Applus+ IDIADA’s Post

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 Today, we started a new module in our Data Science and AI bootcamp with atomcamp. The class was very interactive, and Ma'am Mahnoor Salman, as usual, delivered an exceptional lecture on the introduction to machine learning. One aspect I particularly appreciate about her classes is her approach to new concepts and her encouragement for us to ask questions. The topics were covered are as follows: 𝑰𝒏𝒕𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏 𝒕𝒐 𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈: Machine Learning is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. It involves developing algorithms that allow computers to identify patterns and make decisions based on data. The goal is to create models that can make predictions or take actions based on input data. 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝙞-𝙎𝙪𝙥𝙚𝙧𝙫𝙞𝙨𝙚𝙙 𝙈𝙇: In supervised learning, the model is trained on a labeled dataset, meaning that each training example is paired with an output label. The model learns to map inputs to outputs based on this data. Common supervised learning tasks include classification (e.g., spam detection) and regression (e.g., predicting house prices). 𝙞𝙞-𝙐𝙣𝙨𝙪𝙥𝙚𝙧𝙫𝙞𝙨𝙚𝙙 𝙈𝙇: Unlike supervised learning, unsupervised learning involves training a model on data that does not have labeled responses. The goal is to identify patterns or structures within the data. Common techniques include clustering (e.g., customer segmentation) and dimensionality reduction (e.g., reducing the number of features in a dataset while retaining important information). 𝙍𝙚𝙜𝙧𝙚𝙨𝙨𝙞𝙤𝙣: Regression is a type of supervised learning that focuses on predicting a continuous outcome variable based on one or more predictor variables. The aim is to model the relationship between the input variables and the continuous output. For example, in predicting house prices, regression models use features like square footage and number of bedrooms to estimate the price of a house. These topics form the foundation of machine learning and are essential for understanding more complex models and applications. #atomcamps #DataScience #MachineLearning #AI #BigData #DataAnalytics #DeepLearning #DataVisualization #Python #Statistics #DataEngineer #ML #DataScientist #AIResearch #NeuralNetworks #DataMining #DataScienceCommunity #ArtificialIntelligence #AIandML #PredictiveAnalytics #DataDriven

I recently completed a project as part of my machine learning and AI coursework. Here are some highlights: Problem Statement: In this project, I focused on developing a text summarization tool to efficiently condense long pieces of text into shorter, more digestible summaries. Methodology I leveraged the powerful [Hugging Face transformers library] (https://huggingface.co/)  to implement the summarization task. Key steps included: -Installation and Setup: Ensured all necessary libraries, such as Gradio for interface creation and transformers for model handling, were installed. -Model Selection: Chose the "Falconsai/text_summarization" model from Hugging Face. - Pipeline Creation: Utilized the `pipeline` function from transformers to streamline the text summarization process. Key Findings: The summarization tool successfully generated concise summaries while retaining the essential information from the original texts. This model demonstrated high accuracy and efficiency, making it a valuable asset for processing extensive textual data. Impact: The potential applications of this summarization tool are vast, ranging from content creation and news aggregation to academic research and business intelligence. It can significantly enhance productivity by reducing the time spent on reading and information extraction. 🔗 Check out the full code on GitHub:   [ https://lnkd.in/gffVR5Vi) #DataScience #MachineLearning #AI #Python #DataAnalytics #TextSummarization #HuggingFace

Great demo to understand the agentic workflow to use genAI to solve a problem. A new normal now.

Excited to share that I’ve completed a new course, "Artificial Intelligence Applied to Business and Companies" on Udemy! 🎓 The course covers three real-world business case studies, each solved with different AI techniques: - Process Optimization: Improving e-commerce warehouse flow with Q-Learning. - Cost Minimization: Reducing energy expenses in data centers using Deep Q-Learning. - Profit Maximization: Boosting online retail profits with Thompson Sampling. The course includes advanced mathematical and probabilistic concepts, detailed Machine Learning models, and full AI implementation in Python. Looking forward to applying this knowledge to drive innovation and efficiency in business! 🚀 #AI #BusinessOptimization #MachineLearning #DeepLearning #ArtificialIntelligence

