Introduction to AI and Machine Learning
With no prior experience, you will have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment. In the assignments you will be guided in how data scientists apply the important concepts and techniques such as Map-Reduce that are used to solve fundamental problems in big data. You’ll feel empowered to have conversations about big data and the data analysis process.
This introduction to artificial intelligence, machine learning, and data science allows learners to start exploring the foundations of these exciting fields. Learners will complete 3 projects using Python code and the same tools used by professionals in the field. Learners will start learning about the types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. Then we will learn about the steps of successful machine learning projects. These steps include data collection, data preparation, model training, accuracy determination, and model improvement. This is an Outschool flexible schedule class, so all lessons are pre-recorded videos that can be completed at a time convenient for them. ****This is a coding-class using real code and the same tools used by professional AI and Machine Learning engineers. Please, review the coding requirements listed at the end of this description or the parental guidance section.****
What You’ll Learn:
- Explore the big ideas behind AI, and explore how you can use AI in live video projects for amazing, dynamic effects.
- You’re the star! Create incredible live-video projects. Transform yourself into a fire-breathing dragon, add greenscreen effects, apply custom face paint, and much more, all using simple-to-use Tynker block code and your computers’ built-in webcam.
- Track landmarks on your live-video projects. Make sock puppets and creatures that follow your movements. Calculate angles and distance to create exercise apps and other responsive AR-style video projects.
- Explore how computers recognize objects and use computer-vision. Use the same technology that self-driving cars harness, right from Tynker.
- Create flexible games and apps that understand natural language, using Tynker’s NLP blocks. Explore the big ideas of Natural Language Processing (NLP), and get familiar with training an AI from scratch.
What is the target audience?
- You might be thinking, all of the above – and that is fine. But as a complete beginner learning Unreal Engine 4.
- The rendering system in Unreal Engine 4 is an all-new, DirectX 11 pipeline that includes deferred shading.
Learning a new game engine as a complete beginner is very intimidating. There are a lot of tutorials, documentation and advice already out but how do you start and proceed with learning Unreal Engine 4 is unclear. You get pulled into many different directions and end up confused and overwhelmed. I have spent a lot of time deconstructing what it takes to learn a game engine from scratch. What it is that you should focus on first and what you should avoid until later.
Starting Course
After Intro
