Generative AI Development – Mobile | Web | Desktop

The Professional AI Development course is designed for individuals seeking to enhance their skills and expertise in the field of artificial intelligence (AI) development. This comprehensive course provides a deep dive into AI concepts, tools, and techniques, enabling participants to become proficient in building advanced AI applications.

The course begins with an introduction to AI and machine learning (ML), laying a strong foundation for understanding the principles and methodologies behind AI development. Participants will explore the latest generative AI models and gain expertise in prompt engineering, an essential skill for creating AI systems that respond intelligently to user inputs.

This includes topics such as:

• What is AI?
• What is machine learning?
• The history of AI
• The different types of AI
• The different types of machine learning
• The ethical considerations of AI development

Python programming is a fundamental component of AI development, and this course covers both the basics and advanced concepts. Participants will learn object-oriented programming, Python modules and packages, and file handling techniques for efficient data manipulation. They will also gain hands-on experience with MongoDB, a powerful database for storing and retrieving AI-related data.

• Introduction to AI and machine learning
• Python programming basics
• Object-oriented programming in Python
• File handling in Python
• Databases in Python

The course progresses to advanced Python topics, including NumPy for numerical computing, Pandas for data analysis, SciPy for scientific computing, Django for web development, and Matplotlib for data visualization. Participants will gain practical skills in these libraries, equipping them to handle complex AI tasks and analyze AI-generated insights effectively.

Introduction to the basics of natural language processing (NLP). NLP is a field of computer science that deals with the interaction between computers and human (natural) languages. It is a critical component of AI chatbot development, as it allows chatbots to understand and respond to natural language queries.

• Introduction to NLP
  • NLP is a field of computer science that deals with the interaction between computers and human (natural) languages. It is a critical component of AI chatbot development, as it allows chatbots to understand and respond to natural language queries.
• Tokenization and stemming
  • Tokenization is the process of breaking a text string into individual tokens. Stemming is the process of reducing a word to its root form. These two techniques are used to prepare text for further processing by NLP algorithms.
• Part-of-speech tagging
  • Part-of-speech tagging is the process of assigning a part-of-speech tag to each word in a text string. This information is used by NLP algorithms to understand the meaning of a text string.
• Named entity recognition
  • Named entity recognition is the process of identifying named entities in a text string. Named entities are typically people, places, organizations, or other entities that are mentioned in a text string.
• Text classification
  • Text classification is the process of assigning a category to a text string. This can be used to categorize text strings into different topics or to identify the sentiment of a text string.

Machine learning is a crucial aspect of AI development, and participants will explore various ML algorithms and techniques. They will learn how to apply supervised and unsupervised learning algorithms to real-world datasets, using popular ML libraries like scikit-learn. Additionally, participants will dive into the exciting field of deep learning using PyTorch, acquiring the ability to develop and train deep neural networks for advanced AI applications.

• Introduction to machine learning
  • Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. It is a critical component of AI chatbot development, as it allows chatbots to learn and improve over time.
• Supervised learning
  • Supervised learning is a type of machine learning where the computer is trained on a set of labeled data. The labeled data tells the computer what the correct output should be for a given input.
• Unsupervised learning
  • Unsupervised learning is a type of machine learning where the computer is not trained on labeled data. The computer is instead given a set of unlabeled data and it must learn to identify patterns in the data.
• Reinforcement learning
  • Reinforcement learning is a type of machine learning where the computer learns by trial and error. The computer is given a reward for taking actions that lead to desired outcomes and a punishment for taking actions that lead to undesired outcomes.
• Natural language processing with machine learning
  • • Machine learning for NLP tasks There are a number of machine learning tasks that can be used for NLP. These include:
      • Text classification
      • Named entity recognition
      • Part-of-speech tagging
      • Sentiment analysis
      • Question answering
      • Machine translation

• Cloud computing
  • What is cloud computing?
  • The different types of cloud computing
  • The benefits of cloud computing
  • The challenges of cloud computing
  • Deploying an AI chatbot on the cloud
• User experience design
  • What is user experience design?
  • The principles of user experience design
  • Designing a chatbot for user experience
• Data security
  • What is data security?
  • The importance of data security
  • The different threats to data security
  • Protecting data security in AI chatbots

Participants will delve into Gradio, a user-friendly library that simplifies the creation and deployment of custom machine learning interfaces. They will learn how to design interactive and intuitive user interfaces for AI applications, enhancing user experience and accessibility.

To expand their data gathering capabilities, participants will master web scraping techniques using Selenium and BeautifulSoup. They will learn how to extract data from websites and other online sources, enabling them to acquire the necessary datasets for AI development. Additionally, participants will advance their Gradio skills, creating sophisticated user interfaces with advanced functionalities.

The course also delves into multi-model machine learning, allowing participants to harness the power of multiple ML models concurrently. They will explore OpenAI’s ChatGPT, a leading conversational AI model, and leverage HuggingFace’s comprehensive suite of tools, including Models, Datasets, Spaces, and the Inference API. These skills will enable participants to develop highly intelligent and adaptive AI systems.

Participants will gain expertise in computer vision, natural language processing (NLP), and audio/video processing—essential AI domains. They will learn how to analyze images, process and understand text, and work with audio/video data, enabling them to create AI applications with advanced perceptual capabilities.

As AI development often involves deploying applications on cloud platforms, participants will acquire essential skills in server management. They will explore Amazon Web Services (AWS) and the Google Cloud AI Platform, learning how to deploy, scale, and manage AI applications in the cloud environment effectively.

Throughout the course, participants will utilize popular development tools such as GitHub, Google Colab, and Visual Studio Code, fostering collaboration, version control, and efficient development practices.

Upon completing the Professional AI Development course, participants will possess a comprehensive understanding of AI concepts and a wide range of skills in AI development. They will be capable of building and deploying advanced AI applications across various domains. This course serves as a stepping stone towards becoming a professional AI developer, opening doors to exciting career opportunities in the ever-evolving field of artificial intelligence.