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Machine learning full guide gdsc haldia
- 2. What we will Learn today :- • Introduction to Machine Learning • Simple Linear Regression • Loss/Cost/Error Function
- 3. What is Machine Learning? Machine learning is teaching computers to learn from data, like a student getting smarter by studying.
- 4. AI vs ML vs DL vs DS vs GenAI
- 5. Types of Machine Learning ● Supervised Machine Learning ● Un-Supervised Machine Learning ● Reinforcement Learning ● Semi Supervised Machine Learning
- 6. Supervised Machine Learning Supervised learning trains with labelled data (think flashcards) to make predictions on new data Labelled data - This has a label attached that tells you what the data is. There are two main categories of supervised learning: • Regression- output will be Numerical , Decimal value • Classification – output will be categorical value like ‘dog or not dog’.
- 7. Un-Supervised Machine Learning Unsupervised learning explores unlabelled data, finding hidden patterns and groups Unlabelled data - This is raw data without any labels. There are three main categories of supervised learning: • Clustering: This groups similar data points together, like sorting customers by purchase history. • Association rule learning: This finds relationships between data points • Dimensionality reduction: This simplifies complex data by reducing the number of features, making it easier to analyse.
- 8. Simple Linear Regression (supervised ML) Output – continuous values
- 9. Simple Linear Regression (supervised ML) Goal – To create a best fit line in such a way that the summation of the error will be minimum. Prediction Formula y_pred = mx + c
- 11. Thank You
- 13. What we will Learn today :- • Convergence Algorithm • Multiple Linear Regression • Types of Cost/Loss Function
- 14. Convergence Algorithm Convergence in machine learning is when a model's performance stops improving during training, signifying it has reached an optimal state.
- 17. Multiple Linear Regression Multiple linear regression predicts a value using several factors. Imagine estimating house prices based on size, location, and number of bedrooms. So our equation will be :- Y_pred = c + m1x1 + m2x2 + m3x3 + …….+mnxn
- 19. How will your gradient descent look in case of 2 Input feature
- 20. Types of Cost Function: • Mean Squared Error • Mean Absolute Error • Root Mean Squared Error
- 23. Root Mean Square Error
- 24. Thank You
- 26. What we will Learn today :- • Covariance, Pearson corelation coefficient, Spearman rank corelation • Performance Metrices • Regularization • Elastic Net Regression
- 27. Covariance It Quantifies the relationship between x and y
- 28. When it will be 0?
- 33. R_Squared :-
- 36. Ridge Regression
- 37. Lasso Regression
- 39. Thank You
- 41. What we will Learn today :- • Bias Variance Trade-off • Logistic Regression • Performance metrices
- 42. Bias Varience Trade off Training Accuracy Testing Accuracy
- 49. Cost Function:-
- 51. Performance Metrices:- • Confusion Metrix • Accuracy Score • Precision • Recall • F-beta
- 53. Confusion Metrix
- 54. Accuracy
- 56. Precision
- 57. Recall Assignment – Predict whether a person has diabetes or not
- 58. F-beta Tomorrow Stock market is going to crash
- 59. Thank You
- 61. What we will Learn today :- • Multicollinearity • Decision Tree Classifier • Decision Tree Regressor
- 63. Decision Tree • Decision Tree Classifier • Decision Tree Regressor
- 66. Purity Check
- 67. Entropy
- 68. Gini Index
- 70. Information Gain
- 71. Decision Tree Post Pruning and Pre Pruning
- 72. Pre pruning
- 76. Thank You