Artificial Intelligence

Complete Roadmap to learn Machine Learning and Artificial Intelligence 👇👇 Week 1-2: Introduction to Machine Learning - Learn the basics of Python programming language (if you are not already familiar with it) - Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning - Study linear algebra and calculus basics - Complete online courses like Andrew Ng's Machine Learning course on Coursera Week 3-4: Deep Learning Fundamentals - Dive into neural networks and deep learning - Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) - Implement deep learning models using frameworks like TensorFlow or PyTorch - Complete online courses like Deep Learning Specialization on Coursera Week 5-6: Natural Language Processing (NLP) and Computer Vision - Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis - Dive into computer vision concepts like image classification, object detection, and image segmentation - Work on projects involving NLP and Computer Vision applications Week 7-8: Reinforcement Learning and AI Applications - Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks - Explore AI applications in fields like healthcare, finance, and autonomous vehicles - Work on a final project that combines different aspects of Machine Learning and AI Additional Tips: - Practice coding regularly to strengthen your programming skills - Join online communities like Kaggle or GitHub to collaborate with other learners - Read research papers and articles to stay updated on the latest advancements in the field Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible. 2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day. Best Resources to learn ML & AI 👇 Learn Python for Free Prompt Engineering Course Prompt Engineering Guide Data Science Course Google Cloud Generative AI Path Unlock the power of Generative AI Models Machine Learning with Python Free Course Machine Learning Free Book Deep Learning Nanodegree Program with Real-world Projects AI, Machine Learning and Deep Learning Join @free4unow_backup for more free courses ENJOY LEARNING👍👍

10 Things you need to become an AI/ML engineer: 1. Framing machine learning problems 2. Weak supervision and active learning 3. Processing, training, deploying, inference pipelines 4. Offline evaluation and testing in production 5. Performing error analysis. Where to work next 6. Distributed training. Data and model parallelism 7. Pruning, quantization, and knowledge distillation 8. Serving predictions. Online and batch inference 9. Monitoring models and data distribution shifts 10. Automatic retraining and evaluation of models Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 All the best 👍👍

WebScraping with Gen AI During this session, we'll explore the following topics: 1️⃣ Basics of Web Scraping: Understand the fundamental concepts and techniques of web scraping and its legal and ethical considerations. 2️⃣ Scraping with Gen AI: Discover how Gen AI revolutionizes the web scraping landscape with real-world examples. 3️⃣ Jina Reader API: Get acquainted with the Jina Reader API, a powerful tool for obtaining LLM-friendly input from URLs or web searches. 4️⃣ ScrapeGraphAI: Dive into ScrapeGraphAI, a groundbreaking Python library that combines LLMs and direct graph logic for creating robust scraping pipelines. Event Details: 🗓 Date: 22 June, Saturday ⏰ Time: 11:00 AM IST 🔗 Register now: https://www.buildfastwithai.com/events/web-scraping-with-gen-ai Connect with Founder from IIT Delhi; https://www.linkedin.com/in/satvik-paramkusham/

If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc. Yes, you might hear a lot about them or some other trending technology of the year...but guess what! Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy. Instead, here are basic skills that will get you further than mastering any framework: 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML. You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability 𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning. 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks. You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/ 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process. 𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚: Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently. You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai I love frameworks and libraries, and they can make anyone's job easier. But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 All the best 👍👍

WebScraping with Gen AI During this session, we'll explore the following topics: 1️⃣ Basics of Web Scraping: Understand the fundamental concepts and techniques of web scraping and its legal and ethical considerations. 2️⃣ Scraping with Gen AI: Discover how Gen AI revolutionizes the web scraping landscape with real-world examples. 3️⃣ Jina Reader API: Get acquainted with the Jina Reader API, a powerful tool for obtaining LLM-friendly input from URLs or web searches. 4️⃣ ScrapeGraphAI: Dive into ScrapeGraphAI, a groundbreaking Python library that combines LLMs and direct graph logic for creating robust scraping pipelines. Event Details: 🗓 Date: 22 June, Saturday ⏰ Time: 11:00 AM IST 🔗 Register now: https://www.buildfastwithai.com/events/web-scraping-with-gen-ai Connect with Founder from IIT Delhi; https://www.linkedin.com/in/satvik-paramkusham/

