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ML GDSC GHRECM.pptx
- 2. Who_AM_I
- 3. What is Machine Learning?
- 4. OUTPUT LAYER ACTIVATED NEURONS INPUT LAYER Image adapted from becominghuman.ai by Venkatesh Tata
- 6. Each has its benefits Rule-based Approach Machine Learning – Rules are defined – Improvements come from algorithms and network – Learns patterns from data – Improvements may from additional data Why ML?
- 8. Ye Maths use kaha hota hai yar 😥?
- 10. Maths ● Statistics & Regression ● Integration & Differentiation
- 11. Machine Learning LifeCycle ML Extended PROPRIETARY + CONFIDENTIAL
- 12. ML Extended PROPRIETARY + CONFIDENTIAL
- 13. Data Cleaning ML Extended PROPRIETARY + CONFIDENTIAL
- 14. ML Extended PROPRIETARY + CONFIDENTIAL Data
- 15. ML Extended PROPRIETARY + CONFIDENTIAL Bunty the Data Engineer
- 16. ML Extended PROPRIETARY + CONFIDENTIAL Data Cleaning Technique ● Remove duplicate or irrelevant observations ● SQL Normalization ● Filter unwanted outliers
- 17. ML Extended PROPRIETARY + CONFIDENTIAL Types of Data ● Matrix based Data ● Statistical Data ● Image Data …………………… so on
- 18. ML Extended PROPRIETARY + CONFIDENTIAL ● Linear Transformation ● Standard Deviation ● Laplace Transform
- 20. Data Visualization Tools ● Excel ● Tabuleau ● Google Charts ● Qlik
- 21. ML Extended PROPRIETARY + CONFIDENTIAL Types Machine Learning 1. Supervised Machine Learning 2. Unsupervised Machine Learning 3. Reinforcement Learning
- 22. Model learns patterns from unlabelled data. Machine Learning Supervised Unsupervised Model is trained on labeled data stop_sign_4 stop_sign_1 stop_sign_2 stop_sign_3
- 24. Collect Data Train and Test Model Define Objective Predict and Evaluate Focus on User Machine Learning Process
- 25. Collect Data Train and Test Model Define Objective Predict and Evaluate Focus on User Machine Learning Process
- 29. Use of ML - Object Detection
- 30. Use of ML - Chatbot ?
- 31. Use of ML - Chatbot
- 32. Use of ML - Chatbot
- 33. Use of ML - Product Recommendation System
- 34. Use of ML - Stock Market Prediction
- 36. TensorFlow is a free and open-source software library for machine learning and artificial intelligence
- 39. QUIZ TIME
- 40. What is Machine Learning? A. The autonomous acquisition of knowledge through the use of manual programs B. The selective acquisition of knowledge through the use of manual programs C. The autonomous acquisition of knowledge through the use of computer programs D. The selective acquisition of knowledge through the use of computer programs
- 41. Machine Learning is a subset of? A. Data Learning B. Deep Learning C. Artificial Intelligence D. None of the above
- 42. How many types of Machine Learning? A. 3 B. 5 C. 7 D. 9
- 43. What device below is not an example of machine learning? A. Wearable fitness tracker B. Google assistant C. Speech to text D. Google search E. None of the above
- 44. Which of the following does not use machine learning below? A. Prediction B. Image recognition C. Face recognition D. Medical diagnoses E. Feeding the newborn
- 45. Among the following options identify the one which is not a type of learning? A. Semi unsupervised learning B. Supervised learning C. Reinforcement learning D. Unsupervised learning
- 46. Identify the kind of learning algorithm for “facial identities for facial expressions” A. Prediction B. Recognition pattern C. Recognizing anomalies D. Generating patterns
- 47. What is the application of machine learning methods to a large database called? A. Big data computing B. Internet of things C. Data mining D. AI
- 48. Identify the successful applications of ML A. Learning to classify new astronomical structures. B. Learning to recognize spoken words C. Learning to drive an autonomous vehicle. D. All the above.
- 49. Artificial Intelligence is the process that allows a computer to learn and make decisions. A. True B. False
Editor's Notes
- This session will go into a little more detail about machine learning. It is helpful if you have already completed some introduction to machine learning but if you have any questions please ask.
- This session will go into a little more detail about machine learning. It is helpful if you have already completed some introduction to machine learning but if you have any questions please ask.
- Let's review. How would you explain machine learning? Answers could include (shown on the following slides): Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions. Computers learning how to do a task without being explicitly programmed to do so.
- What is a neural network? In short it’s a mathematical function that maps a given input to a desired output. In the Introductory module, you learned about a couple types of machine learning models such as classification, regression, etc. Simple models work fine work simple data but as soon as you want to interpret complex data like an image, audio, or even numerical data with lots of variables e.g. for housing prices = location + number of bedrooms + size + etc you need something capable of dealing with that complexity. These simpler models are connected into layers and this is where the depth of "deep" learning comes from. In reality a neural network is a big complex mathematical function which looks nothing like what you see here but the diagram may help you see what's going on. Like any other machine learning system, a model is trained with data fed into the model and it attempts to make a prediction. At first the prediction will be essentially garbage but that feedback is used to improve the model. Once the prediction quality is sufficient, the model is tested with new data and it makes a prediction based on what it learned from the training data. What this diagram doesn't show is how each neuron is able to learn and improve based on feedback. Let's look more deeply at how a neuron is able to make a decision. Image adapted from becominghuman.ai by Venkatesh Tata
- Let's review. How would you explain machine learning? Answers could include (shown on the following slides): Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions. Computers learning how to do a task without being explicitly programmed to do so.
