Daywise | Blog |
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Day1 ( What is ML ) | Explanation Link |
Day2 ( AI Vs ML Vs DL ) | Explanation Link |
Day3 ( Types of Machine Learning ) | Explanation Link |
Day4 ( Batch Learning in ML ) | Explanation Link |
Day5 ( Online Learning in ML) | Explanation Link |
Day6 (Instance-Based Vs Model-Based Learning ) | Explanation Link |
Day7 (Challenges in ML ) | Explanation Link |
Day8 (Applications in ML) | Explanation Link |
Day9 (Machine Learning Development Life Cycle) | Explanation Link |
Day10 (Data Engineer V Data Analyst V Data Scientist V ML Engineer) | Explanation Link |
Day11 (Tensor in Machine Learning ) | Explanation Link |
Day12 (Tools Knowledge-Jupyter Notebook,Google Colab,Kaggle | Explanation Link |
Day13 (End to End Toy Project ) | Explanation Link |
Day14 (Framing Machine Learning Problem) | Explanation Link |
Day15 (Working with CSV ) | Explanation Link |
Day16 (Working with JSON & SQL) | Explanation Link |
Day17 (Fetching Data From API) | Explanation Link |
Day18 (Fetching Data Using WebScraping) | Explanation Link |
Day19 (Understanding Your Data in ML ) | Explanation Link |
Day20 (EDA using Univariate Analysis ) | Explanation Link |
Day21 (EDA using Bivariate and Multivariate Analysis) | Explanation Link |
Day22 (Pandas Profiling ) | Explanation Link |
Day23 (Feature Engineering) | Explanation Link |
Day24 (Standardization in ML) | Explanation Link |
Day25 (Normalization in ML ) | Explanation Link |
Day26 (Encoding Categorical Data-Label Encoding) | Explanation Link |
Day27 (One Hot Encoding ) | Explanation Link |
Day28 (Column Transformer in ML ) | Explanation Link |
Day29 (Machine Learning Pipelines ) | Explanation Link |
Day30 (Function Transformer in ML ) | Explanation Link |
Day31 (Power Transformer in ML ) | Explanation Link |
Day32 (Encode Numerical Features ( Binning & Binarization )) | Explanation Link |
Day33 (Handling Mixed Variable in Feature Engineering 👨💻 ) | Explanation Link |
Day34 (Handling Date & Time Variable in Feature Engineering) | Explanation Link |
Day35 (Handling Missing Data -( Complete Case Analysis ) ) | Explanation Link |
Day36 (Handling missing data - Numerical Data - Simple Imputer) | Explanation Link |
Day37 ( Handling Missing Categorical Data -Most Frequent Imputation- Missing Category Imputation | Explanation Link |
Day38 ( Missing Indicator - Random Sample Imputation ) | Explanation Link |
Day39 ( KNN Imputer -- Multivariate Imputation ) | Explanation Link |
Day40 ( Multivariate Imputation by Chained Equations [ MICE ] ) | Explanation Link |
Day41 ( Outliers in Machine Learning ) | Explanation Link |
Day42 ( Outlier Detection & Removal using Z-score Method ) | Explanation Link |
Day43 ( Outlier Detection and Removal using the IQR Method ) | Explanation Link |
Day44 ( Outlier Detection using Percentile Method ) | Explanation Link |
Day45 ( Feature Construction & Feature Splitting ) | Explanation Link |
Day46 ( Curse of Dimensionality ) | Explanation Link |
Day47 ( Principle Component Analysis (PCA) Part 1 ) | Explanation Link |
Day48 ( Principle Component Analysis (PCA) Part 2 ) | Explanation Link |
Day49 ( Principle Component Analysis (PCA) Part 3 ) | Explanation Link |
Day50 ( Simple Linear Regression Code + Intuition ) | Explanation Link |
Day51 ( Simple Linear Regression - Mathematical Formulation ) | Explanation Link |
