MachineLlearning introduction
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This document serves as an introductory overview of machine learning, detailing its principles, types, and applications. It explains the differences between supervised, unsupervised, deep learning, and reinforcement learning, along with concepts such as features and regression models. The course emphasizes hands-on experience and the importance of relevant data in training machine learning algorithms to make predictions.
![Types of Machine Learning System
[NOC]](https://image.slidesharecdn.com/introductiontoml-200616172757/85/MachineLlearning-introduction-20-320.jpg)
MachineLlearning introduction
- 2. About this Course / Program FUN Motivation R E S E A R C H Strong Community Engagement
- 3. Agenda •Intro to Machine Learning •Types of Machine Learning System •Supervised Machine Learning •Regression •Hands-On
- 4. What is Machine Learning ? First Questions first
- 5. What is Machine Learning ?
- 6. What is Machine Learning ? Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions. It can be understood as Computers learning how to do a task without 'being explicitly programmed' to do so.
- 7. What is Machine Learning ? Machine Learning Algorithms are those that can tell you something interesting about the data (patterns !), without you having to write any custom code specific to the problem. Instead of writing code explicitly, we feed data to these ML algorithms and they build their own logic based on the data and its patterns.
- 8. What is Machine Learning ? Hence, ML is the “Art of Seeking Information and Meaning from Data”
- 9. What is Machine Learning ?
- 14. What are your favorite application of Machine Learning?
- 15. Some more terms
- 16. Some more terms
- 17. Deep Learning
- 18. Types of Machine Learning System Machine Learning Supervised Machine Learning Unsupervised Machine Learning Deep Learning Reinforcement Learning
- 19. Types of Machine Learning System Unsupervised Unsupervised learning is when we are dealing with data that has not been labeled or categorized. Supervised Supervised learning algorithm takes labeled data and creates a model that can make predictions given new data. Deep Learning Deep learning utilizes neural networks which, just like the human brain, contain interconnected neurons that can be activated or deactivated. Reinforcement Reinforcement learning uses a reward system and trial-and-error in order to maximize the long- term reward.
- 20. Types of Machine Learning System [NOC]
- 22. Classification vs. Regression ! CLASSIFICATION: In a classification problem, there might be test data consisting of photos of animals, each one labeled with its corresponding name. The model would be trained on this test data and then the model would be used to classify unlabeled animal photos with the correct name. REGRESSION: In a regression problem, there is a relationship trying to be determined among many different variables. Usually, this takes place in the form of historical data being used to predict future quantities. An example of this would be predicting the future price of a stock based on past prices movements.
- 23. What are Features ? Features are the variables which distinguish one example from another. They tell the machine learning model what parts of the data to look for patterns for achieving the goal. Lots of data is crucial to a machine learning system but it needs to be helpful and relevant data. Though you never know until you experiment to see what variables truly make an impact.
- 24. An Example Consider the problem, "Predicting the Price of a House" What features should we use ?
- 25. Features : Location Number of bedrooms No of floors Size of property Number of light switches? Colour of house? Parking Availability?
- 26. Weights & Bias: Weights and biases (commonly referred to as w and b or Ѳ {theta} notation) are the learnable parameters of a machine learning model. Weights control the signal (or the strength of the connection) between two neurons. In other words, a weight decides how much influence the input will have on the output. Biases, which are constant, are an additional input into the next layer that will always have the value of 1.
- 27. Regression (by fitting a curve / an equation to observed data). For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.
- 28. Regression
- 29. How to identify a Regression model ?
- 30. Ready to dive deeper?
- 31. Ready to dive deeper?
- 32. Linear Regression model w1 and w2 can also be called Ѳ1{theta1} and Ѳ2 and b as Ѳ0 b
- 34. The Loss Function (indicated in the graph by the dotted red line) (yellow dots)
- 35. Gradient Descent
- 36. Gradient Descent This is called Gradient Descent.
- 37. Gradient Descent
- 38. Don't use Linear Regression Blindly ;)
- 41. So that was Regression !!
- 42. Python??
- 43. Code: Linear regression #import data set #Splitting the data
- 44. Code: Linear regression #Fitting Simple Linear Regression #This is called Model ##Predicting the test results
- 45. Code: Linear regression #Visualising the training set Results
- 46. Code: Linear regression #Giving External Data for Price Prediction #if we want to take manual input from the user and then calculate the price