Modern Artificial Intelligence Masterclass: Build 6 Projects

Build, train, and deploy two deep neural networks for emotion ai. A cnn with residual blocks predicts facial key points, while an emotion classifier detects emotions.

Explore the theory and intuition of artificial neural networks, from neurons to the single neuron model. See how weights, bias, inputs, and activation functions—sigmoid, Relu, tanh—enable multi-layer perceptron learning.

Learn how artificial neural networks train with gradient descent to minimize loss, using training, validation, and testing splits to ensure generalization, and adaptive learning rate strategies.

Learn convolutional neural networks and residual networks, including feature extraction with kernels and pooling, then train dense classifiers and compare ResNets to AlexNet on ImageNet.

Build a deep residual network for facial key-point detection using Keras and TensorFlow 2.0, featuring conv and identity blocks, batch normalization, and max pooling insights.

Train a facial key points detector model with Adam optimizer and mean squared error loss, save the best model via checkpoint, and store weights in HDF5 and architecture in JSON.

Assess the trained ResNet facial points detection model on unseen data, load the best weights, and evaluate at about 85% accuracy, using GPU training for faster results.

Explore and preprocess a facial expression dataset for emotion classification, converting string pixel data to 96x96 images, and train a five-class model (anger, disgust, sad, happy, surprise) with visualization.

Build and train a facial expression classifier using rest blocks and convolution blocks, with Keras data augmentation and two stages, for five emotions via softmax, with early stopping and checkpointing.

Explore how to assess classifier performance with a confusion matrix, accuracy, precision, and recall, distinguishing true and false positives and negatives, including type one and type two errors.

Combine facial key points and emotion models to produce joint predictions with about 85% accuracy, then visualize results for deployment.

Explore how ai accelerates brain tumor detection and localization in mri scans by building a two-model pipeline: a tumor detector and a u-net segmentation model.

Explore the theory of convolutional neural networks and residual networks, including skip connections, and how CNNs extract features to achieve strong ImageNet performance.

Train a classifier model with transfer learning on a resnet backbone to detect brain tumors in MRI scans, then localize them with a resnet segmentation model, using data generators.

Load a pre-trained two-output classifier from json architecture and weights, use argmax on test predictions, compare to ground truth, and report 98% accuracy with confusion matrix and classification report.

Explore resnet-based segmentation by combining a u-net backbone with residual blocks to enable pixel-level brain tumor localization, using encoder, bottleneck, and decoder with skip connections.

Train a segmentation resnet model to localize tumors using a focal tversky loss and a custom data generator, then save the best weights and architecture.

Load the pre-trained ResNet MRI architecture and segmentation weights, compile with Adam, and run the prediction pipeline to generate and compare predicted masks against ground truth for brain tumor localization.

Kick off a case study applying AI and ML to market segmentation, building targeted campaigns, and exploring exploratory data analysis with autoencoders, k-means, PCA, and Plotly visuals.

Explore AI applications in marketing through unsupervised learning and market segmentation. Build and evaluate clustering models with K-means, the elbow method, PCA, and autoencoders to tailor targeted campaigns.

Understand the theory and intuition of k-means clustering, an unsupervised algorithm that groups data by euclidean distance to centroids, using elbow method to choose clusters and within-cluster sum of squares.

Apply the elbow method to identify the optimal number of clusters for k-means by scaling data, plotting inertia across k values, and selecting the elbow point.

Apply the k-means clustering algorithm with five clusters to scaled sales data, obtain cluster centers and labels, inverse transform centers to original units, and visualize per-cluster distributions.

Apply principal component analysis to reduce features and visualize clustering in 2D and 3D, using k-means and PCA components to reveal distinct groupings and insights.

Explore autoencoders for dimensionality reduction, then apply K-means elbow method and PCA to cluster data and reveal three meaningful customer segments.

Import libraries and the UCI credit card dataset to build a classifier predicting defaults. Describe the 30,000-sample data and features like limit balance, age, and encoded pay statuses.

Visualize and explore a credit card dataset using pandas, numpy, seaborn, and matplotlib; assess data quality, compare defaulted versus non-defaulted customers, and analyze distributions, correlations, and visualizations.

Create a cleaned training and testing dataset by one-hot encoding categorical features and combining them with numerical data for an XGBoost model.

Understand the theory and intuition behind XGBoost, an ensemble of gradient boosted trees learned from residuals, with many hyperparameters for fast, robust regression and classification.

Train and evaluate an xgboost classifier locally with sklearn, then deploy on AWS Sagemaker for inference, using train-test split and a grid search to tune hyperparameters.

Optimize xgboost hyperparameters with grid search in sklearn, testing gamma, subsample, colsample by tree, and max depth to select model and print classification report; explore aws sagemaker auto ml.

Deploy a trained xgboost classifier on aws sagemaker, configure one m4.xlarge instance, and perform inference with text csv data. Evaluate precision, recall, and accuracy, and delete endpoint to avoid charges.

Explore creative ai to craft art masterpieces with the deep dream algorithm. Learn its theory and train a deep dream model using keras and tensorflow 2.0 to create trippy visuals.

Explore how creative AI can generate art, music, and stories, assess AI creativity against human benchmarks, and examine deep dream and neural networks through real-world art projects.

Import a pre-trained inception v3 model with top off and then on, explore activations and transfer learning, compare 21.8 million vs 23.8 million parameters, using TensorFlow 2.2.0 and Keras.

Run a pre-trained Inception net v3, feed an image, extract activations from mixed three, five, seven, and beyond to build a deep dream feature extraction model and visualize layer outputs.

discover how the deep dream algorithm manipulates a pre-trained convolutional neural network to maximize layer activations by gradient ascent, revealing how early edges and deeper features shape dreamlike images.

Explore gradient operations in tf 2.0 with gradient tape to compute first-order derivatives, using y equals x cubed and y equals x to the fourth plus x to the fifth.

Implement the deepdream step two by maximizing activations through gradient ascent on the input image using gradient tape and tf.function, with step size and steps, visualizing loss progress.

Train six ai models in datarobot, including a residual neural network with keras and tensorflow and a logistic regression with l1 and l2 regularization. Evaluate with confusion matrices.

Explore AWS EC2 and IAM essentials, learn how to select instance types for AI and ML workloads, leverage Sagemaker and elastic inference, and understand on-demand, spot, and reserved pricing.

Discover how to create a free AWS account and use the 12-month free tier to run EC2, store data in S3, and build, train, and deploy ML models with SageMaker.

Explore AWS SageMaker Studio as an integrated ML IDE that unifies model building, training, tuning, deploying, and managing experiments with Autopilot, debugging, and edge deployment.

Explore Amazon SageMaker Studio with a hands-on walkthrough of notebooks, kernel selection, and experiment workflows. Learn to upload data, run experiments, compare models, and deploy a production endpoint.