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100 days ML Notes v2
100 Days of ML Notes - Part 1
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Lectures
What is Machine Learning? | 100 Days of Machine Learning
01_What is Machine Learning_100DaysofML_CampusXocr
AI Vs ML Vs DL for Beginners in Hindi
02_AI Vs ML Vs DL for Beginners in Hindi_100DaysofML_CampusXocr
Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth
03_Types of Machine Learning for Beginners_100DaysofML_CampusXocr
Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types
04_Batch Machine Learning Offline Vs Online Learning_100DaysofML_CampusXocr
Online Machine Learning | Online Learning | Online Vs Offline Machine Learning
05_Online Machine Learning Online Learning Online Vs Offline Machine Learning_100DaysofML_CampusXocr
Instance-Based Vs Model-Based Learning | Types of Machine Learning
06_Instance-Based Vs Model-Based Learning_Types of Machine Learning_100DaysofML_CampusXocr
Challenges in Machine Learning | Problems in Machine Learning
07_Challenges in Machine Learning Problems in Machine Learning_100DaysofML_CampusXocr
Application of Machine Learning | Real Life Machine Learning Applications
08_Application of Machine Learning Real Life Machine Learning Applications_100DaysofML_CampusXocr
Machine Learning Development Life Cycle | MLDLC in Data Science
09_Machine Learning Development Life Cycle MLDLC in Data Science_100DaysofML_CampusXocr
Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles
10_Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer Data Science Job Roles_100DaysofML_CampusXocr
What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning
11_What are Tensors Tensor In-depth Explanation Tensor in Machine Learning_100DaysofML_CampusXocr
Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML
12_Installing Anaconda For Data Science Jupyter Notebook for Machine Learning Google Colab for ML_100DaysofML_CampusXocr
End to End Toy Project | Day 13 | 100 Days of Machine Learning
13_End to End Toy Project Day 13_100 Days of Machine Learning_100DaysofML_CampusXocr
How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively
14_How to Frame a Machine Learning Problem How to plan a Data Science Project Effectively_100DaysofML_CampusXocr
Working with CSV files | Day 15 | 100 Days of Machine Learning
15_Working with CSV files Day 15 100 Days of Machine Learning_100DaysofML_CampusXocr
Working with JSON/SQL | Day 16 | 100 Days of Machine Learning
16_Working with JSONSQL Day 16 100 Days of Machine Learning_100DaysofML_CampusXocr
Fetching Data From an API | Day 17 | 100 Days of Machine Learning
17_Fetching Data From an API Day 17 100 Days of Machine Learning_100DaysofML_CampusXocr
Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning
18_Fetching data using Web Scraping Day 18 100 Days of Machine Learning_100DaysofML_CampusXocr
Understanding Your Data | Day 19 | 100 Days of Machine Learning
19_Understanding Your Data Day 19 100 Days of Machine Learning_100DaysofML_CampusXocr
EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning
20_EDA using Univariate Analysis Day 20 100 Days of Machine Learning_100DaysofML_CampusXocr
EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning
21_EDA using Bivariate and Multivariate Analysis_Day 21_100 Days of Machine Learning_100DaysofML_CampusXocr
Pandas Profiling | Day 22 | 100 Days of Machine Learning
22_Pandas Profiling_Day 22_100 Days of Machine Learning_100DaysofML_CampusXocr
What is Feature Engineering | Day 23 | 100 Days of Machine Learning
23_What is Feature Engineering_100DaysofML_CampusXocr
Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning
24_Feature Scaling - Standardization_Day 24_100 Days of Machine Learning_100DaysofML_CampusXocr
Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling
25_Feature Scaling_ Normalization_MinMaxScaling_MaxAbsScaling_RobustScaling_100DaysofML_CampusXocr
