#pythonml Suchergebnisse

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