Machine Learning

📌 How to Do Evals on a Bloated RAG Pipeline 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-12-21 | ⏱️ Read time: 71 min read Comparing metrics across datasets and models #DataScience #AI #Python

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Take Control of Selling in Amazon! 💫Too many tools, too little time? With dynamic pricing, real-time stock tracking, order monitoring and AI-powered BuyBox hunting, SellerFlash makes selling effortless in Amazon. 💫Say goodbye to manual chaos. With SellerFlash, you will manage listings, inventory, buyer messages and feedback campaigns all from one smart cloud platform designed for Amazon sellers. 👉🏽https://www.sellerflash.com/en/ Sponsored By WaybienAds

📌 Tools for Your LLM: a Deep Dive into MCP 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-12-21 | ⏱️ Read time: 8 min read MCP is a key enabler into turning your LLM into an agent by providing it… #DataScience #AI #Python

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Take Control of Selling in Amazon! 💫Too many tools, too little time? With dynamic pricing, real-time stock tracking, order monitoring and AI-powered BuyBox hunting, SellerFlash makes selling effortless in Amazon. 💫Say goodbye to manual chaos. With SellerFlash, you will manage listings, inventory, buyer messages and feedback campaigns all from one smart cloud platform designed for Amazon sellers. 👉🏽https://www.sellerflash.com/en/ Sponsored By WaybienAds

📌 EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas 🗂 Category: DATA SCIENCE 🕒 Date: 2025-12-20 | ⏱️ Read time: 9 min read Learn how to analyze product performance, extract time-series features, and uncover key seasonal trends in… #DataScience #AI #Python

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📌 Understanding the Generative AI User 🗂 Category: PRODUCT MANAGEMENT 🕒 Date: 2025-12-20 | ⏱️ Read time: 11 min read What do regular technology users think (and know) about AI? #DataScience #AI #Python

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🚀Stanford just completed a must-watch for anyone serious about AI: 🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold. If you’re building your AI career, stop scrolling. This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum. 📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • How Transformers actually work (tokenization, attention, embeddings) • Decoding strategies & MoEs • LLM finetuning (LoRA, RLHF, supervised) • Evaluation techniques (LLM-as-a-judge) • Optimization tricks (RoPE, quantization, approximations) • Reasoning & scaling • Agentic workflows (RAG, tool calling) 🧠 My workflow: I usually take the transcripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention. 🎥 Watch these now: - Lecture 1: https://lnkd.in/dDER-qyp - Lecture 2: https://lnkd.in/dk-tGUDm - Lecture 3: https://lnkd.in/drAPdjJY - Lecture 4: https://lnkd.in/e_RSgMz7 - Lecture 5: https://lnkd.in/eivMA9pe - Lecture 6: https://lnkd.in/eYwwwMXn - Lecture 7: https://lnkd.in/eKwkEDXV - Lecture 8: https://lnkd.in/eEWvyfyK - Lecture 9: https://lnkd.in/euiKRGaQ 🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them. If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss. Let’s level up. https://t.me/CodeProgrammer 😅

18) Which technique is used to handle categorical features that have a natural ordering (like "low", "medium", "high")? A. One-hot encoding B. Frequency encoding C. Ordinal encoding D. Target encoding Correct answer: C. 19) How can you visualize the decision boundaries of a classifier in a 2D feature space? A. Using PCA to reduce dimensions first B. Plotting the classifier's decision function over a mesh grid C. Using silhouette plots D. Creating a confusion matrix heatmap Correct answer: B. 20) Which OpenCV function is used to detect edges in an image using the Canny algorithm? A. cv2.edge_detect() B. cv2.find_edges() C. cv2.Canny() D. cv2.detect_contours() Correct answer: C. 21) What is the purpose of TF-IDF in text processing? A. To count the frequency of words in a document B. To convert text to numerical vectors C. To weight words by their importance in a document relative to a corpus D. To perform named entity recognition Correct answer: C. 22) Which technique is appropriate for reducing the dimensions of a sparse feature matrix? A. PCA B. LDA C. TSVD (Truncated Singular Value Decomposition) D. Kernel PCA Correct answer: C. 23) How can you handle imbalanced classes in a classification problem? A. Use class_weight parameter in classifiers B. Resample the dataset (oversampling minority class or undersampling majority class) C. Use appropriate evaluation metrics like F1-score or AUC D. All of the above Correct answer: D. 24) Which library is commonly used to save and load scikit-learn models? A. pickle B. json C. joblib D. model_saver Correct answer: C. 25) What is transfer learning in the context of deep learning? A. Transferring model weights from one architecture to another B. Using a pre-trained model on a new task with minimal additional training C. Transferring data between different datasets D. Converting models between different frameworks (e.g., TensorFlow to PyTorch) Correct answer: B.