You train a machine learning model and it does exceedingly well on your training data but the results on the validation/testing sets are not great. What is going on and what can you do? This is a common example of overfitting where the model memorizes the training data and is not generalizable to unseen data. This could be due to issues like poor paremeter choice, lack of sufficient feature engineering, training for too many epochs, poor preprocessing leading to noisy data, overly complex model trained on limited data, and so on. What can we do about overfitting? Among other solutions, we can use a technique known as regularization. In most literature, one will come across terms like L1/L2 regularization which respectively use penalize the absolute values of the weights and large weights to minimize the effects of overfitting. In L1 regularization, the L1 norm is the sum of the absolute value of the vector values so for an array [3, 13, 24], the L1 norm would be 40. This is interesting because it is also the Manhattan distance from the point (0, 0) or the origin. How is this useful in preventing overfitting? By penalizing the sum of the absolute values of the weights, some weights will be completely removed from the model leading to a less complex model. This is also problematic because we now introduce sparsity in the model which can lead to information loss. Okay, how about L2 regularization? Instead of penalizing the sum of the absolute values, L2 penalizes large weights based on the sum of the squares of all weights ("bigger" squares will be penalized more). By "removing" these large weights, the model optimizes for smaller weighted solutions significantly reducing the model complexity, and improving model performance. This can lead to better generalizability as the model will focus on learning patterns instead of memorizing a specific pattern. Other solutions to the overfitting problem include dropout where a certain percentage of neurons are dropped during training and data augmentation that introduces novelty during the training phase leading to better pattern recognition. What methods have you used to tackle overfitting and which ones do you like best? PS: This image is largely generated with an LLM and may be erroneous. #python #aicommunity #ai #datascience #machinelearning

🚀 Creating IIT-JEE Question Paper using AI Agents The recent release of JEE Advanced results took me back to the day when I nervously checked my own scores. As a fun experiment, I wondered: could I use Generative AI to create an entire JEE question paper from scratch? So, I created AI agents to generate high-quality JEE questions. Here's how I built it: Simply prompting LLMs to give good questions didn't work well because the quality and difficulty level of the questions were inconsistent. I used the CrewAI agent framework and GPT-4o model for this exercise. The process involves two key agents: 1️⃣ The Question Creator Agent crafts thought-provoking questions based on specific topics from the IIT-JEE syllabus. 2️⃣ The Question Reviewer Agent meticulously reviews each question, providing feedback and suggestions to refine them. These agents converse among themselves to generate high-quality, challenging questions that test the analytical skills and conceptual understanding of JEE aspirants. See the video below for a tutorial. I created a detailed Python notebook to show you how you can do it too! (Link in comments) —— Want to learn how to build AI apps? Join me for a free, 1-hour “Function Calling with LLMs” workshop on Friday, where we will explore how to add superpowers to LLMs. Register here: https://lnkd.in/gJwkcJXg #AIAgents #GPT4o #BuildFastwithAI #FunctionCalling #FreeWorkshop 

𝐃𝐚𝐲 𝟰𝟰/𝟏𝟎𝟎: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝟏𝟎𝟎 𝐝𝐚𝐲𝐬𝐨𝐟𝐌𝐋 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗠𝗟) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn and make predictions or decisions based on data. The primary goal of machine learning is to develop systems that can automatically learn and improve from experience without being explicitly programmed. Thanks to Daniel Etukudo for bringing this great initiative and Simplilearn for the video material I am currently using to learn machine learning   #100daysofML #100daysofAI #dalensai #learning #Data #datascience #datascientist #datastorytelling #coursera #100daysofcode #100daysofcodechallenge #100daysofcodingchallenge #100daysoflearning #100daysofdatascience #100daysofpython #100daysofai

