Introduction to Machine learning with Python
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This document discusses machine learning with Python. It begins with an introduction to artificial intelligence and machine learning, highlighting key events. It then discusses why machine learning is useful, including developing adaptive systems, data mining, and replacing monotonous tasks. The document introduces Python as a language for machine learning and describes supervised and unsupervised learning algorithms. It provides examples of using supervised learning for classification and unsupervised learning for clustering. The document concludes with a question and answer section.
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Introduction to Machine learning with Python
- 1. Machine Learning With Python Chariza Pladin Data Analyst - Accenture Inc. chariza.b.pladin@accenture.com AUG 2017
- 2. AGENDA ● Artificial Intelligence ● Machine Learning ● Introduction to Python ● Machine Learning Algorithms ● Supervised Learning ● Unsupervised Learning ● Q/A MACHINE LEARNING WITH PYTHON | AUG 2017
- 4. “Intelligence exhibited by machines, rather than humans or other animals” MACHINE LEARNING WITH PYTHON | AUG 2017
- 5. 1943 1950 1956 1968 1969 1973 1981 1990 1997 AI KEYNOTES THROUGHOUT TIME 2002 2005 2008 2010 2011 2014 WW2 Robot Story Top-Down Approach 2001: A Space Odyssey Shakey the Robot The AI Winter A solution for big business Back to Bottom-up Man vs machine Roomba War Machines Google’s Speech Recognition Dance Bots Man vs machine 21st century Are machine intelligent now?
- 7. “Gives computers the ability to learn without being explicitly programmed.” MACHINE LEARNING WITH PYTHON | AUG 2017
- 8. MACHINE LEARNING WITH PYTHON | AUG 2017
- 15. def checkColor(img): #lots of code def checkSize(img): #lots of code def checkTexture(img): #lots of code def analyzeShape(img): #lots of code MACHINE LEARNING WITH PYTHON | AUG 2017
- 16. ● Develop systems that can automatically adapt and customize themselves to individual users. ● Discover new knowledge from large databases (data mining) ● Ability to mimic human and replace certain monotonous tasks which require some intelligence. ● Develop systems that are too difficult/expensive to construct manually because they require specific and detailed skills or knowledge tuned to a specific task. MACHINE LEARNING WITH PYTHON | AUG 2017
- 21. ● Supervised Learning ● Unsupervised Learning ● Reinforcement Learning MACHINE LEARNING WITH PYTHON | AUG 2017
- 22. consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. SUPERVISED LEARNING MACHINE LEARNING WITH PYTHON | AUG 2017
- 23. Create a classifier by finding patterns in examples. SUPERVISED LEARNING MACHINE LEARNING WITH PYTHON | AUG 2017 TRAIN CLASSIFIER MAKE PREDICTION COLLECT TRAINING DATA
- 24. SUPERVISED LEARNING MACHINE LEARNING WITH PYTHON | AUG 2017
- 25. do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. UNSUPERVISED LEARNING MACHINE LEARNING WITH PYTHON | AUG 2017
- 26. MACHINE LEARNING WITH PYTHON | AUG 2017
- 27. MACHINE LEARNING WITH PYTHON | AUG 2017
- 28. MACHINE LEARNING WITH PYTHON | AUG 2017 MACHINE LEARNING Q&A
- 29. MACHINE LEARNING WITH PYTHON | AUG 2017 THANK YOU ! :)