Build 75 Powerful Data Science & Machine Learning Projects

Learn to detect ID tampering with computer vision and verify PAN and Aadhaar card authenticity by uploading an image, computing a similarity score, and applying thresholds.

Learn to set up a pan card tampering detection project in Google Colab, using computer vision and structural similarity with scikit-image, cv2, and PIL, fetching images via requests.

Learn to build a pan card detector by resizing input images, converting to grayscale, computing structural similarity, and using thresholds and contours to identify tampering in identity documents.

Create a Flask app to deploy the model, with a project folder and config.py including base, development, and debug settings, plus views handling uploads.

Implement a web app with get and post routes that uploads images, resizes and grayscales them, detects contours and similarity, draws bounding rectangles, saves outputs, and renders index.html with predictions.

Deploy a python web app to Heroku by uploading to GitHub, connecting to Heroku, and deploying the main branch to obtain the app URL. Include requirements.txt and a proc file.

Demonstrates testing a deployed pan card detector with a tampered image in a computer vision app, showing 28.57% correct, and outlines creating essential files, GitHub workflow, and Heroku deployment.

Build a convolutional neural network with Keras and TensorFlow to predict dog breeds from images. Load data from Kaggle, preprocess, train, evaluate, and generate predictions with hyperparameter tuning.

Build a dog breed classification cnn using Keras and TensorFlow in Colab. Set up Kaggle API, download and preprocess the dataset, and train a sequential model with a train-test split.

This lecture covers data preprocessing for dog breed classification: load labels, select three breeds (Scottish Deerhound, Maltese dog, Bernese Mountain Dog), one-hot encode, and normalize images by 255.

Design and train a sequential CNN model with convolutional 2D layers, max pooling, flatten, and dense layers to classify into three classes, using softmax, categorical cross entropy, and Adam optimizer.

Plot model accuracy over epochs with matplotlib, compare training and validation accuracy, and report an 83.33% test accuracy while highlighting how hyperparameters like layers and activations affect results.

Build a Streamlit dog breed predictor by loading the dog breed h5 model, creating a main app.py with image upload and preprocessing for on-page prediction.

Create a requirements file, install dependencies with pip, and run streamlit using main_app.py from the Anaconda prompt to view a dog breed prediction at localhost:8501.

Learn how to implement image watermarking with OpenCV by adding logo and text watermarks for copyright protection, selecting region of interest and visualizing results in Google Colab.

Learn to use Google Colab for image watermarking with OpenCV, NumPy, requests, and PIL to add logo and text watermarks for copyright protection.

Explore how to add image and text watermarks using NumPy and OpenCV, compute image dimensions, center placement, and ROI, and customize patterns and text watermarks for copyright protection.

Create a Flask app for an image watermarking project, configure settings, handle image uploads and watermark types, and prepare for Heroku deployment with pipreqs and a requirements file.

create a new GitHub repository, upload and commit the image watermarking project, connect to Heroku, enable automatic deploys from the main branch, and test logo and text watermarking with requirements.txt.

Build a multi-class traffic sign classifier with a convolutional neural network in TensorFlow and Keras, Kaggle data in Colab, preprocessing, one-hot encoding, training, testing, and visualization for self-driving cars.

Connect Kaggle to Google Colab, download the GT SRB German traffic sign dataset, unzip and prepare the data and libraries to train a CNN classifier with TensorFlow and Keras.

Visualize traffic sign images and preprocess by resizing to 50×50, normalizing pixel values, converting to numpy arrays, applying one-hot encoding to 43 class labels, and performing an 80/20 train‑validation split.

Build and train a cnn architecture with conv 2d layers, max pooling, dropout, and flattening, with hyperparameter tuning, to classify 43 classes using softmax and sparse categorical cross-entropy.

Create a test-set model by cleaning data, scaling images to 50 by 50, normalizing by 255, and predicting 43 traffic sign classes with a model for self-driving cars.

Learn to extract text from images using Tesseract and pytesseract, refine results with OpenCV—noise removal, blur, thresholding, erosion, and rectangle drawing for OCR-based document analysis.

Learn to perform optical character recognition in google colab by installing tesseract, pytesseract, and opencv, downloading an image from the web, and extracting text from it.

Extract text from images with Pytesseract using a custom English configuration on the loaded image via image_to_string, then apply OpenCV steps—grayscale conversion, blur, and thresholding—to clean and improve accuracy.

Explore erosion and morphological transformations with OpenCV to extract text, detect edges, and correct skew in images. Build a pipeline using template matching, pytesseract boxes, and a Flask app.

Create a flask-based optical character recognition app by configuring the project, wiring app files, and handling image uploads and text extraction with a frontend built in html and css.

Install libraries from the requirements.txt with pip install -r requirements.txt, then start the app with python app.py to run the optical character recognition app and test by uploading an image.

Develop a convolutional neural network to detect plant disease using TensorFlow and Keras. Upload data to Colab via Google Drive, preprocess images, and train, validate, and predict results.

Build a cnn classification model in Google Colab to predict plant disease, using TensorFlow and Keras, by mounting Google Drive, loading data, and importing libraries for image processing and training.

Visualize 256x256 RGB images across three plant disease classes, convert to numpy arrays, normalize, and apply one-hot encoding for model training.

Build a three-class plant disease CNN with conv2d, max pooling, flatten, dense layers, softmax, and Adam; train on a train/validation split and save as h5 or json for Streamlit deployment.

Create a streamlit app to deploy a plant disease classifier by loading a Keras model, processing uploaded leaf images with OpenCV, predicting diseases like tomato bacterial spot.

Detect and count vehicles in images and videos using OpenCV and Haar cascades, with grayscale preprocessing, XML classifiers, and contour-based bounding boxes for cars and buses.

Leverage Google Colab to import libraries like OpenCV, NumPy, and PIL, download cars.xml and bus_front.xml Haar cascade files, and detect and count vehicles in images and videos.

Learn to transform images through grayscale conversion, Gaussian blur, dilation, and morphological operations, then detect vehicles using Haar cascades for cars and buses with multi-scale detection, and output annotated videos.

Build a Flask app for vehicle detection and counting using OpenCV cascade classifiers, image preprocessing, and post-processing. Deploy via GitHub and Heroku with a prepared config, requirements, and proc file.

Explore a face swapping project with OpenCV and Dlib, loading a pre-trained shape predictor, extracting landmarks, triangulating faces, and applying seamless cloning to match color.

Import libraries in Google Colab for a face swapping project, using OpenCV, Dlib, NumPy, Requests, and Pillow, and download the 68-face landmarks shape predictor.

Convert images to NumPy arrays and grayscale for preprocessing, create a zero mask, detect facial landmarks with Dlib, triangulate and warp, and output the final swapped image with color correction.

Create a Flask Faceswap app by building config.py, app.py, init.py, and views.py, handling image uploads, performing face detection with dlib, and designing a front end with HTML and materialize.

Develop a convolutional neural network to predict bird species from images using Keras in Google Colab, covering data prep, model architecture, training, evaluation, and visualization of predictions.

Mount Google Drive in Colab to load the bird species dataset and build a Keras-based convolutional neural network for multi-class classification, tuning activation, learning rate, batch size, and epochs.

Analyze six bird classes with 224×224 rgb images and 811 labels. Perform train-test split, normalize by 255, reshape xtrain and xtest, and one-hot encode.

Create a convolutional neural network sequential model with conv2d, max pooling, flatten, and dense layers to classify bird species using softmax.

Build a flask app for a bird species classification model, loading a six-class model from static/model/bird species.h5, handling uploads, and deploying with a proc file and requirements.txt.