- Machine learning will not become the default option for software. It is best suited for certain situations but not all. Question: What are other examples of machine learning you could imagine?
- In news articles and discussions, it's common to hear artificial intelligence (AI), machine learning (ML), and deep learning (DL) used interchangibly but there are distinctions between them AI Artificial Intelligence is defined as any technology which appears to do something smart. This can be anything from programmed software to deep learning models which mimic human intelligence ML Machine learning is a specific kind of artificial intelligence but rather than a rule-based approach, the system learns how to do something from rather than being explicitly told what to do.examples DL Deep learning is a specific type of machine learning using a technique known as a neural network which connects multiple models together to solve even more complex types of problems. Deep Learning, similar to other ML models, learns via examples. It's unique because it connects models to other models in layers in order to handle more complex types of data like as images. Diagram source: Google (author: ostrowskid@)
- Let's review. How would you explain machine learning? Answers could include (shown on the following slides): Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions. Computers learning how to do a task without being explicitly programmed to do so.
- Let's review. How would you explain machine learning? Answers could include (shown on the following slides): Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions. Computers learning how to do a task without being explicitly programmed to do so.
- Let's review. How would you explain machine learning? Answers could include (shown on the following slides): Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions. Computers learning how to do a task without being explicitly programmed to do so.
- Here's one definition.
- Here's one definition.
- Here's one definition.
- Here's one definition.
- Here's one definition.
- Here's one definition.
- Here's one definition.
- Here's one definition.
- In the previous slide, I said supervised machine learning is analogous to a student taking a test. Let's say I created the 4 machine learning regression models above. Which one is the best? It depends on your goal and what variable you are trying to optimize for. To grade how well a model is doing on its test, machine learning practitioners measure the distance between the model's prediction (indicated in these graphs by the blue line) and the example data. This is known as loss. [Click to animate slide] With the loss displayed. Which model is best at achieving the goal? [Click to animate slide] Model #3 has the lowest loss which indicates this model is best at achieving the goal.
- In the previous slide, I said supervised machine learning is analogous to a student taking a test. Let's say I created the 4 machine learning regression models above. Which one is the best? It depends on your goal and what variable you are trying to optimize for. To grade how well a model is doing on its test, machine learning practitioners measure the distance between the model's prediction (indicated in these graphs by the blue line) and the example data. This is known as loss. [Click to animate slide] With the loss displayed. Which model is best at achieving the goal? [Click to animate slide] Model #3 has the lowest loss which indicates this model is best at achieving the goal.
- Here's one definition.
- Machine learning looks for patterns in data. The majority of ML applications today are supervised learning where labeled data is used to teach the model. This includes tasks like classification and regression. Supervised machine learning is analogous to a student learning a tricky maths concept by studying a set of questions and their corresponding answers. After mastering the mapping between questions and answers, the student can then provide answers to new (never-before-seen) questions on the same topic. The signs on the left are labeled data so a supervised machine learning system could learn from these labels what stop signs around the world look like. The signs on the right are unlabeled data. Even the sign with the word stop because labeled data requires a human's rating to indicate what it is. Images: https://wikipedia.org/wiki/Stop_sign https://wikipedia.org/wiki/Comparison_of_European_road_signs
- Machine learning looks for patterns in data. The majority of ML applications today are supervised learning where labeled data is used to teach the model. This includes tasks like classification and regression. Supervised machine learning is analogous to a student learning a tricky maths concept by studying a set of questions and their corresponding answers. After mastering the mapping between questions and answers, the student can then provide answers to new (never-before-seen) questions on the same topic. The signs on the left are labeled data so a supervised machine learning system could learn from these labels what stop signs around the world look like. The signs on the right are unlabeled data. Even the sign with the word stop because labeled data requires a human's rating to indicate what it is. Images: https://wikipedia.org/wiki/Stop_sign https://wikipedia.org/wiki/Comparison_of_European_road_signs
- Machine learning begins with the needs of your user and business. With this in mind, you define an objective so you know how to proceed. Examples could include: Predict which friends a user is likely to share a photo with. Suggest the user should eat in a new city based on the restaurants they have visited in the past. These goals should articulate success metrics as well. Machine learning models learn from examples so it is essential to find a large existing source of data that is relevant to your problem. Experts say, collecting, cleaning, exploring, and other data processes tend to be the longest but most critical part of the process. The arrow pointing back from prediction to data shows the iterative nature of ML. You will need to refine your model and perhaps your data collection and processing based on the feedback until it is achieving the results you are looking for.
- Machine learning begins with the needs of your user and business. With this in mind, you define an objective so you know how to proceed. Examples could include: Predict which friends a user is likely to share a photo with. Suggest the user should eat in a new city based on the restaurants they have visited in the past. These goals should articulate success metrics as well. Machine learning models learn from examples so it is essential to find a large existing source of data that is relevant to your problem. Experts say, collecting, cleaning, exploring, and other data processes tend to be the longest but most critical part of the process. The arrow pointing back from prediction to data shows the iterative nature of ML. You will need to refine your model and perhaps your data collection and processing based on the feedback until it is achieving the results you are looking for.
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google
- In order to assist users, it can be helpful to predict what they might do next. This could be a prediction of the next keyboard key a user will select as you see in the screenshot. This could be used to propose a spelling correction or suggest replies to a text message. Other examples of sequence prediction could include the next video a user might want to watch or a next stop on a vacation. Sequence diagram source: Google Keyboard source: Google