1
What is Machine Learning? | 100 Days of Machine Learning
20:00
2021-03-13
2
AI Vs ML Vs DL for Beginners in Hindi
16:02
2021-03-15
3
Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth
27:42
2021-03-16
4
Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types
11:28
2021-03-17
5
Online Machine Learning | Online Learning | Online Vs Offline Machine Learning
19:28
2021-03-18
6
Instance-Based Vs Model-Based Learning | Types of Machine Learning
16:44
2021-03-19
7
Challenges in Machine Learning | Problems in Machine Learning
23:40
2021-03-20
8
Application of Machine Learning | Real Life Machine Learning Applications
29:02
2021-03-22
9
Machine Learning Development Life Cycle | MLDLC in Data Science
25:13
2021-03-23
10
Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles
26:23
2021-03-24
11
What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning
41:29
2021-03-25
12
Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML
37:06
2021-03-26
13
End to End Toy Project | Day 13 | 100 Days of Machine Learning
30:43
2021-03-27
14
How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively
22:22
2021-03-29
15
Working with CSV files | Day 15 | 100 Days of Machine Learning
36:30
2021-03-30
16
Working with JSON/SQL | Day 16 | 100 Days of Machine Learning
17:00
2021-03-31
17
Fetching Data From an API | Day 17 | 100 Days of Machine Learning
22:50
2021-04-01
18
Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning
37:49
2021-04-02
19
Understanding Your Data | Day 19 | 100 Days of Machine Learning
15:23
2021-04-03
20
EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning
30:31
2021-04-05
21
EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning
38:03
2021-04-06
22
Pandas Profiling | Day 22 | 100 Days of Machine Learning
13:04
2021-04-07
23
What is Feature Engineering | Day 23 | 100 Days of Machine Learning
24:52
2021-04-08
24
Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning
32:38
2021-04-09
25
Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling
23:31
2021-04-10
26
Encoding Categorical Data | Ordinal Encoding | Label Encoding
19:53
2021-04-12
27
One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning
30:12
2021-04-13
28
Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn
15:41
2021-04-14
29
Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning
45:39
2021-04-15
30
Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform
32:13
2021-04-16
31
Power Transformer | Box - Cox Transform | Yeo - Johnson Transform
21:28
2021-04-17
32
Binning and Binarization | Discretization | Quantile Binning | KMeans Binning
38:25
2021-04-19
33
Handling Mixed Variables | Feature Engineering
12:10
2021-04-20
34
Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning
14:18
2021-04-21
35
Handling Missing Data | Part 1 | Complete Case Analysis
24:54
2021-04-22
36
Handling missing data | Numerical Data | Simple Imputer
31:21
2021-04-23
37
Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp
13:34
2021-04-24
38
Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4
37:05
2021-04-26
39
KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5
24:27
2021-04-27
40
Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer
18:31
2021-04-28
41
What are Outliers | Outliers in Machine Learning
17:07
2021-04-29
42
Outlier Detection and Removal using Z-score Method | Handling Outliers Part 2
17:46
2021-04-30
43
Outlier Detection and Removal using the IQR Method | Handing Outliers Part 3
14:05
2021-05-01
44
Outlier Detection using the Percentile Method | Winsorization Technique
16:23
2021-05-03
45
Feature Construction | Feature Splitting
12:22
2021-05-04
46
Curse of Dimensionality
15:25
2021-05-05
47
Principle Component Analysis (PCA) | Part 1 | Geometric Intuition
33:54
2021-05-06
48
Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution
56:17
2021-05-07
49
Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization
43:26
2021-05-08
50
Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi
33:36
2021-05-10
51
Simple Linear Regression | Mathematical Formulation | Coding from Scratch
53:31
2021-05-11
52
Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score
43:56
2021-05-13
53
Multiple Linear Regression | Geometric Intuition & Code
20:57
2021-05-14
54
Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch
48:11
2021-05-15
55
Multiple Linear Regression | Part 3 | Code From Scratch
16:01
2021-05-17
56
What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression
17:38
2022-06-20
57
Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation
1:57:56
2021-05-21
58
Batch Gradient Descent with Code Demo | Simple Explanation in Hindi
1:04:49
2021-05-22
59
Stochastic Gradient Descent
49:35
2021-05-25
60
Mini-Batch Gradient Descent
22:10
2021-05-26
61
Polynomial Regression | Machine Learning
26:46
2021-05-29
62
Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning
8:05
2020-06-05
63
Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models
19:58
2021-06-02
64
Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models
43:41
2021-06-03
65
Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models
18:43
2021-06-04
66
5 Key Points - Ridge Regression | Part 4 | Regularized Linear Models
30:17
2021-06-08
67
Lasso Regression | Intuition and Code Sample | Regularized Linear Models
28:37
2021-06-10
68
Why Lasso Regression creates sparsity?
