Artificial Intelligence A - Z Concepts with 25+ Projects

Explore the fundamentals of artificial intelligence, including machine learning and deep learning, and see how AI powers daily apps like Google Maps, Netflix, and Instagram.

Learn basic Python concepts and install Python with essential AI libraries like NumPy, pandas, Matplotlib, and Seaborn; explore data loading, dataframes, and visualization in AI contexts.

Explore machine learning basics, its role within artificial intelligence, supervised and unsupervised learning, and the data-driven pipeline for building and evaluating models.

Learn to train, evaluate, and compare machine learning models for heart disease prediction using data collection, preprocessing, train/test split, and algorithms like logistic regression, naive bayes, and random forest.

Learn to build a content-based movie recommendation system using tf-idf vectorization and cosine similarity, leveraging keywords, cast, tagline, and director data for Netflix-style suggestions.

Predict employee salary using linear regression with years of experience as the key feature. Split data into training and testing sets, evaluate with R-squared score and mean squared error.

Master natural language processing (nlp), including natural language understanding and generation (nlu and nlg), and learn pipelines and applications from personal assistants to translation and chatbots.

Discover hate speech detection using NLP, from data cleaning and tokenization to feature extraction with countvectorizer and tfidfvectorizer, and classify with logistic regression.

Explore speech recognition and speech emotion in NLP, converting audio to text via microphone and applying sentiment analysis to classify positive, negative, and neutral content.

Discover how to build a personal AI virtual assistant in Python using NLP and speech recognition to open websites, play music, tell time, and email friends.

Build your first neural network with Keras on a CSV dataset, loading data with pandas. Train, evaluate, and predict using a sequential model on TensorFlow backend for binary classification.

Learn to perform real-time object detection with YOLO v3 in OpenCV, using a pre-trained deep learning model to detect 80 COCO classes with fast, accurate bounding boxes.

Learn to build an image classification model using a convolutional neural network (cnn) to distinguish dogs from cats, train, evaluate, and predict with feature extraction and cnn layers.

Detect leaf diseases with deep learning using a convolutional neural network and computer vision techniques to train, test, and predict 25 disease categories, supporting agricultural productivity.

Explore handwritten text recognition with a cnn-based deep learning approach that converts handwritten Tamil characters from digital images into text, using custom datasets, model training, and prediction.

Train a traffic sign recognition model using a CNN on a 50,000-image dataset with 43 classes, covering data exploration, CNN architecture, training, validation, and testing.

Colorize black-and-white images with OpenCV and a pre-trained deep learning model in the lab color space, predicting the a and b channels from the grayscale L channel.

Explore text extraction by OCR using computer vision and deep learning, employing OpenCV and Tesseract with pytesseract in Python to convert images into editable text.

Build a driver drowsiness detection system with deep learning and computer vision. Train a CNN using OpenCV and Keras to detect eye status in real time and alert the driver.

Learn how to build an image caption generator using cnn feature extraction and lstm-based captioning, with nlp, pre-trained models, and image-caption datasets, training, and testing workflows.

Explore computer vision as a core artificial intelligence technology that enables machines to understand visual data, with OpenCV, image processing, and real-world applications like facial recognition and self-driving cars.

Explore real-time face and eye detection with OpenCV using Haar cascade classifiers and pre-trained XML models, implementing a simple pipeline to detect faces, crop them, and locate eyes.

Detects vehicles using OpenCV and Haar cascade classifier with a pre-trained car XML, draws bounding boxes, and counts vehicles in images and videos for traffic surveillance.

Detect and track objects by color using OpenCV: convert to HSV, create color masks, apply morphological cleanup, and draw contours to highlight color-based objects in images and in real time.

Learn to build a face recognition system by distinguishing face detection from recognition, prepare datasets, train with face recognition library in Python, and deploy testing with encoding and distance comparisons.

Learn to build a real-time face mask detector using convolutional neural networks and MobileNet v2, train on a mask vs no-mask dataset, and deploy for live camera analysis.

Develop a smart attendance system using face recognition with Python and OpenCV, leveraging face detection, feature extraction, and recognition to log attendance.

Develop face emotion recognition with computer vision and deep learning, using pre-trained models to detect faces, extract features, and classify emotions such as happy, sad, anger, fear, surprise, and neutral.