#pythonml نتائج البحث

Closed the module with the Transformer architecture and the innovations behind modern language models. From Shakespeare-style text generation to Neural Machine Translation — this chapter was a perfect finish. On to the next phase 💪 I'm not stopping.. #pythonml #learningeveryday

Day 37/40 of Python-ML. Today I continued with Deep Computer Vision using CNNs and explored how models learn to see patterns. Covered: • Visual cortex inspiration • Convolutional layers and filters • Stacking feature maps • Pooling layers Steady progress. #pythonml

Day 36/40 of Python-ML. Continuetion--Feature Preprocessing Today I added: . One-hot encoding for categorical features . Embedding-based encoding . Keras preprocessing layers . TF Transform & TFDS for scalable data pipelines Getting closer to building smarter pipelines. #pythonMl

Day 34/40 of python-ml. Today I went deeper into TensorFlow—beyond basic models. Covered: • TF vs NumPy • Custom losses, metrics & layers • When to use dynamic models • Built my own Layer Normalization • Wrote a full custom training loop for Fashion-MNIST #pythonML

Day 33/40 of Python-ML. Custom ML Models. Today I covered: Custom loss functions & metrics Custom activations and regularizers Building custom layers & models Using Autodiff for gradients Writing custom training loops Boosting performance with "tf.function" #pythonML

Day 32/40 of Python-ML. Training neural networks configuration. Wrapped up yesterday’s chapter on avoiding overfitting — learned how techniques like L1/L2 regularization, Dropout, and Max-Norm Regularization which help models generalize better... #pythonMl

. Backpropagation helps networks learn complex patterns Secondly, Using Keras with TensorFlow 2, I explored: . Building models via Sequential, Functional & Subclassing APIs . Monitoring training with Callbacks & TensorBoard . Saving and restoring trained models #pythonML #keras

Day 29/40 of Python-ML. Building on yesterday’s unsupervised learning, I dived deeper into anomaly & novelty detection Covered: • PCA for anomaly detection • Elliptic Envelope (Fast-MCD) • Isolation Forest • Local Outlier Factor (LOF) • One-Class SVM #pythonML

Day 28/40 of Python-ML. Unsupervised Learning Techniques. Today I explored how models find patterns without labels. Covered: • Clustering & K-Means • Image segmentation & preprocessing • DBSCAN & Gaussian Mixtures • Anomaly & novelty detection #pythonMl

Day 27/40 of Python-ML. Dimensionality Reduction Learned how to simplify complex data while keeping its essence. Covered: • The curse of dimensionality • Projection & manifold learning • PCA & explained variance • Kernel & Incremental PCA • LLE & others #pythonMl

Day 26/40 of Python-ML. Ensemble Learning & Random Forests. Explored how combining models boosts performance! Covered: • Voting classifiers • Bagging, pasting & out-of-bag evaluation • Boosting (AdaBoost, Gradient Boosting) • Stacking & feature importance #pythonML

Day 25/40 of Python-ML. Decision Trees. Today I learned how models split data to make smart predictions. Covered: • Training & visualizing trees • Making predictions & estimating probabilities • Gini impurity vs entropy • Decision trees for regression. #PythonML

Day 24/40 of Python-ML. Today I explored: Support Vector Machines (SVMs) Covered: • Linear & nonlinear SVMs • Polynomial & RBF kernels • Soft margins & optimization • SVM for regression SVMs show how math + geometry combine to create strong decision boundaries. #pythonML

Day 23/40 of python-ML. Still on Training models- today i learnt how to improve model performance through: .Learning Curves .Regularized Linear Models — Ridge, Lasso, and Elastic Net .Early Stopping to prevent overfitting and save training time. #pythonML

Day 22/40 of python-ML. Today I explored Training Models -learning how machines actually learn! From Linear Regression and the Normal Equation to Gradient Descent methods (Batch, Stochastic, Mini-batch), all key to understanding how models fit and improve over time. #PythonML

Day 21/40 of Python-ML. Classification in ML. Today I Explored how models classify data using the MNIST dataset. Learned about: • Binary & multiclass classification • Confusion matrix, precision & recall • ROC curve & trade-offs #pythonMl #LearningEveryday

Day 19/40 of Python-ML. Today I dived into a complete ML workflow using real-world data! I learned how to: • Frame a problem & choose performance metrics • Explore, visualize & find correlations • Clean & prepare data • Handle categorical/text features #pythonML #End2EndML

ML isn’t just about algorithms — it’s about understanding data and building reliable models. Key lessons so far : • Not enough or poor-quality data hurts performance • Overfitting & underfitting are real struggles • Model tuning and validation are crucial #pythonML

