P.01Understand the key differences between artificial intelligence, machine learning, and deep learning with clear definitions, examples, and real-world applications.
P.02Learn OpenCV fundamentals including image I/O, pixel manipulation, color conversion, resizing, filtering, edge detection, and feature detection with SIFT and SURF.
P.03Understand the divide and conquer algorithmic paradigm through the maximum subarray sum problem, with Python implementation and step-by-step analysis.
P.04EditorPickA comprehensive guide to EDA covering visualization techniques, summary statistics, correlation analysis, data cleaning, PCA, anomaly detection, and feature engineering.
P.05EditorPickMaster bitwise operations, bitmasking, bit manipulation tricks, and bit-based algorithms for competitive programming and software engineering interviews.
P.06Build a CNN model using ResNet50 transfer learning to classify gender from eye images, covering data preprocessing, model architecture, and evaluation.
P.07A step-by-step guide to writing and publishing research papers in artificial intelligence, machine learning, and deep learning — from ideation to submission.
P.08Learn to predict stock prices using Long Short-Term Memory (LSTM) networks in Python with TensorFlow, from data preprocessing to building and evaluating the model.
P.09A hands-on guide to building a fruit image classifier using transfer learning with ResNet50 in TensorFlow and Keras, achieving high accuracy on the Fruits-360 dataset.