Playlist Duration

Playlist · Gate Smashers

Machine Learning

by Gate Smashers · 55 videos

Total watch time

8h 20m

at 1× speed · exactly 8 hours, 19 minutes, 54 seconds at 1×

At an hour a day you'd finish in 9 days

The shape of the course

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

1h 30m
1h 26m
1h 4m
1h 23m
2h 3m
53m 21s

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Videos (55)

8h 20m in total · tick what you've watched

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Videos to
Watched Leave out of the total
1 New to ML? Follow These Steps to Build Any Machine Learning Model 9:59 2025-07-26
2 Lec-1: Introduction to Data Science & ML | Roadmap to Learn Data Science & ML 8:24 2023-08-19
3 Lec-2: Supervised Learning Algorithms | Machine Learning 8:50 2023-08-25
4 Lec-3: Introduction to Regression with Real Life Examples 7:19 2023-08-24
5 Lec-4: Linear Regression📈 with Real life examples & Calculations | Easiest Explanation 11:01 2023-08-28
6 Lec-5: Logistic Regression with Simplest & Easiest Example | Machine Learning 10:01 2023-09-02
7 Lec-6: Linear Regression Vs. Logistic Regression | Supervised Learning | Machine Learning 4:37 2023-09-05
8 Lec-7: kNN Classification with Real Life Example | Movie Imdb Example | Supervised Learning 10:13 2023-08-29
9 Lec-8: Naive Bayes Classification Full Explanation with examples | Supervised Learning 13:31 2023-09-01
10 Lec-9: Introduction to Decision Tree 🌲 with Real life examples 6:07 2023-09-06
11 Lec-10: Decision Tree 🌲 ID3 Algorithm with Example & Calculations 🧮 16:38 2023-09-07
12 Lec-11: Conditional Probability with Easiest Explanation & Example 6:23 2023-09-08
13 Lec-12: Introduction to Ensemble Learning with Real Life Examples | Machine⚙️ Learning 5:58 2023-09-09
14 Lec-13: K-mean Clustering with Numerical Example | Unsupervised Learning | Machine🖥️ Learning 🙇‍♂️🙇 7:51 2023-11-04
15 Lec-14: Hierarchical Clustering | Agglomerative vs Divisive with examples 6:06 2023-11-05
16 Lec-15: Single Linkage Clustering | Agglomerative Clustering | Hierarchical Clustering 6:16 2023-11-15
17 Lec-16: Complete Linkage⛓️ Clustering with Example | Clustering in Unsupervised Learning | ML 9:05 2023-11-23
18 Lec-17: K-medoids Clustering with Numerical Example | Machine Learning 11:53 2023-11-19
19 Lec-18: Random Forest 🌳 in Machine Learning 🧑‍💻👩‍💻 8:33 2023-12-06
20 Lec-19: kNN for Classified & Regression with Easiest Explanation | Machine Learning 🤖🙇 7:21 2023-12-08
21 Lec-20: Mean, Median, Mode with Real Life examples | Machine Learning 7:41 2023-12-11
22 Lec-21: Standard Deviation & Variance with Examples 8:16 2023-12-12
23 Lec-22: Bagging/Bootstrap Aggregating in Machine Learning with examples 4:56 2023-12-14
24 Lec-23: Supervised vs Unsupervised learning with real life example 7:31 2023-12-15
25 Python code for Mean, Median, Mode, SD, Variance and Range 0:48 2023-12-18
26 Lec-24: How Weights are Increased in Boosting | Ensemble Learning 6:48 2023-12-19
27 Lec-25: BAGGING vs. BOOSTING vs STACKING in Ensemble Learning | Machine Learning 6:22 2023-12-20
28 Lec-48: Bayes Theorem & Total Probability with Examples 7:16 2024-01-14
29 Lec-26: Cross Validation in Machine Learning with Examples 6:51 2024-01-14
30 Lec-27: Pearson's Correlation Coefficient | Supervised Learning | Data Science & Machine Learning 7:38 2024-01-26
31 Lec-28: kNN(k Nearest Neighbour) Numerical Example | Supervised Learning | Machine Learning 9:09 2024-02-24
32 Lec-29: Decision Tree 🌳 Example | Calculate Entropy, Information ℹ️ Gain | Supervised Learning 6:57 2024-02-26
33 Lec-30: Single Linkage Clustering Example | Unsupervised Learning | Machine Learning 6:52 2024-02-29
34 Lec-31: Token & Parameters in LLama3 META Models | 8B & 70B Parameters Model | GPT model 7:09 2024-05-02
35 Lec-32: What is Data Preprocessing & Data Cleaning | Various Techniques with Example 5:53 2024-05-11
36 Lec-33: How to Deal with Missing Values in DataSet | Data Preprocessing & Data Cleaning 9:27 2024-05-06
37 Lec-34: kNN Imputation with Examples | Data Preprocessing and Data Cleaning 🧹 7:51 2024-05-07
38 Lec-35: Fit() & Transform() Method | Data Preprocessing | Machine Learning 6:36 2024-05-09
39 Lec-36: Feature Extraction in Data preprocessing | Machine Learning 9:21 2024-05-10
40 Lec-37: Ridge and Lasso Regression | Machine Learning 14:10 2025-03-04
41 Lec-38: Mean Squared Error (MSE) | Machine learning 9:53 2025-03-18
42 Lec-39: Multiple Linear Regression (MLR) | Machine Learning 12:48 2025-03-17
43 Lec-40: Support Vector Machines (SVMs) | Machine Learning 10:23 2025-03-21
44 Lec-41: Numerical Explanation on SVM | How Support Vector Machine Algorithm Works 16:07 2025-03-26
45 Lec-42: Linear Discriminant Analysis (LDA) | Machine Learning 13:21 2025-03-19
46 Lec-43: Bias & Variance Tradeoff Explained: How to Fix Overfitting & Underfitting? 14:44 2025-04-02
47 Lec-44: K-Fold Cross Validation in Machine Learning 9:52 2025-04-01
48 Lec-45: Leave-One-Out Cross Validation (LOOCV) Explained with Example | Machine Learning 9:36 2025-04-02
49 Lec-46: Principal Component Analysis (PCA) Explained | Machine Learning 14:06 2025-04-06
50 Lec-47: How to update cost in K-Medoid Clustering | Machine Learning 12:05 2025-04-05
51 Lec-48: Perceptron Learning in ANN | Single Layer Perceptron Model 15:04 2025-04-03
52 Lec-49: What is Multilayer Perceptron (MLP)? | How It Works in Machine Learning 12:56 2025-04-04
53 Lec-50: Single Layer Neural Network | Machine Learning 12:03 2025-04-07
54 What is Data Leakage | Data Leakage vs Overfitting Explained 5:08 2026-08-13
55 Confusion Matrix: TP, TN, FP & FN Explained | Machine Learning 8:10 2026-08-18
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