Python & TensorFlow: Deep Dive into Machine Learning

TensorFlow is an open source framework by Google for building and training models, with a core graph and tensors, high level APIs like Keras, and cross platform deployment.

Learn how to install and set up TensorFlow across Windows, Mac, and Ubuntu by installing Python, adding it to your path, and running pip install tensorflow.

Explore TensorFlow architecture by examining graphs, tensors, operations, and sessions, and learn how dataflow enables machine learning and deep learning through backpropagation and SGD optimization.

Learn the fundamentals of APIs, including REST, SOAP, and GraphQL, and how to use documentation, endpoints, and HTTP requests to fetch data with Python.

Explore TensorFlow APIs, including Keras for building and training deep neural networks, TensorFlow Lite for mobile devices, TensorFlow.js for in-browser ML, and TFX for production pipelines.

Explore supervised learning, training models to predict outputs from labeled data, using features and labels, and distinguish regression from classification, with training and test sets to ensure generalization.

Explore linear regression, a supervised learning method that fits the best line to predict outcomes from features like height and weight, using Python libraries such as NumPy, Matplotlib, and scikit-learn.

Explore logistic regression for binary outcomes with a sigmoid model, maximum likelihood estimation, ROC–AUC evaluation, and one hot encoding on a heart disease dataset in Python.

Learn how decision trees perform classification and regression by testing attributes at nodes, splitting data via information gain and other criteria, and visualizing interpretable models with iris dataset examples.

Explore random forests, an ensemble method combining multiple decision trees to improve accuracy and reduce overfitting. Implement in Python with scikit-learn, train on breast cancer dataset, and visualize feature importances.

Explore support vector machines (SVMs) and implement them in Python with scikit-learn, using linear and non-linear kernels to define the maximum-margin hyperplane for classification and regression.

Explore unsupervised learning to discover patterns in unlabeled data, compare it with supervised learning, and review algorithms like k-means, PCA, and GANs.

Explore k-means clustering, an unsupervised learning method that partitions data into k clusters by iteratively assigning points to nearest centers, updating means, and converging, illustrated with Python and scikit-learn.

Explore hierarchical clustering in Python, using agglomerative clustering and dendrogram visualizations with scikit-learn and scipy to reveal nested data structures in a 2D dataset.

Apply principal component analysis to reduce the iris dataset from four features to two principal components. Visualize the transformed data with a scatter plot colored by species.

Explore neural networks inspired by the brain, with input, hidden, and output layers, weights, biases, activation functions, and training via back propagation.

Learn how a two-to-three-layer neural network with a hidden layer works and is built in Python with TensorFlow, trained on xor data using sigmoid activation and gradient descent, then tested.

Explore convolutional neural networks for image recognition, including convolutional layers, pooling, activations, and fully connected layers. Build CNNs with TensorFlow and Keras on cifar ten dataset, applying batch normalization and dropout.

Explore recurrent neural networks for processing sequential data with a hidden state and LSTM, applying Keras on IMDb with max features 10,000 and max length 500 to reach 0.85 accuracy.

Define the problem, gather and preprocess data, choose architecture, train, test, and optimize a deep neural network with two hidden layers of 64 relu neurons for binary classification using TensorFlow.

Split data into training and testing sets with 80/20 (or 70/30) using random_state for reproducibility, and apply random or stratified splits to prevent overfitting.

Learn to evaluate models with metrics like accuracy, precision, recall, F1 score, MSE, and R2, using a notebook to predict y_pred and split data into training and testing sets.

Explore underfitting and overfitting, diagnose when models are too simple or too complex, and apply fixes like polynomial features, regularization, and cross-validation.

Explore hyperparameter tuning techniques, including manual search, grid search, random search, and Bayesian optimization, to optimize model performance, prevent overfitting, and improve generalization using cross-validation and metrics.

Save and restore models in scikit-learn and TensorFlow, using joblib for scikit-learn and model.save with tf.keras for TensorFlow, for prediction and further training.

Deploy TensorFlow models using TensorFlow serving and TensorFlow.js by exporting with saved_model.save, converting to TensorFlow.js format, loading models, and making inferences through REST APIs.

Explore distributed TensorFlow for deep learning across multiple machines, covering data and model parallelism, parameter servers, and strategies to address communication, synchronization, and scalability.

Explore TensorBoard for visualization and debugging in TensorFlow, mastering metrics, graphs, histograms, and high-dimensional embeddings with the projector, plus profiling GPU and memory use during training.

Explore image classification with a convolutional neural network on the MNIST dataset, including data preprocessing, CNN architecture, training with Adam and categorical cross-entropy, evaluation, and predicting labels.

Master machine learning foundations and deep learning with TensorFlow, covering supervised and unsupervised methods, neural networks, CNNs and RNNs, and image classification deployment.