From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase

Introduction 2 lectures 06:36 You, This Course and Us We - the course instructors - start with introductions. We are a team that has studied at Stanford, IIT Madras, IIM Ahmedabad and spent several years working in top tech companies, including Google and Flipkart. Next, we talk about the target audience for this course: Analytics professionals, modelers and big data professionals certainly, but also Engineers, Product managers, Tech Executives and Investors, or anyone who has some curiosity about machine learning. If Machine Learning is a car, this class will teach you how to drive. By the end of this class, students will be able to: spot situations where machine learning can be used, and deploy the appropriate solutions. Product managers and executives will learn enough of the 'how' to be able intelligently converse with their data science counterparts, without being constrained by it. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. A sneak peek at what's coming up This course is both broad and deep. It covers several different types of machine learning problems, their solutions and shows you how to practically apply them using Python. Introduction 2 lectures 06:36 You, This Course and Us We - the course instructors - start with introductions. We are a team that has studied at Stanford, IIT Madras, IIM Ahmedabad and spent several years working in top tech companies, including Google and Flipkart. Next, we talk about the target audience for this course: Analytics professionals, modelers and big data professionals certainly, but also Engineers, Product managers, Tech Executives and Investors, or anyone who has some curiosity about machine learning. If Machine Learning is a car, this class will teach you how to drive. By the end of this class, students will be able to: spot situations where machine learning can be used, and deploy the appropriate solutions. Product managers and executives will learn enough of the 'how' to be able intelligently converse with their data science counterparts, without being constrained by it. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. A sneak peek at what's coming up This course is both broad and deep. It covers several different types of machine learning problems, their solutions and shows you how to practically apply them using Python. You, This Course and Us We - the course instructors - start with introductions. We are a team that has studied at Stanford, IIT Madras, IIM Ahmedabad and spent several years working in top tech companies, including Google and Flipkart. Next, we talk about the target audience for this course: Analytics professionals, modelers and big data professionals certainly, but also Engineers, Product managers, Tech Executives and Investors, or anyone who has some curiosity about machine learning. If Machine Learning is a car, this class will teach you how to drive. By the end of this class, students will be able to: spot situations where machine learning can be used, and deploy the appropriate solutions. Product managers and executives will learn enough of the 'how' to be able intelligently converse with their data science counterparts, without being constrained by it. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. You, This Course and Us We - the course instructors - start with introductions. We are a team that has studied at Stanford, IIT Madras, IIM Ahmedabad and spent several years working in top tech companies, including Google and Flipkart. Next, we talk about the target audience for this course: Analytics professionals, modelers and big data professionals certainly, but also Engineers, Product managers, Tech Executives and Investors, or anyone who has some curiosity about machine learning. If Machine Learning is a car, this class will teach you how to drive. By the end of this class, students will be able to: spot situations where machine learning can be used, and deploy the appropriate solutions. Product managers and executives will learn enough of the 'how' to be able intelligently converse with their data science counterparts, without being constrained by it. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. You, This Course and Us We - the course instructors - start with introductions. We are a team that has studied at Stanford, IIT Madras, IIM Ahmedabad and spent several years working in top tech companies, including Google and Flipkart. Next, we talk about the target audience for this course: Analytics professionals, modelers and big data professionals certainly, but also Engineers, Product managers, Tech Executives and Investors, or anyone who has some curiosity about machine learning. If Machine Learning is a car, this class will teach you how to drive. By the end of this class, students will be able to: spot situations where machine learning can be used, and deploy the appropriate solutions. Product managers and executives will learn enough of the 'how' to be able intelligently converse with their data science counterparts, without being constrained by it. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. You, This Course and Us We - the course instructors - start with introductions. We are a team that has studied at Stanford, IIT Madras, IIM Ahmedabad and spent several years working in top tech companies, including Google and Flipkart. Next, we talk about the target audience for this course: Analytics professionals, modelers and big data professionals certainly, but also Engineers, Product managers, Tech Executives and Investors, or anyone who has some curiosity about machine learning. If Machine Learning is a car, this class will teach you how to drive. By the end of this class, students will be able to: spot situations where machine learning can be used, and deploy the appropriate solutions. Product managers and executives will learn enough of the 'how' to be able intelligently converse with their data science counterparts, without being constrained by it. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. We - the course instructors - start with introductions. We are a team that has studied at Stanford, IIT Madras, IIM Ahmedabad and spent several years working in top tech companies, including Google and Flipkart. Next, we talk about the target audience for this course: Analytics professionals, modelers and big data professionals certainly, but also Engineers, Product managers, Tech Executives and Investors, or anyone who has some curiosity about machine learning. If Machine Learning is a car, this class will teach you how to drive. By the end of this class, students will be able to: spot situations where machine learning can be used, and deploy the appropriate solutions. Product managers and executives will learn enough of the 'how' to be able intelligently converse with their data science counterparts, without being constrained by it. