Text summarization using NLP

Explore python Libraries - Numpy, Scipy, Matplotlib

Numpy Gives functions for working with multidimensional arrays. Numpy arrays have 1)Rank 2)Shape (given by dim of vector) Numpy arrays can be created like: a=np.array([1,2,3],[2,3,4]) #with initialize a=np.zeros(2,2) / np.ones(2,2) / np.random.random(4,4)

Coursera Course 3 Structuring Machine Learning Projects

Week One - Video One - Why ML STrategy Why we should learn care about ML Strategy Here when we try to improve the performance of the system we should consider about a lot of things . They are: -Amount of data - Amount of diverse data - Train algorithm longer with gradient descent -use another optimization algorithm like Adam - use bigger network or smaller network depending out requirement - use drop out - add l2 regularization - network architecture parameters like number of hidden units, Activation function etc. Second Video - Orthogonalization Orthogonalization means in a deep learning network we can change/tune so many things for eg. hyper parameters to get a more performance in the network . So most effective people know what to tune in order to achieve a particular effect. For every set of problem there is a separate solution. Don't mix up the problems and solutions. For that, first we should find out where is the problem , whether it is with training ...

Libraries For ML Projects in Python

Top machine learning libraries for Python 1. Numpy Numerical Python It is the most fundamental package for scientific computing in python. It provides operations for matrix and array. Numpy arrays are used in most of the ML projects. The library provides vectorization of mathematical operations on the NumPy array type 2. Scipy modules for linear algebra, optimization, integration, and statistics. It contains modules for linear algebra, optimization, integration, and statistics. 3. Pandas It works with labelled and relational data. It designed for quick and easy data manipulation, aggregation, and visualization. Here is just a small list of things that you can do with Pandas: Easily delete and add columns from DataFrame Convert data structures to DataFrame objects Handle missing data, represents as NaNs Powerful grouping by functionality 4. Matplotlib Used for generation of simple and powerful visual...

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