For working professionals willing to pivot their careers to AI: Here are the steps you can take right now: 1. Learn the basics of AI ================== You need to understand the differences among various AI jargons (e.g., what is the difference between statistical ML vs. deep learning? What exactly is an LLM?) and when to use which to solve a given business problem. Many fast-paced courses can teach you all of this without having to learn coding. (Shameless plug: I have a course that I will add in the comments section below) 2. Build an AI project in your current work ============================== Find a problem statement in your current work that can be solved using AI and will deliver some value. Work on this during your extra hours, then showcase it to your management to get official approval to make it a full-fledged project. 3. Collaborate with the AI team in your company for inner sourcing ================================================ Many companies have the concept of inner sourcing where, say, an AI team is too busy and has a list of tasks they have opened on their GitHub repository that others can work on. Use this as an opportunity to do some real AI work and build rapport with the AI team. 4. Attend AI conferences ================== By attending AI conferences, you will not only learn but also build a network with AI professionals who will help you in your AI career journey. 5. Attend an AI bootcamp at a university or online learning company ================================================= Artificial Intelligence 👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5 Like for more ❤️ All the best 👍👍

Save significant time every day with these ChatGPT's 7 prompts: 1. Make Hard Topics Easier to Understand: Prompt: Divide the (topic) into smaller, simpler pieces. Use comparisons and examples from everyday life to make the idea easier to grasp and more relevant. ChatGPT Prompts

Step 9: Career and Freelance Tips (1/2) Searching for Internships and Jobs: Using job portals and company websites. Leveraging university career centers. Joining professional associations. Networking and referrals. Crafting effective resumes and cover letters. (2/2) Preparing for Interviews: Technical preparation (coding practice, concepts). Behavioral preparation (STAR method). Researching companies. Mock interviews. Useful apps for interview preparation. Working with Freelance: Finding opportunities on freelance platforms. Building a strong profile and portfolio. Managing projects and client communication. Time management and payment methods. Artificial Intelligence Hope this helps you ☺️

People: AI will rule the world Meanwhile AI

Step 8: Practical Applications and Projects Identifying Real-World Problems: Planning and Outlining Projects: Choosing the Right Algorithm: Overcoming Overfitting: Building a Strong Portfolio Artificial Intelligence

Step 7: Generative Models Variational Autoencoders (VAEs): Encoder and decoder networks. Latent space representation. Reparameterization trick. KL divergence loss. Applications (data generation, anomaly detection). Generative Adversarial Networks (GANs): Generator and discriminator networks. Adversarial training. Loss functions (minimax, Wasserstein). DCGANs (Deep Convolutional GANs). Applications (image generation, style transfer). Artificial Intelligence

Step 6: Advanced Topics in Computer Vision Object Detection: Region-based methods (R-CNN, Fast R-CNN, Faster R-CNN). YOLO (You Only Look Once). SSD (Single Shot MultiBox Detector). RetinaNet. Anchor boxes and non-maximum suppression. Image Segmentation: Semantic segmentation (U-Net, SegNet). Instance segmentation (Mask R-CNN). Panoptic segmentation. Fully Convolutional Networks (FCNs). CRFs (Conditional Random Fields). Artificial Intelligence

Step 5: Deep Learning for Computer Vision Convolutional Neural Networks (CNNs): Convolutional layers. Pooling layers. Fully connected layers. Activation functions (ReLU, Sigmoid, Tanh). Batch normalization and dropout. Advanced CNN Architectures: AlexNet and VGGNet. ResNet (Residual Networks). Inception and GoogLeNet. DenseNet (Densely Connected Networks). MobileNet and EfficientNet. 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