AI (Artificial Intelligence) Fundamentals Training

Length: 2 Days

AI (ARTIFICIAL INTELLIGENCE) FUNDAMENTALS TRAINING

The AI (Artificial Intelligence) Fundamentals by Tonex is a comprehensive training course that covers the basics of AI, Machine Learning, Deep Learning and ChatGPT. The course starts with an introduction to AI, and then covers additional topics such as machine learning, deep learning, natural language processing, ChatGPT, robotics and computer vision. The course also includes hands-on exercises that allow students to apply the concepts they have learned.

The AI Fundamentals by Tonex is an excellent option for anyone who wants to learn about artificial intelligence and more.

Who Should Attend?

Recommended for

The course is taught by experienced instructors who are experts in AI. The instructors have a wealth of experience in the field, and they are passionate about teaching others.

The AI Fundamentals by Tonex is designed for anyone who wants to learn about artificial intelligence including:

  • Current and aspiring data scientists
  • Analysts
  • Engineers
  • Program and Project Managers
  • Business leaders

Course Outlines

You will be learning

Fundamentals of AI

  • Fundamentals of Machine Learning and Deep Learning
  • Definition and overview of AI
  • Historical background and key milestones
  • Various applications of AI in different domains
  • Ethical considerations and challenges in AI development
  • Fundamentals of Natural Language Processing (NLP)
  • Fundamentals of ChatGPT
  • Introduction of AI integration with Robotics and Computer vision
  • The Future Trends of Artificial Intelligence

Fundamentals of Machine Learning (ML)

  • What is Machine Learning?
  • Supervised, unsupervised, and reinforcement learning
  • Key components of the ML process (data, models, evaluation)
  • Training and testing data, validation techniques
  • Evaluation metrics and performance measures

Machine Learning Algorithms and Techniques

  • Linear regression and logistic regression
  • Decision trees and random forests
  • Support Vector Machines (SVM)
  • Clustering algorithms (K-means, hierarchical clustering)
  • Neural networks and deep learning
  • Dimensionality reduction techniques (PCA, t-SNE)
  • Ensemble learning and boosting algorithms

Introduction to Natural Language Processing (NLP)

  • Introduction to NLP and its applications
  • Text preprocessing techniques (tokenization, stemming, etc.)
  • Bag-of-words and TF-IDF representation
  • Word embeddings (Word2Vec, GloVe)
  • Sentiment analysis and text classification
  • Named Entity Recognition (NER) and Part-of-Speech (POS) tagging

Introduction to ChatGPT

  • Overview of ChatGPT and its capabilities
  • Training process and underlying architecture
  • Fine-tuning and customization options
  • Best practices for interacting with ChatGPT
  • Ethical considerations and responsible AI usage

Practical Projects and Case Studies

  • Building a sentiment analysis model using ML
  • Creating a chatbot using ChatGPT and NLP techniques
  • Image classification using deep learning
  • Text generation and storytelling with ChatGPT
  • Exploring real-world AI applications and case studies

Challenges and Future Directions

  • Current challenges and limitations of AI and ML
  • Recent advancements and emerging trends in the field
  • Ethical implications and responsible AI development
  • Opportunities and potential future developments

Conclusion and Final Project

  • Recap of key concepts and takeaways
  • Final project showcasing the application of AI and ML
  • Discussion and reflection on the course material
  • Resources for further learning and exploration