6.55 GB | 10min 35s | mp4 | 2560X1440 | 16:9
FileName :1 – Introduction.mp4 | Size: (91.69 MB)
FileName :2 – What is scikit-learn – Understanding Its Role in the Python Data Science Ecosys.mp4 | Size: (106.37 MB)
FileName :3 – Machine Learning Landscape – Supervised vs Unsupervised vs Reinforcement.mp4 | Size: (82.01 MB)
FileName :4 – How to Get Help & Course Resources – Documentation, Cheat Sheets, and Q&A.mp4 | Size: (79.68 MB)
FileName :5 – Setting Up Your Python Environment – Installing Anaconda Miniconda.mp4 | Size: (95.92 MB)
FileName :6 – Installing scikit-learn – Using pip and conda.mp4 | Size: (81.97 MB)
FileName :7 – Installing Dependencies – NumPy, Pandas, Matplotlib, and SciPy.mp4 | Size: (104.38 MB)
FileName :8 – Your First Jupyter Notebook – Creating and Navigating Notebooks.mp4 | Size: (92.86 MB)
FileName :9 – Verifying Your Installation – Running Your First import sklearn.mp4 | Size: (103.28 MB)
FileName :10 – The Estimator API – The Fundamental fit() and transform() predict() Paradigm.mp4 | Size: (104.04 MB)
FileName :11 – Understanding Data Representation – The NumPy Array and Pandas DataFrame Require.mp4 | Size: (100.8 MB)
FileName :12 – Train-Test Split – The Importance of train test split.mp4 | Size: (115.87 MB)
FileName :13 – Data Preprocessing – Scaling, Normalization, and Standardization (StandardScaler.mp4 | Size: (100.3 MB)
FileName :14 – Feature Engineering – Creating New Features and Handling Categorical Data (OneHo.mp4 | Size: (112.2 MB)
FileName :15 – Pipelines – Chaining Preprocessing and Modeling Steps (Pipeline).mp4 | Size: (105.22 MB)
FileName :16 – Model Evaluation – Metrics Accuracy, Precision, Recall, F1-Score, and RMSE.mp4 | Size: (112.08 MB)
FileName :17 – Class Linear Models – Linear and Logistic Regression (LinearRegression, Logisti.mp4 | Size: (109.92 MB)
FileName :18 – Class Support Vector Machines – SVM for Classification and Regression (SVC, SVR.mp4 | Size: (130.56 MB)
FileName :19 – Class Tree-Based Models – Decision Trees (DecisionTreeClassifier).mp4 | Size: (126.08 MB)
FileName :20 – Class Ensemble Methods – Random Forests (RandomForestClassifier).mp4 | Size: (125.21 MB)
FileName :21 – Class Ensemble Methods 2 – Gradient Boosting (GradientBoostingClassifier).mp4 | Size: (115.34 MB)
FileName :22 – Class Ensemble Methods 3 – AdaBoost (AdaBoostClassifier).mp4 | Size: (117.04 MB)
FileName :23 – Class Clustering – K-Means Clustering (KMeans).mp4 | Size: (140.58 MB)
FileName :24 – Class Dimensionality Reduction – PCA (PCA) and t-SNE (TSNE).mp4 | Size: (100.65 MB)
FileName :25 – Class Nearest Neighbors – K-Nearest Neighbors (KNeighborsClassifier).mp4 | Size: (116.9 MB)
FileName :26 – Method fit() – The Training Process Explained in Depth.mp4 | Size: (113.82 MB)
FileName :27 – Method predict() & predict proba() – Making Predictions and Understanding Proba.mp4 | Size: (95.46 MB)
FileName :28 – Method transform() vs fit transform() – The Difference and When to Use Each.mp4 | Size: (114.41 MB)
FileName :29 – Method score() – Getting a Quick Evaluation Metric.mp4 | Size: (115.58 MB)
FileName :30 – Method set params() & get params() – Tuning and Viewing Model Parameters.mp4 | Size: (124.62 MB)
FileName :32 – Submodule model selection – GridSearchCV and RandomizedSearchCV for Hyperparame.mp4 | Size: (105.07 MB)
FileName :34 – Submodule metrics – Classification Report, Confusion Matrix, ROC Curves.mp4 | Size: (103.48 MB)
FileName :36 – Submodule preprocessing – Advanced Techniques (Polynomial Features, Binning).mp4 | Size: (110.83 MB)
FileName :37 – Submodule feature selection – Selecting the Best Features for Your Model.mp4 | Size: (101.27 MB)
FileName :38 – Pipelines & ColumnTransformers – Building Clean and Reproducible ML Workflows.mp4 | Size: (106.54 MB)
FileName :39 – Feature Scaling Best Practices – When to Scale and When Not To.mp4 | Size: (104.36 MB)
FileName :40 – Model Persistence – Saving and Loading Models Using joblib.mp4 | Size: (96.86 MB)
FileName :41 – Debugging & Performance – Using validation curve and learning curve.mp4 | Size: (113.65 MB)
FileName :42 – Common Errors – Type Errors (Handling Mismatched Data Types and Shapes).mp4 | Size: (116.31 MB)
FileName :43 – Common Errors – Value Errors (Dealing with NaN Values and Infinite Values).mp4 | Size: (103.51 MB)
FileName :44 – Common Errors – Memory Errors (Strategies for Working with Large Datasets).mp4 | Size: (141.49 MB)
FileName :45 – Common Errors – Convergence & Fit Warnings (Debugging Why a Model Isn’t Training.mp4 | Size: (114.77 MB)
FileName :46 – Project 1 Titanic Survival – Binary Classification with Logistic Regression.mp4 | Size: (107.7 MB)
FileName :47 – Project 2 Housing Price Prediction – Linear Regression with Feature Engineering.mp4 | Size: (120.76 MB)
FileName :48 – Project 3 Digits Recognition – Classification with SVM.mp4 | Size: (127.75 MB)
FileName :49 – Project 4 Iris Clustering – Unsupervised Learning with K-Means.mp4 | Size: (125.27 MB)
FileName :50 – Project 5 Movie Review Sentiment – Text Feature Extraction with TfidfVectorizer.mp4 | Size: (128.28 MB)
FileName :51 – Project 6 Decision Tree Visualization – Understanding a Model’s Decision Path.mp4 | Size: (161.79 MB)
FileName :52 – Project 7 Ensemble Voting – Combining Classifiers for Better Accuracy.mp4 | Size: (132.9 MB)
FileName :53 – Real Project 1 Customer Churn Prediction – End-to-End ML Pipeline.mp4 | Size: (145.1 MB)
FileName :54 – Real Project 2 Credit Card Fraud Detection – Handling Class Imbalance.mp4 | Size: (139.33 MB)
FileName :55 – Real Project 3 Stock Price Movement Predictor – Time-Series Feature Engineering.mp4 | Size: (123.1 MB)
FileName :56 – Top 10 Coding Questions – How to Code a Pipeline, Grid Search, etc.mp4 | Size: (83.86 MB)
FileName :58 – Explain Your Project – How to Present a Project in an Interview.mp4 | Size: (120.88 MB)
FileName :59 – Mock Interview – Simulated Questions and Answers.mp4 | Size: (84.72 MB)
FileName :60 – The Scikit-Learn Final Project – A Comprehensive Final Test with Solution Walkth.mp4 | Size: (127.17 MB)
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