Download as PDF, PPTX
![66
Coding Interview
Given a list of numbers and a number k, return whether
any two numbers from the
list add up to k. For example, given [10, 15, 3, 7] and k of
17, return true since 10 + 7 is 17.
Can you do this in one pass?](https://image.slidesharecdn.com/0-190213140028/85/Machine-Learning-Introduction-65-320.jpg)
Uploaded byYounesCharfaoui
Machine Learning Introduction
This document provides an overview of a machine learning beginner course. It introduces the instructor and provides definitions of machine learning from various sources. It then covers the main categories of artificial intelligence and the general hierarchy of computer science and machine learning. The rest of the document outlines the types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It also provides examples of machine learning applications and recommends additional resources for learning machine learning.
More Related Content
Similar to Machine Learning Introduction
Machine Learning Introduction
- 1. Machine Learning BeginnerCourse 1.المقدمة1.Introduction دورةللمبتدئين االلة تعليم
- 2. Hello! I am YounesCharfaoui And I will be you instructor in during this course. You can find me at: mxcsyounes@gmail.com 2
- 3. “ “AI is allabout figuring what to do when you don’t know what to do” - Peter Norvig. 3
- 4. 1. AI Philosophy What isMachine Learning 4
- 5. What is AI? Artificialintelligence is about creating machines capable of solving problems like we do, through reasoning, intuition and creativity, in other words AI is the simulation of intelligent, human-like processes by machines. 5
- 6.
- 7.
- 8.
- 9. Machine Learning is NotThat !, because most of newcomers thing it like that. 9
- 10. Machine Learning “Learning isany process by which a system improves performance from experience.” - Herbert Simon (1978) 10
- 11. Machine Learning “Machine Learningis the study of algorithms that • Improve their performance P • At some task T • With experience E. A well-defined learning task is given by <P, T, E>.” - Tom Mitchell (1998) 11
- 12. Machine Learning “Machine Learningis The Ability to machines to learn from the past experience, i.e. to learn from the data some correlation that help for taking a decision or doing predictions latter. ” - Younes Charfaoui (2018) 12
- 13.
- 14.
- 15. In General 15 In generalML, requires mathematical concepts and computer science concepts in order to get most of it.
- 16. How to ML? What are the tool and the concepts need to make a good career in ML ? 16
- 17. How to ML? 17
- 18. How to ML? 18
- 19.
- 20. Autonomous Cars 21 We canteach a car to identify object on the road such us cars, persons, street lane, etc..
- 21. Autonomous Cars Technology 22 Thebest example is Stanley car in 2003 !!
- 22. Robotics 23 From self-driving carsto auto-navigating drones, AI & ML extends into many parts of robotics and transportation. The ultimate aim is to use location and sensor data to create vehicles capable of operating without human input.
- 23. Robotics 24 Always in Agriculture, Robotsare trained to Crop picking !!
- 24. Robotics 25 Robots are alsoplaying great role in healthcare section !!
- 25. Image Recognition 26 The AmazingDog face, ML to identify faces and then add in image of ears and noses in the correct places, that how money is made !!
- 26. Image Recognition 27 ML isused to track the neighboring object around self driving car !
- 27. Image Recognition 28 ML isused in Agriculture, Crop Monitoring !! Really amazing.
- 28. Image Recognition 29 To identifyand recognize persons and objects from images, really this can be hard !!
- 29. Image Recognition Positions 30 Evenharder !!
- 30. 31 Even more when races jointhe party of computer vision!!
- 31.
- 32. 33 And of course thehandy player, occlusion !!
- 33. You are joking! 34
- 34. Hell no ! 35 It’sa lion pretending a cat -_- !!
- 35.
- 36. And what aboutthis ? 37
- 37. Natural Language Processing 38 Theuse of assistants and smart home can’t be accomplished without NLP, Famous examples of NLP voice control include Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana.
- 38. Natural Language Processing 39 Translationis another side where ML & NLP played a great ball in it.
- 39. Natural Language Processing 40 Besideusing NLP & ML into reading texts, some government are using in in Legal Document Processing.
- 40. Natural Language Processing 41 Evenmore when ML & CV & NLP are joined together to create this awesome canvas
- 41.
- 42. Healthcare 43 With CV andML, diagnosis are made easily and accurately better than expert doctors.
- 43.
- 44. Gaming 45 And finally Thegaming section, where ML is also showing great results.
- 45.
- 46.
- 47. Overview of MLTypes 48 Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning
- 48. Supervised Learning 49 Supervised learningconcentrates on learning patterns through connecting the relationship between variables and known outcomes and working with labeled datasets. Supervised learning works by feeding the machine sample data with various features (represented as “X”) and the correct value output of the data (represented as “y”).
- 49. Supervised Learning 50 1 23 4 5 6 7 10 1 4 9 16 25 36 ? ? the training data you feed to the algorithm includes the desired solutions, called labels
- 50. Unsupervised Learning 51 The trainingdata does not include Targets here so we don’t tell the system where to go , the system has to understand itself from the data we give, training data (without desired outputs)
- 51. Reinforcement Learning 52 Reinforcement Learningis a very different beast. The learning system, called an agent in this context, can observe the environment, select and perform actions, and get rewards in return (or penalties in the form of negative rewards)
- 52.
- 53.
- 54. 55 Artificial Intelligence A ModernApproach Stuart J. Russell and Peter Norvig
- 55. 56 Oliver Theobald Machine LearningFor Absolute Beginners
- 56. 57 Shai Shalev-Shwartz &Shai Ben-David Understanding Machine learning
- 57. 58 Aurélien Géron Hands-On MachineLearning with Scikit-Learn and TensorFlow
- 58. 59 Yuxi (Hayden) Liu PythonMachine Learning By Example
- 59. 60 Gabriel Cánepa What YouNeed to Know about Machine Learning
- 60. 61 Mastering Machine Learning withPython in Six Steps Manohar Swamynathan
- 61. 62 Advanced Machine Learning withPython John Hearty
- 62. 63 • Analytics VidhyaBlogs • Towards Data Science • Sebastian Ruder • Colah Medium Articles/ Blogs Read as much as you can medium blogs and some bloggers in ML community write very brilliant articles its strengthens your concepts and you see this of from other perspectives and gain more deeper insights of the concepts
- 63. 64 YouTube Must subscribe tothis Youtubers 1. Siraj Raval 2. Arxiv Insight 3. Sentdex 4. Two Minute Papers 5. Deep Lizard
- 64. 65 Community • Wizards (Slackchannel by Siraj Raval) • Analytics Vidhya Community(Slack Channel) • Reddit (/MachineLearning) • Kaggle Discussions Forums • Fast.ai Forums • github.com/topics/machine-learning
- 65. 66 Coding Interview Given alist of numbers and a number k, return whether any two numbers from the list add up to k. For example, given [10, 15, 3, 7] and k of 17, return true since 10 + 7 is 17. Can you do this in one pass?
- 66.