Encoding Categorical Data | Ordinal Encoding | Label Encoding
26_Encoding Categorical Data Ordinal Encoding Label Encoding_100DaysofML_CampusXocr
One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning
27_One Hot Encoding_Handling Categorical Data _Day 27_100 Days of Machine Learning_100DaysofML_CampusXocr
Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn
28_Column Transformer in Machine Learning How to use ColumnTransformer in Sklearn_100DaysofML_CampusXocr
Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning
29_Machine Learning Pipelines A-Z _Day 29_100 Days of Machine Learning_100DaysofML_CampusXocr
Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform
30_Function Transformer Log Transform Reciprocal Transform Square Root Transform_100DaysofML_CampusXocr
Power Transformer | Box - Cox Transform | Yeo - Johnson Transform
31_Power Transformer Box - Cox Transform Yeo - Johnson Transform_100DaysofML_CampusXocr
Binning and Binarization | Discretization | Quantile Binning | KMeans Binning
32_Binning and Binarization Discretization Quantile Binning KMeans Binning_100DaysofML_CampusXocr
Handling Mixed Variables | Feature Engineering
33_Handling Mixed Variables Feature Engineering_100DaysofML_CampusXocr
Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning
34_Handling Date and Time Variables Day 34 100 Days of Machine Learning_100DaysofML_CampusXocr
Handling Missing Data | Part 1 | Complete Case Analysis
35_Handling Missing Data Part 1 Complete Case Analysis_100DaysofML_CampusXocr
Handling missing data | Numerical Data | Simple Imputer
36_Handling missing data Numerical Data Simple Imputer_100DaysofML_CampusXocr
Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp
37_Handling Missing Categorical Data Simple Imputer Most Frequent Imputation Missing Category Imp_100DaysofML_CampusXocr
Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4
38_Missing Indicator Random Sample ImputationHandling Missing Data Part 4_100DaysofML_CampusXocr
KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5
39_KNN Imputer Multivariate Imputation Handling Missing Data Part 5_100DaysofML_CampusXocr
Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer
40_Multivariate Imputation by Chained Equations for Missing Value MICE Algorithm Iterative Imputer_100DaysofML_CampusXocr
What are Outliers | Outliers in Machine Learning
41_What are Outliers Outliers in Machine Learning_100DaysofML_CampusXocr
Outlier Detection and Removal using Z-score Method | Handling Outliers Part 2
42_Outlier Detection and Removal using Z-score Method Handling Outliers Part 2_100DaysofML_CampusXocr
Outlier Detection and Removal using the IQR Method | Handing Outliers Part 3
43_Outlier Detection and Removal using the IQR Method Handing Outliers Part _100DaysofML_CampusXocr
Outlier Detection using the Percentile Method | Winsorization Technique
44_ Outlier Detection using the Percentile Method Winsorization Technique_100DaysofML_CampusXocr
Feature Construction | Feature Splitting
45_Feature ConstructionFeature Splitting_100DaysofML_CampusXocr
Curse of Dimensionality
46_Curse of Dimensionality_100DaysofML_CampusXocr
Principle Component Analysis (PCA) | Part 1 | Geometric Intuition
47_Principle Component Analysis (PCA) Part 1 Geometric Intuition_100DaysofML_CampusXocr
Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution
48_Principle Component Analysis (PCA)Part 2Problem Formulation and Step by Step Solution_100DaysofML_CampusXocr
Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization
49_Principle Component Analysis(PCA)Part 3Code Example and Visualization_100DaysofML_CampusXocr
Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi
50_Simple Linear Regression Code + Intuition Simplest Explanation in Hindi_100DaysofML_CampusXocr
Simple Linear Regression | Mathematical Formulation | Coding from Scratch
51_Simple Linear Regression Mathematical Formulation Coding from Scratch_100DaysofML_CampusXocr
Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score
52_Regression Metrics MSE, MAE & RMSE R2 Score & Adjusted R2 Score_100DaysofML_CampusXocr
52_Regression Metrics MSE, MAE RMSE R2 Score Adjusted R2 Score
Multiple Linear Regression | Geometric Intuition & Code
53_ Multiple Linear Regression Geometric Intuition & Code_100DaysofML_CampusXocr
Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch
54_ Multiple Linear Regression Part 2 Mathematical Formulation From Scratch_100DaysofML_CampusXocr
Multiple Linear Regression | Part 3 | Code From Scratch
55_ Multiple Linear Regression Part 3 Code From Scratch_100DaysofML_CampusXocr
Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation
56_ Gradient Descent From Scratch End to End Gradient Descent Gradient Descent Animation_100DaysofML_CampusXocr
Batch Gradient Descent with Code Demo | Simple Explanation in Hindi
57_Batch Gradient Descent with Code Demo Simple Explanation in Hindi_100DaysofML_CampusXocr
Stochastic Gradient Descent
58_Stochastic Gradient Descent_100DaysofML_CampusXocr
Mini-Batch Gradient Descent
59_Mini-Batch Gradient Descent_100DaysofML_CampusXocr
Polynomial Regression | Machine Learning
60_ Polynomial Regression Machine Learning_100DaysofML_CampusXocr
Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning
61_Bias Variance Trade-off Overfitting and Underfitting in Machine Learning_100DaysofML_CampusXocr
Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models
62_Ridge Regression Part 1 Geometric Intuition and Code Regularized Linear Models_100DaysofML_CampusXocr
Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models
63_Ridge Regression Part 2 Mathematical Formulation Code from scratch Regularized Linear Models_100DaysofML_CampusXocr
Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models
64_Ridge Regression Part 3 Gradient Descent Regularized Linear Models
5 Key Points - Ridge Regression | Part 4 | Regularized Linear Models
65_5 Key Points - Ridge Regression Part 4 Regularized Linear Models_100DaysofML_CampusXocr
Lasso Regression | Intuition and Code Sample | Regularized Linear Models
66_Lasso Regression Intuition and Code Sample Regularized Linear Models_100DaysofML_CampusXocr
Why Lasso Regression creates sparsity?
67_Why Lasso Regression creates sparsity_100DaysofML_CampusXocr
ElasticNet Regression | Intuition and Code Example | Regularized Linear Models
68_ ElasticNet RegressionIntuition and Code Example Regularized Linear Models_100DaysofML_CampusXocr
Logistic Regression Part 1 | Perceptron Trick
69_Logistic Regression Part 1 Perceptron Trick_100DaysofML_CampusXocr
Logistic Regression Part 2 | Perceptron Trick Code
70_ Logistic Regression Part 2 Perceptron Trick Code_100DaysofML_CampusXocr
Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML
71_Logistic Regression Part 3 Sigmoid Function 100 Days of ML_100DaysofML_CampusXocr
Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy
72_Logistic Regression Part 4 Loss Function Maximum Likelihood Binary Cross Entropy_100DaysofML_CampusXocr
Derivative of Sigmoid Function
73_Derivative of Sigmoid Function_100DaysofML_CampusXocr
Logistic Regression Part 5 | Gradient Descent & Code From Scratch
74_Logistic Regression Part 5 Gradient Descent & Code From Scratch_100DaysofML_CampusXocr
Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 1
75_Accuracy and Confusion Matrix Type 1 and Type 2 Errors Classification Metrics Part 1_100DaysofML_CampusXocr
Precision, Recall and F1 Score | Classification Metrics Part 2
76_Precision, Recall and F1 Score Classification Metrics Part 2_100DaysofML_CampusXocr
Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 6
77_Softmax Regression Multinomial Logistic Regression Logistic Regression Part 6_100DaysofML_CampusXocr
Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 7
78_Polynomial Features in Logistic Regression Non Linear Logistic Regression Logistic Regression 7_100DaysofML_CampusXocr