24:30
2021-06-11
69
ElasticNet Regression | Intuition and Code Example | Regularized Linear Models
11:41
2021-06-12
70
Logistic Regression Part 1 | Perceptron Trick
47:06
2021-06-15
71
Logistic Regression Part 2 | Perceptron Trick Code
17:07
2021-06-16
72
Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML
40:44
2021-06-17
73
Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy
29:03
2021-06-18
74
Derivative of Sigmoid Function
5:57
2021-06-19
75
Logistic Regression Part 5 | Gradient Descent & Code From Scratch
36:42
2021-06-21
76
Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 1
34:08
2021-06-23
77
Precision, Recall and F1 Score | Classification Metrics Part 2
42:42
2021-06-24
78
ROC Curve in Machine Learning | ROC-AUC in Machine Learning Simplified | CampusX
1:11:15
2023-06-07
79
Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 6
38:21
2021-06-28
80
Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 7
9:11
2021-06-29
81
Logistic Regression Hyperparameters || Logistic Regression Part 8
13:07
2021-06-30
82
Naive Bayes Classifier | Part 1 | Conditional Probability
9:26
2020-03-14
83
Naive Bayes Classifier | Part 2 | Independent Events in Probability
7:59
2020-03-14
84
Naive Bayes Classifier | Part 3 | Mutually Exclusive Events
1:49
2020-03-14
85
Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability
4:27
2020-03-14
86
Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem
9:00
2020-03-14
87
Naive Bayes Classifier | Part 6 | Intuition
14:44
2020-03-14
88
Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm
19:09
2020-03-14
89
Naive Bayes Classifier | Part 8 | Simple Example Code
16:03
2020-03-14
90
Naive Bayes Part 9 | Handling Numerical Data
8:47
2020-04-27
91
What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX
52:01
2023-05-23
92
Support Vector Machines | Geometric Intuition
11:46
2020-07-10
93
Mathematics of SVM | Support Vector Machines | Hard margin SVM
34:54
2020-07-10
94
Mathematics of Support Vector Machine | Soft Margin SVM
14:38
2020-08-07
95
Kernel Trick in SVM | Geometric Intuition
6:18
2020-08-07
96
Kernel Trick in SVM | Code Example
14:04
2020-08-07
97
Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain
58:29
2021-07-02
98
Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees
27:23
2021-07-03
99
Regression Trees | Decision Trees Part 3
35:15
2021-07-05
100
Awesome Decision Tree Visualization using dtreeviz library
18:36
2021-03-19
101
Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning
37:43
2021-07-12
102
Voting Ensemble | Introduction and Core Idea | Part 1
16:30
2021-03-09
103
Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 2
23:50
2021-03-10
104
Voting Ensemble | Regression | Part 3
10:57
2021-03-11
105
Bagging | Introduction | Part 1
31:13
2021-03-12
106
Bagging Ensemble | Part 2 | Bagging Classifiers
22:32
2021-03-16
107
Bagging Ensemble | Part 3 | Bagging Regressor
10:55
2021-03-17
108
Introduction to Random Forest | Intuition behind the Algorithm
33:55
2021-07-19
109
How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest
12:52
2021-07-20
110
Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important
12:02
2021-07-22
111
Random Forest Hyper-parameters
15:17
2021-07-23
112
Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example
11:44
2021-07-26
113
OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning
6:45
2021-07-28
114
Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated
27:20
2021-07-30
115
How Adaboost Classifier Works? | Geometric Intuition
17:14
2021-08-09
116
AdaBoost - A Step by Step Explanation
19:23
2021-08-10
117
AdaBoost Algorithm | Code from Scratch
16:27
2021-08-16
118
AdaBoost Hyperparameters | GridSearchCV in Adaboost
11:13
2021-08-17
119
Bagging Vs Boosting | What is the difference between Bagging and Boosting
6:17
2021-08-18
120
Gradient Boosting Explained | How Gradient Boosting Works?
32:49
2021-09-09
121
Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting
56:42
2021-09-20
122
Gradient Boosting for Classification | Geometric Intuition | CampusX
1:04:33
2023-08-08
123
Introduction to XGBOOST | Machine Learning | CampusX
1:19:37
2023-10-14
124
XGBoost for Regression | XGBoost Part 2 | CampusX
47:17
2023-10-24
125
XGBoost For Classification | How XGBoost works on Classification Problems | CampusX
39:08
2023-11-12
126
The Maths Behind XGBoost | Machine Learning | CampusX
1:57:28
2023-12-02
127
Stacking and Blending Ensembles
35:20
2021-09-30
128
K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning
23:58
2021-08-24
129
K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn
10:13
2021-08-26
130
K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch
33:53
2021-08-25
131
Agglomerative Hierarchical Clustering | Python Code Example
37:23
2021-11-07
132
DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX
34:16
2023-12-17
133
Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE
57:17
2024-05-04
134
Hyperparameter Tuning using Optuna | Bayesian Optimization using Optuna
59:23
2024-09-24