Hooyia: Start Traing your own Machine Learning Models Ready to transform your data into actionable insights? Hooyia’s advanced machine learning course equips you with the skills to develop cutting-edge AI solutions. Our course covers: Master Python for AI: Learn the programming language that powers today’s most sophisticated machine learning models. Scikit-learn: Dive into this essential library for building robust machine learning models with ease. TensorFlow & Keras: Unlock the potential of deep learning with these powerful frameworks, creating models that can recognize patterns and make predictions. Our Expertise, Your Advantage: Custom Machine Learning Models: Tailor AI solutions to meet your specific needs, from prediction to automation. Data Analysis: Turn raw data into meaningful insights that drive smarter decisions. Adaptive AI Systems: Build systems that learn and evolve, adapting to new data and challenges. Optimization of Decision-Making: Use AI to streamline processes and enhance decision-making in your organization. Real-World Application: Weather Prediction Model As part of our course, you’ll get hands-on experience by training a weather prediction model. Imagine predicting tomorrow’s weather with AI—this is just one of the exciting projects you’ll work on! Don’t Miss Out! Join Hooyia and explore the endless possibilities of AI. Take your skills to the next level and become a leader in the AI revolution. 📍 Location: Bafoussam, Deuxieme Carrefour Eveche 📧 Contact: contact@e-hooyia.com #ml #HooYia #AI #machinelearning #artificialintelligence #apprentissageautomatique #python #tensorflow #numpy #llm #LLM #cameroon #Bafoussam

Unveiling the #Magic: How Decision Trees Learn in Supervised #MachineLearning Today's #LearningInPublic adventure delves deeper into the fascinating world of decision trees, specifically focusing on their learning process in supervised learning. We previously explored how decision trees resemble #flowcharts, making #predictions based on a series of #questions. But how do these trees actually learn these questions? The learning process involves iteratively splitting the data based on the most informative feature. 1. The tree starts with the entire dataset at the root node. 2. It analyses all available features in the data (e.g., customer age, income, usage history). 3. The tree identifies the feature that best separates the data points based on their behaviour. (Term: This is called the Gini impurity for classification tasks) 4. The tree creates a split (branch) at the root node based on this chosen feature. Data points satisfying the split condition go down one branch, while others go down the other. 5. This process continues for each branch, with the tree further splitting the data based on the most informative feature at each node, ultimately forming a tree-like structure. Through this iterative process, the decision tree learns the key factors influencing decisions and can then use this knowledge to predict new data. #MachineLearning #AI #LearnInPublic #DeepLearning #ArtificialIntelligence #NeuralNetworks DeepLearning.AI #python #code #pytorch #tensorflow #buildinpublic #DataScience 

🚀 Supercharge Your Machine Learning Journey: Embrace Essential Books for Beginners! 📚💡 Embarking on a career in machine learning is an exhilarating ride, and I'm here to equip you with the ultimate toolkit for success! 💥 Dive into these indispensable books that will not only guide you but also inspire and empower you along the way: "Pattern Recognition and Machine Learning" by Christopher M. Bishop This cornerstone text is your passport to understanding the intricate world of pattern recognition and its applications in machine learning. Get ready to unlock your potential and soar to new heights! 🧠💡 #PatternRecognition #MachineLearning #ChristopherBishop "Introduction to Statistical Learning" by Gareth James et al. Prepare to embark on an exciting journey into the statistical realms of machine learning with this gem of a book. With a perfect blend of theory and practice, you'll be equipped to tackle any challenge that comes your way! 📊🔬 #StatisticalLearning #DataScience #GarethJames "Deep Learning" by Ian Goodfellow et al. Get ready to dive deep into the revolutionary world of deep learning with this comprehensive guide. From neural networks to convolutional wonders, the possibilities are endless – and you're about to become a master of them all! 🌟🔍 #DeepLearning #NeuralNetworks #IanGoodfellow "Machine Learning Yearning" by Andrew Ng Written by none other than the maestro himself, Andrew Ng, this book is your roadmap to building effective and efficient machine learning systems. Get ready to unleash your creativity and transform your ideas into reality! 💪📈 #MachineLearningYearning #AndrewNg #AI "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili Say hello to your new best friend in the world of practical machine learning! With Python as your weapon of choice, you'll be armed and ready to tackle any challenge that comes your way. Get set to code your way to success! 🐍💻 #PythonML #ScikitLearn #SebastianRaschka Armed with these invaluable resources, you're not just embarking on a journey – you're stepping into a world of endless possibilities and boundless joy! ✨ So, dive in, soak up the knowledge, and let's make magic happen together! 🚀 #AI #DataScience #ContinuousLearning #FutureReady