Day 17/40 of Python-ML. Today I explored how Python connects to real-world data using Web APIs. - Made API calls with requests - Accessed and processed live data from the GitHub API. - Visualized top Python repos with Plotly. #pythonML

Day 16/40 of Python-ML Working with CSV files in Python. Today I learned how to read data, handle dates, and plot cool visualizations with matplotlib 📊 From parsing headers to shading charts — it’s all about turning raw data into insights that tell a story. #pythonMl

Some sample project i worked on: Using exception to prevent crashes , handling FileNotFoundError, and analyzing text . On the second frame is a sample of storing data using json and refactoring into more easier and understandable program... #pythonML #LearningEveryday

Day 10/40 of my Python journey. After learning the basics, I’ve started my first real project — Alien Invasion 🛸 using Pygame. So far, I’ve built the game window, background, and ship display. Excited to make it come alive! ⚡ #PythonML

practiced using Matplotlib to create a scatter plot(showing start and ending point), histogram and line graph. visuals makes data clearer and more powerful. #pythonML #matplotlib #learningEveryday

@CDACINDIA is organizing online certificate courses with internship opportunities, starting from 18th March 2024 @GoI_MeitY #ethicalhacking #businessanalyticsAI #pythonML #NLP #cybersecurity #ICT

Python is widely considered as the preferred language for teaching and learning Ml (Machine Learning). As compared to C, C++ and Java the syntax is simpler and Python also consists of a lot of code libraries for ease of use. #Python #ML #PythonML #Shadow

Coming up in #Spark: #PythonML + #PandaUDF’s @martijnspitters #SparkSummit

Visualizing Repositories Using plotly... I added clickable links to access every project from the graph. That’s a wrap . With this, I’ve completed my Python journey — from the basics to working with APIs! Next stop : Machine Learning 🧑‍💻 #pythonML #learningEveryday

#machinelearning #ai #pythonml #knn #classifier

Masked #mammogram data-sets seem to be extremely popular with young data scientists and developers. The number of tests run with #PythonML on mammography data keeps growing everyday. #machinelearning #datascience #Python #breastcancer #oncology #medtech #acumen #healthdata

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4/7 ✅ Running the Model 🖥️ - We are getting output with 10 classes! #PythonML #AIForAll #PyTorchTutorial

4/ Hands-On Machine Learning with Python 🐍 🎓 Course: Machine Learning with Python: A Practical Introduction 📚 Platform: edX 📖 What you'll learn: Practical Python applications in machine learning, data preprocessing, and model building. #PythonML #HandsOnLearning

Alien Invasion Game is finally complete . You start by choosing your difficulty level, then dive into the battle. The game now features a scoreboard, high score tracking (saved even after restart), and shows the remaining ships (lives) as you play. #pythonML #pygame @clcoding

Under #ESoWC2021 STREAM 2 on #MachineLearning 👀Browse challenge 24➡️ CliMetLab: extending new #pythonml package to simplify access to #climate and #weather #data github.com/esowc/challeng… @ECMWF @CopernicusECMWF @pyladies Apply before 16 Apr👉 esowc.ecmwf.int

Python's essential role in ML due to its libraries, ease of use, and community support. 🐍 #PythonML #TechSkills

"Gratitude to everyone who joined Day 2 of our Python ML track. Your presence made it a success. Looking forward to more learning and collaboration ahead! 🚀 #PythonML #GdscNdejje

Got a great idea but not sure how to pitch it? Meet our #MyGalileoApp mentor and a @SiliconLondon Cofounder @FrancescoAgape! He's your go-to-person for any entrepreneurship #startup business modeling questions! 💡 #PythonML #UseGalileo @infosharepl @geoworldmedia @geoawesomeness

🚀Explore the synergy of Python and Machine Learning in #AWSDevOps with me at #Conf42Python! 🌐conf42.com/Python_2024_Gu… Let's unlock the potential of AI-driven DevOps together! 🌟 #PythonML #MachineLearning #CloudInfrastructure #DevOpsOptimization #TechConference #AIinDevOps

Boost your AI career with the Scikit-Learn Machine Learning Certification 🎯 Take the Free Certification Exam : edchart.com/certificate/sc… 🏅 Earn a Globally Recognized Digital Badge : credly.com/org/edchart-te… #ScikitLearn #MachineLearningCertification #PythonML #DataScience

Not sure when to use SelectKBest, RFE, or SelectFromModel? 🤔 Here’s a quick visual summary of feature selection methods in Scikit-Learn: 🧠 Simple → Powerful 📊 Efficient → Accurate 📎 buff.ly/Lr3X5q5 #DataScience #AIWorkflow #PythonML