Machine learning enables computers to learn from data without being explicitly programmed. It involves supervised learning techniques like linear regression, decision trees, and neural networks to make predictions based on patterns in data. Unsupervised learning techniques like clustering are also used. Machine learning has applications in areas like medical diagnosis, self-driving cars, stock market analysis, recommendations, and Google search.

1. Machine Learning
Algorithms
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2. • Machine learning is a core sub-area of artificial
intelligence as it enables computers to get into a
mode of self-learning without being explicitly
programmed.
• Machine learning is method of teaching computers to
make future prediction based some data
Machine learning?

3. 1)Supervised Learning
• Linear Regression
• Nearest Neighbour
• Naive Bayes
• Decision Trees
• Support Vector Machines
(SVM)
• Neural Networks
2)Unsupervised Learning
• k-means clustering,
Association Rules
3)Reinforcement Learning
• Q-Learning
• Temporal Difference (TD)
• Deep Adversarial Networks
Types

4. Linear Regression
• It is also known as Least Squares Approach
• It is relationship between dependent and independent variables

5. Height
(X)
Weight
(Y)
1 65 105
2 65 125
3 62 110
4 67 120
5 69 140
6 65 135
7 61 95
8 67 130
9 71
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Example

6. Example conti..
• Linear model Y=b0+b1 X
Y-intercept Slop of line

7. Example conti..

8. Applications
• Medical diagnosis
• Self driving cars
• Stock market analysis
• Recommendations (amazon etc)
• Google search