# Machine Learning with Python Exam 1) Which method is used to create a one-dimensional array in NumPy? A. np.vector() B. np.array() C. np.matrix() D. np.tensor() Correct answer: B. 2) What is the primary purpose of a sparse matrix? A. To speed up matrix multiplication B. To store only nonzero elements and save computational resources C. To create matrices with random values D. To convert dense matrices to tensor format Correct answer: B. 3) Which pandas method would you use to select specific rows from a DataFrame based on a conditional statement? A. filter() B. where() C. query() D. All of the above Correct answer: D. 4) How can you replace missing values in a pandas DataFrame with the mean of the column? A. df.fillna(df.mean()) B. df.replace_nan(df.mean()) C. df.impute('mean') D. df.interpolate('mean') Correct answer: A. 5) Which scikit-learn method is used to standardize features to have zero mean and unit variance? A. MinMaxScaler B. Normalizer C. StandardScaler D. RobustScaler Correct answer: C. 6) What does the term "one-hot encoding" refer to in machine learning? A. Encoding text data as numerical values B. Creating binary features for each class in a categorical feature C. Transforming features to have values between 0 and 1 D. Encoding time series data as features Correct answer: B. 7) Which method is commonly used for dimensionality reduction that projects data onto principal components? A. Linear Discriminant Analysis B. t-SNE C. Principal Component Analysis D. Non-negative Matrix Factorization Correct answer: C. 8) In k-fold cross-validation, what happens during each iteration? A. The model is trained on k-1 folds and evaluated on the remaining fold B. The model is trained on 1 fold and evaluated on k-1 folds C. The model is trained on random subsets of size k D. The model parameters are adjusted k times Correct answer: A. 9) Which activation function is commonly used in the output layer for binary classification problems in neural networks? A. ReLU B. Softmax C. Sigmoid D. Tanh Correct answer: C. 10) What is the time complexity of finding the k nearest neighbors in a dataset of size n using the brute-force approach? A. O(n) B. O(n log n) C. O(n^2) D. O(kn) Correct answer: D. 11) In decision trees, what does Gini impurity measure? A. The variance of the target variable B. The probability of misclassifying a randomly chosen element C. The information gain from splitting a node D. The depth of the tree Correct answer: B. 12) What is the key difference between Random Forest and standard decision trees? A. Random Forest uses only a subset of features at each split B. Random Forest can handle categorical features directly C. Random Forest uses Gini impurity while decision trees use entropy D. Random Forest is always more accurate than a single decision tree Correct answer: A. 13) Which parameter in SVM controls the trade-off between maximizing the margin and minimizing classification error? A. gamma B. kernel C. C D. degree Correct answer: C. 14) What does the "bagging" technique in ensemble learning refer to? A. Training models on different subsets of features B. Training models on bootstrapped samples of the data C. Combining models with different hyperparameters D. Training models sequentially where each corrects errors of the previous Correct answer: B. 15) Which evaluation metric is most appropriate when dealing with highly imbalanced classification problems? A. Accuracy B. F1-score C. MSE D. R-squared Correct answer: B. 16) Which class in PyTorch is used to define a neural network architecture? A. torch.nn.Network B. torch.nn.Model C. torch.nn.Module D. torch.nn.Sequential Correct answer: C. 17) What is the purpose of the dropout technique in neural networks? A. To reduce the number of output classes B. To prevent overfitting by randomly deactivating neurons during training C. To reduce the input dimensionality D. To speed up the training process Correct answer: B.

📌 Six Lessons Learned Building RAG Systems in Production 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-12-19 | ⏱️ Read time: 10 min read Best practices for data quality, retrieval design, and evaluation in production RAG systems #DataScience #AI #Python

📌 How I Optimized My Leaf Raking Strategy Using Linear Programming 🗂 Category: DATA SCIENCE 🕒 Date: 2025-12-19 | ⏱️ Read time: 13 min read From a weekend chore to a fun application of valuable operations research principles #DataScience #AI #Python

📌 Agentic AI Swarm Optimization using Artificial Bee Colonization (ABC) 🗂 Category: AGENTIC AI 🕒 Date: 2025-12-19 | ⏱️ Read time: 27 min read Using Agentic AI prompts with the Artificial Bee Colony algorithm to enhance unsupervised clustering and… #DataScience #AI #Python

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