Machine Learning with TensorFlow and Keras

This course provides a comprehensive introduction to machine learning using TensorFlow and Keras, two of the most popular frameworks for deep learning and neural networks. Designed for beginners and intermediate developers, this course covers foundational concepts, practical applications, and hands-on projects to build and deploy machine learning models.

Course Outline

1. Introduction to Machine Learning
   • Overview of machine learning concepts and applications
   • Types of machine learning algorithms (supervised, unsupervised, reinforcement learning)
   • Introduction to TensorFlow and Keras for deep learning
2. Getting Started with TensorFlow and Keras
   • Setting up TensorFlow and Keras environment
   • Building your first neural network with Keras
   • Understanding tensors and computational graphs in TensorFlow
3. Data Preprocessing and Feature Engineering
   • Handling missing data and outliers
   • Scaling and normalization of data
   • Feature selection and extraction techniques
4. Supervised Learning with TensorFlow and Keras
   • Introduction to supervised learning tasks (classification and regression)
   • Building and training classification models with Keras
   • Evaluating model performance with metrics (accuracy, precision, recall, etc.)
5. Unsupervised Learning with TensorFlow and Keras
   • Clustering algorithms (K-means, DBSCAN, hierarchical clustering)
   • Dimensionality reduction techniques (PCA, t-SNE)
   • Autoencoders for feature learning and anomaly detection
6. Deep Learning Fundamentals
   • Introduction to deep neural networks (DNNs)
   • Convolutional Neural Networks (CNNs) for image classification
   • Recurrent Neural Networks (RNNs) for sequential data analysis
7. Advanced Deep Learning Techniques
   • Transfer learning and fine-tuning pre-trained models
   • Hyperparameter tuning with TensorFlow’s Keras Tuner
   • Handling overfitting with regularization and dropout
8. Model Deployment and Productionization
   • Exporting and saving TensorFlow models
   • Serving models with TensorFlow Serving or TensorFlow Lite
   • Integrating models into web applications or mobile apps
9. Hands-On Projects
   • Practical projects to apply TensorFlow and Keras concepts
   • Building and optimizing machine learning models
   • Fine-tuning models for specific applications
10. Ethical Considerations and Best Practices
    • Ethics in machine learning and AI
    • Bias and fairness in machine learning models
    • Best practices for model interpretation and transparency
11. Career Paths and Further Learning
    • Career opportunities in machine learning and deep learning
    • Resources for continuous learning and professional development
    • Networking and community engagement in the AI and ML industry