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. We - the course instructors - start with introductions. We are a team that has studied at Stanford, IIT Madras, IIM Ahmedabad and spent several years working in top tech companies, including Google and Flipkart. Next, we talk about the target audience for this course: Analytics professionals, modelers and big data professionals certainly, but also Engineers, Product managers, Tech Executives and Investors, or anyone who has some curiosity about machine learning. If Machine Learning is a car, this class will teach you how to drive. By the end of this class, students will be able to: spot situations where machine learning can be used, and deploy the appropriate solutions. Product managers and executives will learn enough of the 'how' to be able intelligently converse with their data science counterparts, without being constrained by it. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. A sneak peek at what's coming up This course is both broad and deep. It covers several different types of machine learning problems, their solutions and shows you how to practically apply them using Python. A sneak peek at what's coming up This course is both broad and deep. It covers several different types of machine learning problems, their solutions and shows you how to practically apply them using Python. A sneak peek at what's coming up This course is both broad and deep. It covers several different types of machine learning problems, their solutions and shows you how to practically apply them using Python. A sneak peek at what's coming up This course is both broad and deep. It covers several different types of machine learning problems, their solutions and shows you how to practically apply them using Python. This course is both broad and deep. It covers several different types of machine learning problems, their solutions and shows you how to practically apply them using Python. This course is both broad and deep. It covers several different types of machine learning problems, their solutions and shows you how to practically apply them using Python. Jump right in : Machine learning for Spam detection 5 lectures 42:21 Solving problems with computers There are different approaches to using computers to solve problems. We'll compare and contrast those approaches in this section Machine Learning: Why should you jump on the bandwagon? Machine learning is quite the buzzword these days. While it's been around for a long time, today its applications are wide and far-reaching - from computer science to social science, quant trading and even genetics. From the outside, it seems like a very abstract science that is heavy on the math and tough to visualize. But it is not at all rocket science. Machine learning is like any other science - if you approach it from first principles and visualize what is happening, you will find that it is not that hard. So, let's get right into it, we will take an example and see what Machine learning is and why it is so useful. Plunging In - Machine Learning Approaches to Spam Detection Machine learning usually involves a lot of terms that sound really obscure. We'll see a real life implementation of a machine learning algorithm (Naive Bayes) and by end of it you should be able to speak some of the language of ML with confidence. Spam Detection with Machine Learning Continued We have gotten our feet wet and seen the implementation of one ML solution to spam detection - let's venture a little further and see some other ways to solve the same problem. We'll see how K-Nearest Neighbors and Support Vector machines can be used to solve spam detection. Get the Lay of the Land : Types of Machine Learning Problems So far we have been slowly getting comfortable with machine learning - we took one example and saw a few different approaches. That was just the the tip of the iceberg - this class is an aerial maneuver, we will scout ahead and see what are the different classes of problems that Machine Learning can solve and that we will cover in this class. Jump right in : Machine learning for Spam detection. 5 lectures 42:21 Solving problems with computers There are different approaches to using computers to solve problems. We'll compare and contrast those approaches in this section Machine Learning: Why should you jump on the bandwagon? Machine learning is quite the buzzword these days. While it's been around for a long time, today its applications are wide and far-reaching - from computer science to social science, quant trading and even genetics. From the outside, it seems like a very abstract science that is heavy on the math and tough to visualize. But it is not at all rocket science. Machine learning is like any other science - if you approach it from first principles and visualize what is happening, you will find that it is not that hard. So, let's get right into it, we will take an example and see what Machine learning is and why it is so useful. Plunging In - Machine Learning Approaches to Spam Detection Machine learning usually involves a lot of terms that sound really obscure. We'll see a real life implementation of a machine learning algorithm (Naive Bayes) and by end of it you should be able to speak some of the language of ML with confidence. Spam Detection with Machine Learning Continued We have gotten our feet wet and seen the implementation of one ML solution to spam detection - let's venture a little further and see some other ways to solve the same problem. We'll see how K-Nearest Neighbors and Support Vector machines can be used to solve spam detection. Get the Lay of the Land : Types of Machine Learning Problems So far we have been slowly getting comfortable with machine learning - we took one example and saw a few different approaches. That was just the the tip of the iceberg - this class is an aerial maneuver, we will scout ahead and see what are the different classes of problems that Machine Learning can solve and that we will cover in this class. Solving problems with computers There are different approaches to using computers to solve problems. We'll compare and contrast those approaches in this section Solving problems with computers There are different approaches to using computers to solve problems. We'll compare and contrast those approaches in this section Solving problems with computers There are different approaches to using computers to solve problems. We'll compare and contrast those approaches in this section Solving problems with computers There are different approaches to using computers to solve problems. We'll compare and contrast those approaches in this section There are different approaches to using computers to solve problems. We'll compare and contrast those approaches in this section There are different approaches to using computers to solve problems. We'll compare and contrast those approaches in this section Machine Learning: Why should you jump on the bandwagon? Machine learning is quite the buzzword these days. While it's been around for a long time, today its applications are wide and far-reaching - from computer science to social science, quant trading and even genetics. From the outside, it seems like a very abstract science that is heavy on the math and tough to visualize. But it is not at all rocket science. Machine learning is like any other science - if you approach it from first principles and visualize what is happening, you will find that it is not that hard. So, let's get right into it, we will take an example and see what Machine learning is and why it is so useful tificial Neural Networks ...