Machine Learning

Overview

Videos in this playlist 55

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

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