Logistic Regression Hyperparameters || Logistic Regression Part 8
79_Logistic Regression Hyperparameters Logistic Regression Part 8_100DaysofML_CampusXocr
Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain
80_Decision Trees Geometric Intuition Entropy Gini impurity Information Gain_100DaysofML_CampusXocr
Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees
81_Decision Trees - Hyperparameters Overfitting and Underfitting in Decision Trees_100DaysofML_CampusXocr
Regression Trees | Decision Trees Part 3
82_Regression Trees Decision Trees Part 3_100DaysofML_CampusXocr
Awesome Decision Tree Visualization using dtreeviz library
83_Awesome Decision Tree Visualization using dtreeviz library_100DaysofML_CampusXocr
Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning
84_Introduction to Ensemble Learning Ensemble Techniques in Machine Learning_100DaysofML_CampusXocr
Voting Ensemble | Introduction and Core Idea | Part 1
85_Voting Ensemble Introduction and Core Idea Part 1_100DaysofML_CampusXocr
Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 2
86_Voting Ensemble Classification Voting Classifier Hard Voting Vs Soft Voting Part 2_100DaysofML_CampusXocr
Voting Ensemble | Regression | Part 3
87_Voting Ensemble Regression Part 3_100DaysofML_CampusXocr
Bagging | Introduction | Part 1
88_Bagging Introduction Part 1_100DaysofML_CampusXocr
Bagging Ensemble | Part 2 | Bagging Classifiers
89_Bagging Ensemble Part 2 Bagging Classifiers_100DaysofML_CampusXocr
Bagging Ensemble | Part 3 | Bagging Regressor
90_Bagging Ensemble Part 3 Bagging Regressor_100DaysofML_CampusXocr
Introduction to Random Forest | Intuition behind the Algorithm
91_Introduction to Random Forest Intuition behind the Algorithm_100DaysofML_CampusXocr
How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest
92_How Random Forest Performs So Wel Bias Variance Trade-Off in Random Forest_100DaysofML_CampusXocr
Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important
93_Bagging Vs Random Forest What is the difference between Bagging and Random Forest Very Important_100DaysofML_CampusXocr
Random Forest Hyper-parameters
94_Random Forest Hyper-parameters_100DaysofML_CampusXocr
Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example
95_Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV Code Example_100DaysofML_CampusXocr
OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning
96_OOB Score Out of Bag Evaluation in Random Forest Machine Learning_100DaysofML_CampusXocr
Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated
97_ Feature Importance using Random Forest and Decision Trees How is Feature Importance calculated_100DaysofML_CampusXocr
How Adaboost Classifier Works? | Geometric Intuition
98_ How Adaboost Classifier WorksGeometric Intuition_100DaysofML_CampusXocr
AdaBoost - A Step by Step Explanation
99_AdaBoost - A Step by Step Explanation_100DaysofML_CampusXocr
AdaBoost Algorithm | Code from Scratch
100_AdaBoost Algorithm Code from Scratch_100DaysofML_CampusXocr
AdaBoost Hyperparameters | GridSearchCV in Adaboost
101_AdaBoost Hyperparameters GridSearchCV in Adaboost_100DaysofML_CampusX
Bagging Vs Boosting | What is the difference between Bagging and Boosting
102_Bagging Vs Boosting What is the difference between Bagging and Boosting_100DaysofML_CampusX
K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning
103_K-Means Clustering Algorithm Geometric Intuition Clustering Unsupervised Learning_100DaysofML_CampusX
K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn
104_K-Means Clustering Algorithm in Python Practical Example Student Clustering Example sklearn_100DaysofML_CampusX
K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch
105_K-Means Clustering Algorithm From Scratch In Python ML Algorithms From Scratch_100DaysofML_CampusX
Gradient Boosting Explained | How Gradient Boosting Works?