Day52 ( Regression Metrics MSE MAE & RMSE R2 Score & Adjusted R2 Score ) | Explanation Link |
Day53 ( Multiple Linear Regression Geometric intuition & code ) | Explanation Link |
Day54 ( Multiple Linear Regression Mathematical Formulation From Scratch ) | Explanation Link |
Day55 ( Multiple Linear Regression Code From Scratch ) | Explanation Link |
Day56 ( Gradient Descent ) | Explanation Link |
Day57 ( Batch Gradient Descent ) | Explanation Link |
Day58 ( Stochastic Gradient Descent ) | Explanation Link |
Day59 ( Mini-Batch Gradient Descent ) | Explanation Link |
Day60 ( Polynomial Regression ) | Explanation Link |
Day61 ( Bias Variance Trade-off ) | Explanation Link |
Day62 ( Ridge Regression Part 1 ) | Explanation Link |
Day63 ( Ridge Regression Part 2 ) | Explanation Link |
Day64 ( Ridge Regression using Gradient Descent ) | Explanation Link |
Day65 ( Five key points about Ridge Regression ) | Explanation link |
Day66 ( Lasso Regression ) | Explanation link |
Day67 ( Why Lasso Regression creates sparsity ) | Explanation Link |
Day68 ( ElasticNet Regression ) | Explanation Link |
Day69 ( Logistic Regression ) | Explanation Link |
Day70 ( Logistic Regression Perceptron Trick ) | Explanation Link |
Day71 ( Logistic Regression Sigmoid Function ) | Explanation Link |
Day72 ( Logistic Regression-Maximum Likelihood ) | Explanation Link |
Day73 ( Derivative of Sigmoid Function ) | Explanation Link |
Day74 ( Logistic Regression Gradient Descent & Code From Scratch ) | Explanation Link |
Day75 ( Accuracy and Confusion Matrix ) | Explanation Link |
Day76 ( Precision Recall and F1 Score ) | Explanation Link |
Day77 ( Softmax Regression ) | Explanation Link |
Day78 ( Polynomial Features in Logistic Regression ) | Explanation Link |
Day79 ( Logistic Regression Hyperparameters ) | Explanation Link |
Day80 ( Decision Trees Geometric Intuition ) | Explanation Link |
Day81 ( Hyperparameter Tuning in Decision Trees ) | Explanation Link |
Day82 ( Regression Tree - Decision Tree visualization with Dtreeviz ) | Explanation Link |
Day83 ( Ensemble Learning ) | Explanation Link |
Day84 ( How to Develop Voting Ensembles With Python ) | Explanation Link |
Day85 ( Bagging Ensemble Learning ) | Explanation Link |
Day86 ( Random Forest Algorithm in ML ) | Explanation Link |
Day87 ( AdaBoost Alogorithm in ML ) | Explanation Link |
Day88 ( Kmeans Clustering in ML ) | Explanation Link |
Day89 ( Gradient Boosting Algorithm in ML ) | Explanation Link |
Day90 ( Stacking & Blending in ML ) | Explanation Link |
Day91 ( Hierarchical Clustering ) | Explanation Link |
Day92 ( K-Nearest Neighbors(KNN) Algorithm in ML ) | Explanation Link |
Day93 ( Support Vector Machines in ML ) | Explanation Link |
Day94 ( Naive Bayes algorithm in ML ) | Explanation Link |
Day95 ( XGBoost Algorithm in ML ) | Explanation Link |
Day96 ( DBSCAN Clustering in ML ) | Explanation Link |
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The concept of "100 days of ML notes" refers to a self-directed learning challenge aimed at building a solid foundation in machine learning (ML) over a period of 100 days. Remember, consistency is key!
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The concept of "100 days of ML notes" refers to a self-directed learning challenge aimed at building a solid foundation in machine learning (ML) over a period of 100 days. Remember, consistency is key!
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