All Episodes
Displaying 101 - 120 of 121 in total
Episode 100 — Ensemble Thinking: When Combining Models Helps and When It Confuses
This episode teaches ensemble thinking as a decision framework: combining models can improve accuracy and robustness, but it can also create operational and interpreta...
Episode 101 — Neural Network Basics: Neurons, Layers, and What “Representation” Means
This episode introduces neural networks as function approximators that learn internal representations of data, because DataX scenarios may test whether you understand ...
Episode 102 — Activation Functions: ReLU, Sigmoid, Tanh, Softmax and Output Behavior
This episode teaches activation functions as the mechanism that gives neural networks nonlinearity and shapes output behavior, because DataX scenarios may ask you to r...
Episode 103 — Training Mechanics: Backpropagation as Error Correction
This episode explains backpropagation as the mechanism neural networks use to adjust parameters, focusing on the intuitive idea of error correction rather than math de...
Episode 104 — Optimizers: SGD, Momentum, Adam, RMSprop and Practical Differences
This episode explains optimizers as the rules that turn gradients into parameter updates, because DataX scenarios may ask you to recognize why different optimizers beh...
Episode 105 — Regularizing Deep Models: Dropout, Batch Norm, Early Stopping, Schedulers
This episode teaches deep model regularization as a toolkit for controlling overfitting and stabilizing training, because DataX scenarios may test whether you can choo...
Episode 106 — Deep Model Families: CNN, RNN, LSTM, Autoencoders, GANs, Transformers
This episode introduces major deep model families at the conceptual level, focusing on what each family is designed to capture and how to recognize their appropriate u...
Episode 107 — Transfer Learning and Embeddings: Reuse, Fine-Tune, and Cold Start
This episode explains transfer learning and embeddings as strategies for reusing learned representations, because DataX scenarios may test whether you can recognize wh...
Episode 108 — AutoML and Few-Shot Concepts: Where Automation Fits and Where It Fails
This episode teaches AutoML and few-shot concepts as automation tools with clear boundaries, because DataX scenarios may ask you to choose when automation accelerates ...
Episode 109 — Clustering: k-Means, Hierarchical, DBSCAN and Choosing the Right One
This episode teaches clustering as an unsupervised grouping task and trains you to choose among k-means, hierarchical clustering, and DBSCAN based on data geometry, sc...
Episode 110 — Cluster Validation: Elbow, Silhouette, and “Does This Grouping Matter”
This episode teaches cluster validation as a reality check, because DataX scenarios may ask you how to pick k, how to evaluate whether clusters are meaningful, and how...
Episode 111 — Dimensionality Reduction: PCA Intuition and What Components Represent
This episode teaches PCA as a linear dimensionality reduction technique, focusing on intuition and component meaning, because DataX scenarios often test whether you ca...
Episode 112 — Nonlinear Reduction: t-SNE and UMAP for Structure, Not “Truth”
This episode covers t-SNE and UMAP as nonlinear dimensionality reduction methods, emphasizing how to interpret their outputs correctly, because DataX scenarios may tes...
Episode 113 — SVD and Nearest Neighbors: Where They Appear in DataX Scenarios
This episode teaches SVD and nearest neighbors as foundational tools that appear across recommendation, dimensionality reduction, similarity search, and clustering, be...
Episode 114 — Recommenders: Similarity, Collaborative Filtering, and ALS in Plain Terms
This episode explains recommender systems as methods for predicting preference or relevance, focusing on similarity-based approaches, collaborative filtering intuition...
Episode 115 — Domain 3 Mixed Review: Model Selection and ML Scenario Drills
This episode is a mixed review designed to convert Domain 3 model-selection knowledge into fast scenario decisions, because DataX questions often present multiple plau...
Episode 116 — Business Alignment: Requirements, KPIs, and “Need vs Want” Tradeoffs
This episode teaches business alignment as the first constraint layer in DataX scenarios, because many questions are designed to test whether you can translate stakeho...
Episode 117 — Compliance and Privacy: PII, Proprietary Data, and Risk-Aware Handling
This episode covers compliance and privacy as design constraints that shape the entire data lifecycle, because DataX scenarios frequently test whether you can identify...
Episode 118 — Data Acquisition: Surveys, Sensors, Transactions, Experiments, and DGP Thinking
This episode teaches data acquisition as a source-driven decision, because DataX scenarios often require you to choose the right data collection approach and to reason...
Episode 119 — External and Commercial Data: Availability, Licensing, and Restrictions
This episode covers external and commercial data as enrichment options with governance constraints, because DataX scenarios may ask you to evaluate whether third-party...