AI in Practice >>> Free Artificial Intelligence (AI) Course
Free Udemy Artificial Intelligence (AI) Course
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| AI in Practice >>> Free Artificial Intelligence (AI) Course |
Description
The AI in Practice Bootcamp is a comprehensive program designed to prepare data scientists and engineers for the real world. The program comprises five chapters, each with 3 or 4 video lectures and a Capstone project. Participants need basic programming knowledge, but no significant data science skills are required. The first five lessons will focus on video lectures and assignments, with each assignment taking 2-3 hours.
Upon joining the Bootcamp, participants will be added to the Slack Channel, where they can ask questions to mentors. After the lectures and assignments, participants will choose a real-world AI problem to tackle. The program covers five exciting topics: Introduction to AI, Developer Skills, Data Exploring & Engineering, AI Pitfalls and Biases, and Transfer Learning and AutoML.
In the introductory session, participants will learn about the history and rapidly changing field of artificial intelligence. Developer skills include computer basics, working with servers, and putting models in production. Data exploration and engineering are essential steps for data scientists to start working before processing. AI pitfalls and biases are discussed, along with the growing field of fairness and bias. Finally, transfer learning and AutoML are introduced, allowing data scientists to utilize pre-trained models and take the work out of their hands.
Upon joining the Bootcamp, participants will be added to the Slack Channel, where they can ask questions to mentors. After the lectures and assignments, participants will choose a real-world AI problem to tackle. The program covers five exciting topics: Introduction to AI, Developer Skills, Data Exploring & Engineering, AI Pitfalls and Biases, and Transfer Learning and AutoML.
In the introductory session, participants will learn about the history and rapidly changing field of artificial intelligence. Developer skills include computer basics, working with servers, and putting models in production. Data exploration and engineering are essential steps for data scientists to start working before processing. AI pitfalls and biases are discussed, along with the growing field of fairness and bias. Finally, transfer learning and AutoML are introduced, allowing data scientists to utilize pre-trained models and take the work out of their hands.
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