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.

Learn to problem solve, and effectively communicate, like an analyst. This course teaches you to use industry-standard tools to make ethical, data-driven decisions. Experience hands-on training to master SQL, Excel, Tableau, PowerBI, and Python – tools listed in virtually every data analytics job posting across industries.

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 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.

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.

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 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.

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.

This 2-week Natural Language Processing (NLP) virtual bootcamp will teach data science professionals how to design and implement custom NLP models. This workshop covers a wide range of NLP tools and concepts, including text classification, regular expressions, preprocessing text, language models, sequence tagging, vector space language models, word embedding, sentiment analysis, and more.

This course is an introduction to Machine Learning with Scikit-Learn. The curriculum will explore several clustering, classification, and regression algorithms commonly used for machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. Students will learn how to structure machine learning models. This course is part of the school’s Certificate in Data Science program.

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.

In this course, students learn how to convert business problems into machine learning problems, define requirements for a ML problem, including KPIs, using an ML canvas, and create different ML pipelines. Students also learn about data transformation, feature engineering, hyper-parameter tuning, loss functions, and cross-validations on regression and classification tasks. Additionally, students design and solve several ML cases regarding predictive maintenance, churn prediction, and customer segmentation.

Designed for ages 13-18, this course covers the fundamentals of computational thinking and machine learning with Python and TensorFlow.

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.

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.