Artificial Intelligence

Step 4: Machine Learning for Computer Vision Classical Machine Learning Techniques: K-Nearest Neighbors (KNN). Support Vector Machines (SVM). Decision Trees and Random Forests. Naive Bayes. Clustering (K-means, DBSCAN). Dimensionality Reduction: Principal Component Analysis (PCA). Linear Discriminant Analysis (LDA). t-SNE (t-Distributed Stochastic Neighbor Embedding). Independent Component Analysis (ICA). Feature selection techniques. Artificial Intelligence

Step 3: Feature Extraction Traditional Feature Detectors: Edge detection (Sobel, Canny). Corner detection (Harris, Shi-Tomasi). Blob detection (LoG, DoG). SIFT and SURF features. ORB features. Image Segmentation: Thresholding. Watershed algorithm. Contours and shape detection. Region growing. Graph-based segmentation. Artificial Intelligence

Step 2: Basic Image Processing Image Manipulation with OpenCV: Reading, displaying, and saving images. Basic operations (resizing, cropping, rotating). Image filtering (blurring, sharpening, edge detection). Handling image channels and color spaces. Image Manipulation with PIL and Scikit-Image: Image enhancement techniques. Histogram equalization. Geometric transformations. Image segmentation (thresholding, watershed). Artificial Intelligence

Introduction to Computer Vision: Understanding images and pixels. Grayscale and color images. Basic image processing operations. Image formats and conversions. Mathematics for Computer Vision: Linear algebra (matrices, vectors, transformations). Calculus (derivatives, gradients). Probability and statistics (distributions, Bayes theorem). Fourier transforms and convolutions. Eigenvalues and eigenvectors. Artificial Intelligence

Before we start, what is computer vision and what do computer vision engineers do? Computer vision is a field of AI that enables machines to interpret and understand visual data from the world, such as images and videos. Computer vision engineers develop algorithms and systems to automate tasks like image classification, object detection, and image segmentation, transforming visual data into actionable insights for various applications including healthcare, autonomous driving, and security. Artificial Intelligence

Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go: 1. Computer vision 2. Natural language processing. I outlined a roadmap for computer vision I believe many beginners will find helpful.

📚 Machine Learning Mastery with Python Jason Brownlee, 2016

How do you start AI and ML ? Where do you go to learn these skills? What courses are the best? There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos. What’s more important than how you start is why you start. Start with why. Why do you want to learn these skills? Do you want to make money? Do you want to build things? Do you want to make a difference? Again, no right reason. All are valid in their own way. Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started. Got a why? Good. Time for some hard skills. I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course I’ve completed courses from (in order): Treehouse / youtube( free) - Introduction to Python Udacity - Deep Learning & AI Nanodegree Coursera - Deep Learning by Andrew Ng fast.ai - Part 1and Part 2 They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that. If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI. Join for more: https://t.me/machinelearning_deeplearning 👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5 Like for more ❤️ All the best 👍👍

MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications. 2023 lectures are starting in just one day, Jan 9th! Link to register: http://introtodeeplearning.com MIT Introduction to Deep Learning The 2022 lectures can be found here: https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

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