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Playlist · CampusX

100 Days of Machine Learning | CampusX

by CampusX · 134 videos

Listed in Best free machine learning courses on YouTube, ranked by total hours

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2d 14h 50m

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134 videos in blocks of 10 · each column is a block's total, to scale

3h 36m
4h 52m
4h 36m
3h 56m
4h 20m
7h 35m
4h 19m
5h 25m
1h 45m
4h 33m
3h 32m
2h 44m
8h 28m
3h 8m

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Videos (134)

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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
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