AI-X
TensorFlow & Machine Learning Learning Hub
A single place to learn, practice, and master Machine Learning, Deep Learning,
and TensorFlow — from fundamentals to real-world applications.
TensorFlow
- What is TensorFlow?
- TensorFlow is an open-source end-to-end machine learning library for preprocessing data, modelling data and serving models
- Why use TensorFlow?
- Rather than building machine learning and deep learning models from scratch, it's more likely you'll use a library such as TensorFlow. This is because it contains many of the most common machine learning functions you'll want to use.
What are we going to cover
- Introduction to tensors (creating tensors)
- Getting information from tensors (tensor attributes)
- Manipulating tensors (tensor operations)
- Tensors and NumPy
- Using @tf.function (a way to speed up your regular Python functions)
- Using GPUs with TensorFlow
- Exercises to try
- Python for ML (NumPy, Pandas, Matplotlib)
2. Classical Machine Learning
- Regression (Linear, Polynomial)
- Classification (Logistic Regression, KNN, SVM)
- Decision Trees & Random Forest
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)
- Model Evaluation & Metrics
- Overfitting & Underfitting
3. Deep Learning Fundamentals
- What is a Neural Network?
- Perceptron & Activation Functions
- Loss Functions
- Optimizers (SGD, Adam, RMSProp)
- Backpropagation
- Batching & Epochs
4. TensorFlow Core Concepts
- What is TensorFlow?
- Tensors & Operations
- tf.Variable vs tf.constant
- GPU / TPU Basics
- TensorFlow vs PyTorch
5. Keras API (Practical TensorFlow)
- Sequential API
- Functional API
- Building Your First Neural Network
- Binary Classification
- Multi-class Classification
- Regression with Neural Networks
- Callbacks (EarlyStopping, ModelCheckpoint)
6. Computer Vision with TensorFlow
- Image Classification
- Convolutional Neural Networks (CNNs)
- Image Augmentation
- Transfer Learning
- Fine-tuning Pretrained Models
7. Natural Language Processing (NLP)
- Text Vectorization
- Word Embeddings
- RNN, LSTM, GRU
- Text Classification
- Sequence-to-Sequence Models
- Transformers (Basics)
8. Advanced TensorFlow
- Custom Loss Functions
- Custom Training Loops
- tf.data API
- Model Subclassing
- Mixed Precision Training
9. Model Deployment & MLOps
- Saving & Loading Models
- TensorFlow Serving
- FastAPI + TensorFlow
- Model Versioning
- Monitoring & Retraining
10. Real-World Projects
- End-to-End ML Pipeline
- Image Classifier App
- Text Sentiment Analyzer
- Recommendation System
- ML Project Deployment