Skip to main content

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 visualizations .

With a bit of effort you can make just about any visualizations:

    Line plots;
    Scatter plots;
    Bar charts and Histograms;
    Pie charts;
    Stem plots;
    Contour plots;
    Quiver plots;
    Spectrograms.

5. Seaborn

for visualisation of statistical models

6. Bokeh

Used for interactive visualizations.

7. Plotly

Web based tool for visualizations

8. SciKit-Learn

The scikit-learn exposes a concise and consistent interface to the common machine learning algorithms, making it simple to bring ML into production systems.


9. