In the previous tutorial, we reviewed some basic concepts in probability. In this tutorial, we are going to talk about probability distributions and random variables. We will also discuss some probability distributions with commonly appear in real-world datasets and problems.
Probability Distributions and Random variables
Those variables which can take different values randomly are called random variables. If the the variables are discrete in nature, they are called discrete random variables. For instance, the number of heads that might occur in a series of coin tosses (let’s say 15 coin toss) is a discrete random variable. This number can take any whole number value in the range 0 to 15. Similarly, if the variables are continuous in nature, then it is called continuous random variable. For example, the time taken by a radioactive particle to decay is a continuous random variable as it can take infinite number of possible...
We want the lexicographically largest possible string C as possible. C consists of 4's and 7's. We can rearrange digits of numbers A and B as we want. What does all of this imply in terms of digits of C?
It implies that we can always ensure all the 7's in C appear first, and then all the 4's.
Given this, what does the problem reduce to?
The problem reduces the maximizing the number of 7's in C. And then the number of 4's.
How do we achieve that?
Be greedy in arranging the digits of A and B such that we get as many 7's as possible. Then be greedy in arranging the digits of A and B such that we get as many 4's as possible.
We first introduce TF-IDF (term frequency, inverse document frequency), a very commonly used measure in NLP to weigh the importance of different words. This helps us in search engine ranking (also called document retrieval), finding similar or related documents, and so on.
Text Classification and Sentiment Analysis
Then we move on to text classification (separating different types of documents into predefined categories), and use the learned concepts to implement sentiment classification from scratch.
In the previous course, Foundations of Machine Learning you already learnt about the core machine learning concepts as well as the linear regression and gradient descent algorithms. In this course, you will learn various other machine learning algorithms, both for supervised learning as well as unsupervised learning.
This comprehensive Data Science Career Path takes you from being a complete beginner all the way to a data scientist. After completing this path, you'll be ready to analyze real-world datasets in Python and deliver valuable insights. All the required background, such as Python, linear algebra, probability, etc is included in this path.
Key Features of the Career Path
10+ portfolio projects and 300+ exercises to give you a lot of practice and build fluency.
Most of the tutorials are available in three different formats — video, long article and bite-sized cards — so you can learn the way that works best for you.
Articles and videos have code execution built-in. You can play the instructor's code right inside the video!
Exceptional content quality. We teach you the real thing, no dumbing things down or only talking about...
In this course, we will start by learning what machine learning is, and introduce a simple learning algorithm: linear regression + gradient descent. Using this algorithm, we'll introduce the core concepts in machine learning: model parameters, cost function, optimization method, and overfitting and regularization. This course ends with a visual review of these concepts and a tutorial on the different types of machine learning problems.
In this course, we will learn about databases and SQL, used for storing and managing data used in computer systems. We'll also look at map reduce, a programming model that allows us to perform parallel processing on large data sets in a distributed environment. As always, the tutorials will be interleaved with quizzes and hands-on assignments.
This course consists of two end-to-end data science projects. The last tutorial contains a list of 10 project ideas (including datasets and suggested algorithms). It is recommended that you do at-least one end-to-end project as part of the course.