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