106_ Gradient Boosting Explained How Gradient Boosting Works_100DaysofML_CampusX
Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting
107_Gradient Boosting Regression Part 2 Mathematics of Gradient Boosting_100DaysofML_CampusX
Gradient Boosting for Classification | Geometric Intuition | CampusX
108_Gradient Boosting for Classification Geometric Intuition CampusX_100DaysofML_CampusX
Stacking and Blending Ensembles
109_Stacking and Blending Ensembles_100DaysofML_CampusX
Agglomerative Hierarchical Clustering | Python Code Example
110_Agglomerative Hierarchical Clustering_100DaysofML_CampusX
What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX
111_What is K Nearest Neighbor KNN Explained in Hindi Simple Overview in 1 Video CampusX_100DaysofML_CampusX
What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression
112_What are the main Assumptions of Linear RegressionTop 5 Assumptions of Linear Regression_100DaysofML_CampusX
Support Vector Machines | Geometric Intuition
113_ Support Vector Machines Geometric Intuition_100DaysofML_CampusX
Mathematics of SVM | Support Vector Machines | Hard margin SVM
114_Mathematics of SVM Support Vector Machines Hard margin SVM_100DaysofML_CampusX
Mathematics of Support Vector Machine | Soft Margin SVM
115_Mathematics of Support Vector Machine Soft Margin SVM_100DaysofML_CampusX
Kernel Trick in SVM | Code Example
116_Kernel Trick in SVM Code Example_100DaysofML_CampusX
Kernel Trick in SVM | Geometric Intuition
117_Kernel Trick in SVM Geometric Intuition_100DaysofML_CampusX
Naive Bayes Classifier | Part 1 | Conditional Probability
118_Naive Bayes Classifier Part 1 Conditional Probability_100DaysofML_CampusX
Naive Bayes Classifier | Part 2 | Independent Events in Probability
119_Naive Bayes Classifier Part 2 Independent Events in Probability_100DaysofML_CampusX
Naive Bayes Classifier | Part 3 | Mutually Exclusive Events
120_Naive Bayes Classifier Part 3 Mutually Exclusive Events_100DaysofML_CampusX
Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability
121_Naive Bayes Classifier Part 4 Bayes Theorem in Probability_100DaysofML_CampusX
Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem
122_Naive Bayes Classifier Part 5 Problem based upon Bayes Theorem_100DaysofML_CampusX
Naive Bayes Classifier | Part 6 | Intuition
123_Naive Bayes Classifier Part 6 Intuition_100DaysofML_CampusX
Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm
124_Naive Bayes Classifier Part 7 Mathematics behind Naive Bayes Algorithm_100DaysofML_CampusX
Naive Bayes Classifier | Part 8 | Simple Example Code
125_ Naive Bayes Classifier Part 8 Simple Example Code_100DaysofML_CampusX
Naive Bayes Part 9 | Handling Numerical Data
126_Naive Bayes Part 9 Handling Numerical Data_100DaysofML_CampusX
Introduction to XGBOOST | Machine Learning | CampusX
127_Introduction to XGBOOST Machine Learning CampusX
XGBoost for Regression | XGBoost Part 2 | CampusX
128_XGBoost for Regression XGBoost Part 2_100DaysofML_CampusX
XGBoost For Classification | How XGBoost works on Classification Problems | CampusX
129_XGBoost For Classification How XGBoost works on Classification Problems_100DaysofML_CampusX
The Maths Behind XGBoost | Machine Learning | CampusX
130_The Maths Behind XGBoost Machine Learning
DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX
131_DBSCAN Clustering Algorithms Density Based Clustering How DBSCAN Works_100DaysofML_CampusX
Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE
132_Imbalanced Data in Machine Learning Undersampling Oversampling SMOTE_100DaysofML_CampusX
Hyperparameter Tuning using Optuna | Bayesian Optimization using Optuna
133_ Hyperparameter Tuning using Optuna Bayesian Optimization using Optuna_100DaysofML_CampusX
ROC Curve in Machine Learning | ROC-AUC in Machine Learning Simplified | CampusX
134_ROC Curve in Machine Learning ROC-AUC in Machine Learning Simplified_100DaysofML_CampusX