Data Science and Artificial Intelligence

Deep Machine Learning with Python

This Course Includes:

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Certificate of completion
  • Introduction to Python and NumPy
  • Introduction to Machine Learning
  • Introduction to Deep Learning
  • Deep Learning Libraries
  • Neural Network Architectures
  • Training Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Autoencoders and Generative Adversarial Networks (GANs)
  • Natural Language Processing (NLP)
  • Deployment of Deep Learning Models
  • TensorBoard and Model Visualization
  • Hyperparameter Tuning
  • Ethical Considerations in Deep Learning
  • Hands-on Projects and Case Studies
  • Advanced Topics (Optional)

Course Curriculum & Resources

  • Basics of Python programming language
  • Introduction to NumPy for numerical computing

  • Overview of machine learning concepts
  • Understanding supervised and unsupervised learning

  • Basic concepts of neural networks
  • Understanding the architecture of a neural network

  • Introduction to popular deep learning libraries such as TensorFlow or PyTorch
  • Setting up the development environment

  • Understanding different types of neural network architectures (e.g., feedforward, convolutional, recurrent)
  • Architecture design principles

  • Backpropagation and optimization algorithms
  • Fine-tuning model parameters

  • Understanding and implementing CNNs for image classification
  • Transfer learning with pre-trained models

  • Introduction to RNNs for sequence data
  • Applications in natural language processing and time series analysis

  • Understanding autoencoders for unsupervised learning
  • Introduction to GANs for generating synthetic data

  • Processing and analyzing text data
  • Working with word embeddings and language models

  • Strategies for deploying models to production
  • Considerations for scalability and efficiency

  • Using tools like TensorBoard for model visualization and debugging
  • Hyperparameter Tuning
  • Techniques for optimizing model performance through hyperparameter tuning

  • Understanding ethical considerations and biases in machine learning
  • Responsible AI practices

  • Applying deep learning concepts to real-world problems through projects
  • Collaborative coding and debugging exercises

  • Advanced topics such as reinforcement learning, attention mechanisms, and transformers

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    Deep Machine Learning with Python

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