Introduction

TensorFlow has become the leading open-source framework for machine learning (ML) and deep learning, powering AI applications across various industries. This course provides a comprehensive, hands-on approach to developing and deploying machine learning models using TensorFlow 2.x.

Participants will learn how to build, train, and optimize ML models using TensorFlow’s ecosystem, including Keras, TensorFlow Hub, and TensorFlow Serving. The course also covers advanced deep learning architectures, model deployment strategies, and AI-driven applications to prepare learners for real-world ML challenges.

Course Objectives

By the end of this course, participants will be able to:

• Understand the fundamentals of machine learning and deep learning with TensorFlow.
• Build, train, and evaluate ML models using TensorFlow 2.x and Keras.
• Implement advanced deep learning architectures like CNNs, RNNs, Transformers.
• Optimize model performance using hyperparameter tuning and regularization techniques.
• Deploy ML models using TensorFlow Serving, TensorFlow Lite, and TensorFlow.js.
• Integrate ML models into cloud environments like Google Cloud AI, AWS SageMaker, and Azure ML.
• Utilize MLOps best practices for model monitoring, retraining, and scalability.

Who Should Attend?

This course is ideal for:

• Data scientists looking to build deep learning models.
• AI/ML engineers developing and deploying ML solutions.
• Software engineers integrating ML into applications.
• Researchers and analysts applying ML techniques to real-world problems.
• Cloud architects designing scalable ML workflows.

Day-by-Day Course Breakdown

Day 1: Fundamentals of Machine Learning with TensorFlow

Introduction to Machine Learning & TensorFlow

• Overview of ML concepts: Supervised vs. Unsupervised Learning
• Introduction to TensorFlow 2.x and its ecosystem
• Setting up the TensorFlow development environment

Building a Basic Machine Learning Model

• Using Keras APIs for model building
• Data preprocessing with TensorFlow Data Pipelines (tf.data API)
• Hands-on lab: Training a simple regression/classification model

Day 2: Deep Learning with TensorFlow

Understanding Neural Networks & Deep Learning

• Fundamentals of deep learning and multi-layer perceptrons (MLPs)
• Activation functions, backpropagation, and optimization techniques
• Implementing deep neural networks (DNNs) with TensorFlow and Keras

Convolutional Neural Networks (CNNs) for Computer Vision

• Basics of CNNs: Convolution, Pooling, Dropout
• Transfer Learning with TensorFlow Hub
• Hands-on lab: Image classification using CNNs & ResNet, VGG models

Day 3: Recurrent Neural Networks & Transformers

Sequence Modeling with Recurrent Neural Networks (RNNs)

• Understanding RNNs, LSTMs, and GRUs
• Implementing NLP models with TensorFlow and Keras
• Hands-on lab: Sentiment analysis using LSTMs

Transformers and Attention Mechanisms

• Introduction to the Transformer architecture and self-attention
• Implementing text classification and summarization with BERT & GPT
• Hands-on lab: Fine-tuning BERT for NLP tasks

Day 4: Model Optimization & Deployment

Hyperparameter Tuning and Model Optimization

• Optimizing models using TensorFlow Tuner and Keras Callbacks
• Avoiding overfitting: Regularization, Dropout, and Data Augmentation
• Hands-on lab: Fine-tuning a deep learning model

Deploying Machine Learning Models

• TensorFlow Serving for real-time API deployment
• TensorFlow Lite for mobile and edge AI applications
• TensorFlow.js for browser-based ML applications
• Hands-on lab: Deploying a trained model as a REST API

Day 5: MLOps & Real-World Applications

MLOps: Monitoring, Retraining, and Automation

• Model monitoring and retraining strategies
• Automating ML workflows with Kubeflow & TensorFlow Extended (TFX)
• Hands-on lab: Implementing CI/CD for ML pipelines

Capstone Project: End-to-End ML Application

• Participants will build, train, optimize, and deploy a complete ML solution
• Final presentations and peer review of models

Conclusion & Certification

At the end of the course, participants will receive a Certificate of Completion, demonstrating their expertise in Machine Learning with TensorFlow.

This course blends theory, hands-on labs, and real-world case studies, preparing learners for the future of AI and ML.