students also learn about machine learning and statistics.

The Deluxe Data Analytics Track covers course material from both the introductory and advanced level Data Analytics courses. More specifically, this includes learning about Python, Excel, Power BI, and SQL for data analysis, visualization, and optimization.

Blockchain, digital currencies, and smart contract platforms are covered extensively in this course. Students learn how to interact with blockchains manually, basic cryptography, such as hash functions and discrete logarithms, and learn about keys, wallets, addresses, and transactions. Also addressed is ensuring secure transactions, mining work, Ethereum, Solidity, and interaction with smart contracts.

Master Python basics at both a conceptual and applied level in this 7-week course. Students closely examine a wide range of data science areas, such as data collection, visualization, preprocessing, and application of machine learning algorithms, in order to solve complex problems. Specific topics include fundamental data analysis, model building using Scikit-Learn and other platforms, database manipulation, text usage, and time series analysis.

The Applied Machine Learning class teaches popular machine learning algorithms and frameworks. As part of the course, students learn when and how to apply these tools to different real-world scenarios and complete two machine learning portfolio projects.

With Python for Data Science and ML, Promotable students spend eight weeks learning Python through practical applications. Students build supervised and unsupervised machine learning models with their new Python skills and create a data visualization with Matplotlib and Seaborn.

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.