100 Days of Machine Learning

1.

20:00

What is Machine Learning? | 100 Days of Machine Learning
2.

16:02

AI Vs ML Vs DL for Beginners in Hindi
3.

27:42

Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth
4.

11:28

Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types
5.

19:28

Online Machine Learning | Online Learning | Online Vs Offline Machine Learning
6.

16:44

Instance-Based Vs Model-Based Learning | Types of Machine Learning
7.

23:40

Challenges in Machine Learning | Problems in Machine Learning
8.

29:02

Application of Machine Learning | Real Life Machine Learning Applications
9.

25:13

Machine Learning Development Life Cycle | MLDLC in Data Science
10.

26:23

Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles
11.

41:29

What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning
12.

37:06

Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML
13.

30:43

End to End Toy Project | Day 13 | 100 Days of Machine Learning
14.

22:22

How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively
15.

36:30

Working with CSV files | Day 15 | 100 Days of Machine Learning
16.

17:00

Working with JSON/SQL | Day 16 | 100 Days of Machine Learning
17.

22:50

Fetching Data From an API | Day 17 | 100 Days of Machine Learning
18.

37:49

Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning
19.

15:23

Understanding Your Data | Day 19 | 100 Days of Machine Learning
20.

30:31

EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning
21.

38:03

EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning
22.

13:04

Pandas Profiling | Day 22 | 100 Days of Machine Learning
23.

24:52

What is Feature Engineering | Day 23 | 100 Days of Machine Learning
24.

32:38

Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning
25.

23:31

Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling
26.

19:53

Encoding Categorical Data | Ordinal Encoding | Label Encoding
27.

30:12

One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning
28.

15:41

Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn
29.

45:39

Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning
30.

32:13

Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform
31.

21:28

Power Transformer | Box - Cox Transform | Yeo - Johnson Transform
32.

38:25

Binning and Binarization | Discretization | Quantile Binning | KMeans Binning
33.

12:10

Handling Mixed Variables | Feature Engineering
34.

14:18

Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning
35.

24:54

Handling Missing Data | Part 1 | Complete Case Analysis
36.

31:21

Handling missing data | Numerical Data | Simple Imputer
37.

13:34

Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp
38.

37:05

Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4
39.

24:27

KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5
40.

18:31

Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer
41.

17:07

What are Outliers | Outliers in Machine Learning
42.

17:46

Outlier Detection and Removal using Z-score Method | Handling Outliers Part 2
43.

14:05

Outlier Detection and Removal using the IQR Method | Handing Outliers Part 3
44.

16:23

Outlier Detection using the Percentile Method | Winsorization Technique
45.

12:22

Feature Construction | Feature Splitting
46.

15:25

Curse of Dimensionality
47.

33:54

Principle Component Analysis (PCA) | Part 1 | Geometric Intuition
48.

56:17

Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution
49.

43:26

Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization
50.

33:36

Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi
51.

53:31

Simple Linear Regression | Mathematical Formulation | Coding from Scratch
52.

43:56

Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score
53.

20:57

Multiple Linear Regression | Geometric Intuition & Code
54.

48:11

Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch
55.

16:01

Multiple Linear Regression | Part 3 | Code From Scratch
56.

1:57:56

Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation
57.

1:04:49

Batch Gradient Descent with Code Demo | Simple Explanation in Hindi
58.

49:35

Stochastic Gradient Descent
59.

22:10

Mini-Batch Gradient Descent
60.

26:46

Polynomial Regression | Machine Learning
61.

8:05

Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning
62.

19:58

Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models
63.

43:41

Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models
64.

18:43

Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models
65.

30:17

5 Key Points - Ridge Regression | Part 4 | Regularized Linear Models
66.

28:37

Lasso Regression | Intuition and Code Sample | Regularized Linear Models
67.

24:30

Why Lasso Regression creates sparsity?
68.

11:41

ElasticNet Regression | Intuition and Code Example | Regularized Linear Models
69.

47:06

Logistic Regression Part 1 | Perceptron Trick
70.

17:07

Logistic Regression Part 2 | Perceptron Trick Code
71.

40:44

Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML
72.

29:03

Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy
73.

5:57

Derivative of Sigmoid Function
74.

36:42

Logistic Regression Part 5 | Gradient Descent & Code From Scratch
75.

34:08

Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 1
76.

42:42

Precision, Recall and F1 Score | Classification Metrics Part 2
77.

38:21

Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 6
78.

9:11

Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 7
79.

13:07

Logistic Regression Hyperparameters || Logistic Regression Part 8
80.

58:29

Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain
81.

27:23

Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees
82.

35:15

Regression Trees | Decision Trees Part 3
83.

18:36

Awesome Decision Tree Visualization using dtreeviz library
84.

37:43

Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning
85.

16:30

Voting Ensemble | Introduction and Core Idea | Part 1
86.

23:50

Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 2
87.

10:57

Voting Ensemble | Regression | Part 3
88.

31:13

Bagging | Introduction | Part 1
89.

22:32

Bagging Ensemble | Part 2 | Bagging Classifiers
90.

10:55

Bagging Ensemble | Part 3 | Bagging Regressor
91.

33:55

Introduction to Random Forest | Intuition behind the Algorithm
92.

12:52

How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest
93.

12:02

Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important
94.

15:17

Random Forest Hyper-parameters
95.

11:44

Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example
96.

6:45

OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning
97.

27:20

Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated
98.

17:14

How Adaboost Classifier Works? | Geometric Intuition
99.

19:23

AdaBoost - A Step by Step Explanation
100.

16:27

AdaBoost Algorithm | Code from Scratch
101.

11:13

AdaBoost Hyperparameters | GridSearchCV in Adaboost
102.

6:17

Bagging Vs Boosting | What is the difference between Bagging and Boosting
103.

23:58

K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning
104.

10:13

K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn
105.

33:53

K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch
106.

32:49

Gradient Boosting Explained | How Gradient Boosting Works?
107.

56:42

Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting
108.

1:04:33

Gradient Boosting for Classification | Geometric Intuition | CampusX
109.

35:20

Stacking and Blending Ensembles
110.

37:23

Agglomerative Hierarchical Clustering | Python Code Example
111.

52:01

What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX
112.

17:38

What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression
113.

11:46

Support Vector Machines | Geometric Intuition
114.

34:54

Mathematics of SVM | Support Vector Machines | Hard margin SVM
115.

14:38

Mathematics of Support Vector Machine | Soft Margin SVM
116.

14:04

Kernel Trick in SVM | Code Example
117.

6:18

Kernel Trick in SVM | Geometric Intuition
118.

9:26

Naive Bayes Classifier | Part 1 | Conditional Probability
119.

7:59

Naive Bayes Classifier | Part 2 | Independent Events in Probability
120.

1:49

Naive Bayes Classifier | Part 3 | Mutually Exclusive Events
121.

4:27

Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability
122.

9:00

Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem
123.

14:44

Naive Bayes Classifier | Part 6 | Intuition
124.

19:09

Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm
125.

16:03

Naive Bayes Classifier | Part 8 | Simple Example Code
126.

8:47

Naive Bayes Part 9 | Handling Numerical Data
127.

1:19:37

Introduction to XGBOOST | Machine Learning | CampusX
128.

47:17

XGBoost for Regression | XGBoost Part 2 | CampusX
129.

39:08

XGBoost For Classification | How XGBoost works on Classification Problems | CampusX
130.

1:57:28

The Maths Behind XGBoost | Machine Learning | CampusX
131.

34:16

DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX
132.

57:17

Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE