Data Science and Machine Learning

Data Science and Machine Learning Tutorial Vidoes.

Curated by: sherlockdataintelligence (39 videos)

Currently Playing: Ep 4 — Machine Learning Median - The Book in the Middle Median

#machinelearning #datascience #ml #ai #maths #algortihm #artificialintelligence Core Idea: The middle value resists distortion by extremes. Concept: Median = the middle value when sorted. Why it matters: Robust to outliers where the mean is not. Core mechanic: You will demonstrate df['col'].median(). Example: Income/salary data — the median tells a truer "typical" story than the mean. Common mistake: Reporting the mean when the data is skewed. The Rule: "When the average lies, ask the witness in the middle." Next Clue: Two shelves, same average, wildly different spread.

Tracks in this Playlist

Ep 41 — Machine Learning NN Learning Rate - How Big a Step to Take (Learning Rate)
Ep 40 — Machine Learning Neural Networks - The Room He Can't Fully Explain (Neural Networks)
Ep 39 — Machine Learning Time Series Basics - The Room Over Time (Time Series Basics)
Ep 38 — Machine Learning A/B Testing - Moving One Shelf on Purpose (A/B Testing)
Ep 37 — Machine Learning Multicollinearity -Two Clues Telling the Same Story (Multicollinearity)
Ep 36 — Machine Learning One Hot Encoding Turning Words Into Something Countable (One-Hot Encoding)
Ep 35 — Machine Learning Class Imbalance - When Almost Nothing Is Rare (Class Imbalance)
Ep 34 — Machine Learning Hyperparameter Tuning - Adjusting the Lens (Hyperparameter Tuning)
Ep 33 —Machine Learning Gradient XGBoost - Correcting the Last Mistake (Gradient Boosting / XGBoost)
Ep 32 —Machine Learning - Ensemble - Many Observers, One Conclusion (Ensemble Methods)
Ep 31 —Machine Learning - K Means Clustering - Grouping Without Being Told (K-Means Clustering)
Ep 30 — Machine Learning PCA - Fewer Shelves, Same Story (PCA / Dimensionality Reduction)
Ep 29 — Machine Learning SVM - The Widest Possible Gap (Support Vector Machines)
Ep 28 — Machine Learning Navie Bayes Theorem - The Assumption That Still Works (Naive Bayes)
Ep 26 — Machine Learning Logistic Regression - Yes or No, Nothing Between (Logistic Regression)
Ep 25 — Machine Learning ROC-AUC - The Curve of Every Threshold (ROC-AUC)
Ep 24 — Machine Learning F1 Score - Balancing the Two Numbers (F1 Score)
Ep 23 — Machine Learning Precision and Recall - How Many, and How Sure (Precision & Recall)
Ep 22 — Machine Learning Confusion M - Right, Wrong, and the Two Ways to Be Wrong (Confusion Matrix)
Ep 21 — Machine Learning Correlation -Two Shelves That Move Together (Correlation vs. Causation)
Ep 20 — Machine Learning Outliers The Book That Doesn't Belong (Outliers)
Ep 19 — Machine Learning Missing Data The Gap on the Shelf (Missing Data)
Ep 18 — Machine Learning Random Forests A Room Full of Observers (Random Forest)
Ep 17 —Machine Learning Decision Trees Following the Branch of Clues (Decision Trees)
Ep 16 —Machine Learning Gradient Descent -The Slow Walk Toward the Misplaced Book (Gradient Descent)
Ep 15 — Machine Learning Cross Validation --- Checking Every Shelf, Not Just One Cross-Validation
Ep 14 —Machine Learning Regularization - Simplifying the Story (Regularization)
Ep 13 — Machine Learning Model Underfitting - The Glance That Wasn't Enough (Underfitting)
Ep 12 — Machine Learning Model Overfitting - The Book That Memorized the Room Overfitting
Ep 11 — Machine Learning Bias vs Variance - Too Rigid, Too Restless Bias vs. Variance
Ep 10 — Machine Learning Model Train/Test Split The Shelf He Never Looks At Train/Test Split
Ep 9 — Machine Learning Feature Mapping The Relationship Between Two Books Feature Mapping
Ep 7 — Two Rulers, One Shelf Machine Learning Feature Scaling
Ep 6 — Machine Learning Skew - The Leaning Shelf Skewness
Ep 5 — Machine Learning Standard Devitation - How Evenly the Books Sit Standard Deviation
Ep 4 — Machine Learning Median - The Book in the Middle Median
Ep 3 — Machine Learning Mean - The Average Height of the Shelf Mean
Ep 2 — Machine Learning Data Processing - The Torn Page Data Preprocessing
Ep 1 — Machine Learning EDA -The Room Before Any Book Is Touched Exploratory Data Analysis