Machine Learning Classes Live Online

In this comprehensive data science program, individuals will learn a variety of critical skills to become a data scientist. Students will learn to code with Python, create machine learning models, automate tasks like web scraping, and interact with databases using SQL. Topics include data analysis with Python's Numpy and Pandas packages, data visualization, predictive analytics, machine learning, SQL querying, Python automation, and web scraping. Students will learn hands-on by working in Python and SQL and will complete the program with the skills they need to enter the field of data science. This program offers flexible scheduling and provides a free retake for students to refresh the material.

Develop AI-powered web apps in this advanced Python course, where you'll use Flask and the OpenAI API to create dynamic sentiment analysis applications. Gain essential skills in web development, error handling, and deployment, taking your Python projects to a professional level with real-world AI integration.

In this data science bootcamp, students will build programming skills and data analysis skills using Python. This course is open to beginners and is meant to get individuals up and running with Python programming and data science to generate insights from data. Topics covered include programming fundamentals, working with data frames, data analysis, data visualization, and statistical analysis. This course offers flexible scheduling options and a free retake for students to refresh the materials.

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.

The Fullstack Academy Artificial Intelligence & Machine Learning bootcamp provides current and prospective data professionals with the in-demand skills to specialize in this lucrative, dynamic, and rapidly-growing field. Over 26 weeks part-time, students will learn practical and theoretical machine learning concepts using real-world tools—graduating with the working knowledge and experience needed to qualify for a range of data roles, including those concentrated in AI. Students will also receive professional career coaching support for up to a year following graduation to help build and maintain an ideal, specialized career path in the industry.

Learn to apply machine learning algorithms in real-world scenarios using Python, with hands-on projects that cover regression, classification, and predictive modeling. Ideal for data professionals looking to advance their skills in a high-demand field.

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.

In this Data Analyst course, students will master Python, SQL, Looker (Google Data Studio), Streamlit, and Statistics, equipping them to join any data-driven team with proficiency in creating predictive models using Python and its powerful libraries like Pandas and Numpy. The curriculum covers a range of algorithms, supervised and unsupervised learning models, real-life case practices, and industry-standard tools, along with comprehensive training in strategizing and developing A.I. projects from development to deployment, with a focus on privacy and security.