Mastering Artificial Intelligence

Learn how machine learning, a subset of artificial intelligence, lets computers learn from data, analyze patterns, and predict by cycling through collection, wrangling, analysis, training, testing, and deployment.

Learn data preprocessing for machine learning by collecting data, imputing missing values, performing one-hot and label encoding, splitting into train and test sets, and scaling with a standard scaler.

Explore evaluation metrics for machine learning, including AUC (accuracy), precision, recall, F1 score, and confusion matrix, to assess model performance and choose the best algorithm.

Explore the types of machine learning, focusing on supervised learning with regression and classification, and study algorithms like linear and logistic regression, decision trees, random forests, naive Bayes, and SVM.

Build a linear regression model for house price prediction in a supervised learning lab, using data import, preprocessing, train-test split, training, evaluation with R2, and result visualization.

Explore supervised learning algorithms including logistic regression for binary classification, decision trees and random forests, naive Bayes, and support vector machines, with practical data preprocessing and evaluation examples.

Mastering artificial intelligence: compare classification algorithms on the magic telescope dataset, train and evaluate models like decision tree, random forest, and SVM, with random forest achieving the highest 87% accuracy.

Explore unsupervised learning with unlabeled data to uncover patterns, structures, and relationships, using clustering and k-means to group similar data for tasks like customer segmentation.

Learn how to build a k-means clustering model for unsupervised data segmentation, using mall customer data to form clusters by annual income and spending score via elbow method visualization.

Deep learning is an advanced subset of artificial intelligence that uses multi-layer neural networks to learn and make decisions, enabling image and speech recognition and autonomous vehicles.

Explores how artificial neural networks power recommender systems by processing data through input, hidden, and output layers with weights, biases, and activation functions like ReLU and sigmoid.

Build a simple artificial neural network by preprocessing data with numpy and pandas, encoding features, splitting into train and test sets, and training a Keras sequential model to evaluate accuracy.

Explore convolutional neural networks (cnn) for image recognition, object detection, and image segmentation, and learn the six-layer architecture—from convolution to output, including relu activation, pooling, flattening, and fully connected layers.

Build and train a convolutional neural network for image classification using Keras on the cifar-10 dataset, with conv2d, pooling, dropout, flatten, dense, and softmax, including preprocessing and one-hot encoding.

discover how recurrent neural networks process sequential data using hidden state memory to forecast time series, analyze sentiment, generate text, and support natural language processing and speech recognition.

Build and train an rnn sunspot model that forecasts future sunspots from sequential data using a simple rnn layer, a dense layer, minmax scaling, and Keras.

Explore natural language processing, a deep learning driven subfield of artificial intelligence. Enable computers to understand and generate human language through voice assistants, translation, and chatbots.

Mastering artificial intelligence explains text pre-processing in natural language processing, outlining lowercasing, tokenization, punctuation and number removal, stopword removal, stemming, lemmatization, and contraction handling for natural language processing tasks.

Learn to preprocess text for machine learning by applying lowercasing, tokenization, punctuation and number removal, stopword filtering, and stemming and lemmatization.

Explore sentiment analysis, a natural language processing task that detects emotional tone in text and classifies it as positive, negative, or neutral, with applications in business feedback and social media.

Learn language modeling and generation in natural language processing, compare recurrent neural networks and transformer models with self-attention, and explore applications like text completion and machine translation.

Learn to build a sentiment analysis model from Amazon reviews using text preprocessing, tokenization, and polarity scores, then compare a RoBERTa-based transformer approach and visualize results.

Learn how computer vision enables machines to interpret visual information, with image processing and feature extraction, and applications in autonomous vehicles, surveillance, and medical imaging.

Explore image processing as a cornerstone of computer vision, detailing image acquisition, pre-processing, enhancement, restoration, transformation, segmentation, feature extraction, and compression for machine learning workflows.

Mastering artificial intelligence explains feature extraction in computer vision. It shows how raw images are processed to extract informative features and patterns for face recognition and object detection.

Explore object detection in computer vision, from input images and feature extraction to localization with bounding boxes and classification, including examples like face recognition and attendance.

Explore image segmentation as a key computer vision task, outlining semantic, instance, and boundary-based approaches, and show how segmentation complements object detection and localization.

Explore the steps of image segmentation, from input image and feature extraction to pixel grouping, segmentation algorithms, and post-processing, then cover image classification with convolutional neural networks and deployment.

Build a face mask detection model with a CNN using a Kaggle dataset of masked and unmasked faces. Train, evaluate, and predict mask presence in new images.

Learn how reinforcement learning trains agents to maximize rewards through trial and error in an environment. Explore states, actions, rewards, policies, and Markov decision processes, plus Q-learning and policy gradients.

Learn Q-learning to estimate Q-values from experience using the Bellman equation, then apply value iteration to update the value function and extract the optimal policy toward maximum rewards.

Explore reinforcement learning by building an environment and agent, defining observations, actions, and rewards in Python within Google Colab, with rewards chosen randomly.

Develop a heart disease prediction model as a capstone project, from dataset preparation and logistic regression training to evaluating accuracy and deploying a web app with streamlit.