AI Classes NYC

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.

Master the fundamentals of AI and data science in this certificate program, designed to provide hands-on experience with key tools like Python, SQL, and machine learning algorithms. Whether you're starting fresh or enhancing your skill set, this course prepares you for a successful career in data-driven decision-making.

Dive into the world of generative AI with Noble Desktop's introductory course on ChatGPT. Explore the limitless possibilities of artificial intelligence as you learn to harness ChatGPT's innovative technology to generate text, ideas, and solutions across various domains. Gain practical insights and hands-on experience in crafting prompts and exploring real-world applications, empowering you to leverage the creative potential of AI for your projects and endeavors.

This program offers an in-depth exploration of generative AI, focusing on the creative use of tools like ChatGPT. Students will learn to apply AI in content creation, including generating text and building AI-powered chatbots, making them proficient in cutting-edge technologies. Real-world projects are incorporated to ensure practical, hands-on experience that can be applied across various industries.

This course covers the skills needed to become a Data Analyst or Business Analyst, including data analytics fundamentals, visualization with Tableau, using Python to clean and manipulate data, and how to work with relational databases. One-on-one mentoring is included.

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.

In this program, participants will learn the key skills and tools needed to succeed as a data analyst, including Excel, SQL, and Tableau. Attendees will learn how to use Excel to create graphs, summarize data with PivotTables, and analyze data. In SQL, attendees will learn the essentials of relational databases, queries, functions, and more. Finally, participants will learn Tableau to create visualizations to express their data. The program is open to beginners and has flexible scheduling options. Tuition includes hands-on training, a free retake, and training manuals for each course.

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.