We invite you to learn how unlock the power of AI, transforming your visionary concepts into reality, no matter your present skill level.


Build your own AI






Week 1:

  • Overview of AI, machine learning, and deep learning.
  • Introduction to Python for AI: syntax, data structures, libraries.
  • Setting up the development environment.

Week 2: 

  • Data exploration and preprocessing.
  • Introduction to machine learning models.
  • Model evaluation metrics.

Week 3:

  • Neural networks.
  • Deep learning frameworks.
  • Building your first neural network.

Week 4:

  • Introduction to self-hosting concepts.
  • Docker basics.
  • Introduction to cloud services and APIs.

Week 5:

  • Understanding Convolutional Neural Networks (CNNs).
  • Implementing CNNs.
  • Advanced CNN architectures.

Week 6: 

  • Basics of Recurrent Neural Networks (RNNs).
  • Introduction to Natural Language Processing (NLP).
  • Implementing RNNs for text data.

Week 7: 

  • Introduction to Generative Adversarial Networks (GANs).
  • Basics of reinforcement learning.
  • Implementing a simple reinforcement learning model.

Week 8:

  • Model serialization and deserialization.
  • Introduction to model deployment.
  • Containerizing AI models for deployment.

Week 9:

  • Capstone project work kickoff 
  • Scaling AI systems.

Week 10:

  • Continuation of Capstone project work.
  • Kubernetes, advanced applications

Week 11:

  • Security considerations for AI systems.
  • Ethics and responsible AI.

Week 12:

  • Students present their capstone projects.
  • Q&A.
  • Feedback. 
  • Course wrap-up.

Frequently Asked Questions:

What prior knowledge or skills do I need before taking this course?

This course is designed to be accessible to those without prior knowledge in programming or machine learning. We start from the fundamentals, ensuring everyone has the opportunity to grasp the core concepts of AI. However, it’s important to note that without a background in these areas, you may find the course pace to be quite rapid. We cover a wide range of topics in a condensed timeframe, so while prior experience isn’t mandatory, coming in with some understanding of programming (especially in Python) and basic mathematics could help in keeping up comfortably with the course material.

How will this course help me in understanding and creating self-hosted AI solutions?

This course is structured to not only teach you the theoretical aspects of AI, Machine Learning, and Deep Learning but also to provide you with the practical skills necessary to deploy these technologies in real-world environments. By focusing on self-hosted AI solutions, you’ll learn how to set up, manage, and scale AI applications on your own servers or cloud platforms. This hands-on approach ensures that by the end of the course, you’ll be capable of developing and deploying AI models tailored to your specific needs.

What kind of projects or hands-on experience can I expect from this course?

Throughout the course, you’ll engage in a variety of projects designed to mirror real-world scenarios. From building and training your first machine learning models to deploying complex neural networks for image and speech recognition, each project aims to reinforce the concepts covered in class. The capstone project, in particular, will challenge you to apply all you’ve learned to create a comprehensive AI solution, which could range from a personalized recommendation system to an intelligent chatbot, depending on your interests and the course focus.

Are there any materials or software I need to have access to before starting?

You’ll need a computer with internet access to participate in this course. We’ll be using open-source software and tools extensively, so there are no additional costs for software licenses. Specific requirements, such as Python, Jupyter Notebooks, TensorFlow, and Docker, will be detailed in the course materials. We’ll also guide you through the setup of these tools, ensuring you’re ready to dive into the course content right from the start.

What kind of support and resources will be available to me if I encounter difficulties during the course?

Our commitment to your learning journey doesn’t stop at the lectures. You’ll have access to a dedicated support team, including instructors and teaching assistants, to answer any questions and assist with challenges you may face. Additionally, we foster a collaborative learning environment through forums and discussion groups where you can connect with peers, share insights, and find solutions together. Supplementary resources, such as tutorials, documentation, and best practice guides, will also be readily available to enhance your learning experience as you progress in your journey.

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