Since the majority of the time of building machine learning models would be spent in data processing, it is important to be familiar to the libraries that can help in processing such data. The properties representing the video, i.e., duration, percentage of viewers watching for more than a minute are called features. Īs can be observed, vectors can be used in Machine Learning to define observations and predictions. This could be represented as the following vector: class_probabilities =. Based on this vector, our Machine Learning system may predict that there is an 80% probability that it is a spam video, 18% that it is clickbait, and 2% that it is a good video. This vector could represent a video that lasts 10.5 minutes, but only 5.2% viewers watch for more than a minute, it gets 3.25 views per day on average, and it was flagged 7 times as spam.Īs you can see, each axis may have a different meaning. For each video, we would have a vector representing what we know about it, such as. Similarly, vectors have several usages in Machine Learning, most notably to represent observations and predictions.įor example, say we built a Machine Learning system to classify videos into 3 categories (good, spam, clickbait) based on what we know about them. The rocket’s velocity may be represented by the following vector: which represents the speed in each of x, y, and z-direction. It can be represented as an array of 2 numbers = where ‘a’ may represent the magnitude of 2 Newton and 180 (‘b’) represents the angle in degrees.Īnother example, say a rocket is going up at a slight angle: it has a vertical speed of 5,000 m/s, and also a slight speed towards the East at 10 m/s, and a slight speed towards the North at 50 m/s. It is defined by the magnitude of force as well as a direction. What are vectors? Vector quantity is the one which is defined by a magnitude and a direction. It can be represented either as a row or as a column array. The session covers these and some important attributes of the NumPy array object in detail.
Shape: returns a tuple of integers indicating the size of the array.Ndim: displays the dimension of the array.Some of the important attributes of a NumPy object are: There are several ways to create an array in NumPy like np.array, np.zeros, no.ones, etc. It is a table with same type elements, i.e, integers or string or characters (homogeneous), usually integers. NumPy’s main object is the homogeneous multidimensional array. Numpy can be imported into the notebook using > import numpy as np NumPy provides the essential multi-dimensional array-oriented computing functionalities designed for high-level mathematical functions and scientific computation. complete the Python Machine Learning Ecosystem. Since, arrays and matrices are an essential part of the Machine Learning ecosystem, NumPy along with Machine Learning modules like Scikit-learn, Pandas, Matplotlib, TensorFlow, etc. It is an open source module of Python which provides fast mathematical computation on arrays and matrices. NumPy stands for ‘Numerical Python’ or ‘Numeric Python’. The participants were from countries namely United States, Canada, Australia, Indonesia, India, Thailand, Philippines, Malaysia, Macao, Japan, Hong Kong, Singapore, United Kingdom, Saudi Arabia, Nepal, & New Zealand. It was attended by more than 100 learners around the globe.
This post is an excerpt from a live hands-on training conducted by CloudxLab on 25th Nov 2017. This similarity and added flexibility have resulted in wide acceptance of python in the scientific community lately.
We will realize the similarity of these libraries with existing toolboxes in R and MATLAB. In this post, we will provide an overview of the common functionalities of NumPy and Pandas. Both NumPy and Pandas have emerged to be essential libraries for any scientific computation, including machine learning, in python due to their intuitive syntax and high-performance matrix computation capabilities. Matrix and vector manipulations are extremely important for scientific computations. Python is increasingly being used as a scientific language.
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