AI (Instructor)

Course Description

On the other hand, we denounce with righteous indignation and dislike men who are so beguiled and demoralized by the charms of pleasure of the moment, so blinded by desire, that they cannot foresee the pain and trouble that are bound to ensue and equal blame belongs to those who fail.

Week 1: Introduction and Fundamentals

Class 1:

Introduction to Artificial Intelligence:

 

  • Overview of AI
  • History and evolution of AI
  • Key concepts and terminology

Class 2:

Types of AI and Applications:

  • Narrow AI vs. General AI
  • Machine Learning, Deep Learning, and Natural Language Processing
  • Real-world applications of AI (healthcare, finance, robotics, etc.)

Class 3:

Basics of Machine Learning:

  • Introduction to machine learning
  • Supervised, unsupervised, and reinforcement learning
  • Key algorithms and techniques
Week 2: Machine Learning and Deep Learning

Class 4:

Supervised Learning:

  • Regression and classification
  • Common algorithms (linear regression, decision trees, SVM)
  • Evaluation metrics (accuracy, precision, recall, F1 score)

Class 5:

Unsupervised Learning:

  • Clustering and association
  • Common algorithms (k-means, hierarchical clustering, apriori)
  • Applications and use cases

Class 6:

Introduction to Neural Networks and Deep Learning:

  • Basics of neural networks
  • Structure of a neural network (neurons, layers, activation functions)
  • Introduction to deep learning
Week 3: Advanced Topics and Tools

Class 7:

Deep Learning Techniques:

  • Convolutional Neural Networks (CNNs) for image processing
  • Recurrent Neural Networks (RNNs) for sequence data
  • Generative Adversarial Networks (GANs)

Class 8:

Natural Language Processing (NLP):

  • Basics of NLP
  • Key techniques (tokenization, stemming, lemmatization)
  • Applications (chatbots, sentiment analysis, language translation)

Class 9:

AI Tools and Frameworks:

  • Overview of popular AI tools and frameworks (TensorFlow, PyTorch, Keras)
  • Setting up an AI development environment
  • Introduction to Jupyter Notebooks
Week 4: Implementation and Ethical Considerations

Class 10:

Building AI Models:

  • Data collection and preprocessing
  • Model training and evaluation
  • Hyperparameter tuning and model optimization

Class 11:

AI in Practice:

  • Case studies of successful AI implementations
  • Challenges in deploying AI solutions
  • Future trends in AI

Class 12:

Ethics and Implications of AI:

  • Ethical considerations in AI
  • Bias and fairness in AI models
  • The impact of AI on society and the future of work
  • Course wrap-up and next steps for continued learning

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We Teach the Following Skills

Updated Learning Services

Introduction and Fundamentals

Machine Learning and Deep Learning

Advanced Topics and Tools

Implementation and Ethical Considerations

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