Machine Learning with TensorFlow Nanoschool

Foundations of Machine Learning with TensorFlow

• Understand the role of TensorFlow in machine learning, deep learning, and artificial intelligence development.
• Learn key concepts such as datasets, features, labels, model training, prediction, loss functions, and optimization.
• Explore how TensorFlow supports scalable AI model development for real-world applications.

Python, NumPy, and Data Preparation for TensorFlow

• Prepare datasets using Python, NumPy, Pandas, and basic data preprocessing techniques.
• Handle missing values, scaling, encoding, train-test splitting, and feature preparation.
• Convert clean datasets into formats suitable for TensorFlow model training.

Building Machine Learning Models with TensorFlow and Keras

• Build basic machine learning models using TensorFlow and Keras APIs.
• Understand layers, activation functions, optimizers, loss functions, and model compilation.
• Train models for classification, regression, and prediction-based tasks.

Neural Networks and Deep Learning Fundamentals

• Learn how neural networks work through neurons, weights, biases, activation functions, and backpropagation.
• Design feedforward neural networks for structured data problems.
• Understand model training behavior, overfitting, underfitting, and regularization methods.

Model Training, Evaluation, and Performance Improvement

• Train TensorFlow models using real-world datasets and monitor learning progress.
• Evaluate model performance using accuracy, precision, recall, F1-score, RMSE, MAE, and confusion matrix.
• Improve models using hyperparameter tuning, dropout, batch normalization, and early stopping.

Computer Vision with TensorFlow

• Learn the basics of image data processing and computer vision model building.
• Build convolutional neural networks for image classification and visual pattern recognition.
• Apply TensorFlow to practical use cases such as object recognition, defect detection, and image-based prediction.

TensorBoard, Experiment Tracking, and Model Debugging

• Use TensorBoard to monitor training metrics, loss curves, accuracy, and model behavior.
• Compare experiments and understand how model changes affect performance.
• Debug common TensorFlow training issues and improve model reliability.

Deployment and Real-World TensorFlow Applications

• Understand how trained TensorFlow models are saved, reused, and deployed for practical applications.
• Explore use cases in healthcare, finance, manufacturing, retail, automation, and smart systems.
• Learn how TensorFlow models support prediction, classification, recommendation, and intelligent decision-making.

Capstone: End-to-End Machine Learning with TensorFlow Project

• Work on a complete TensorFlow-based machine learning project from dataset preparation to final model evaluation.
• Build, train, tune, test, and present a practical AI model using TensorFlow and Keras.
• Create a project portfolio that demonstrates real-world TensorFlow and machine learning skills.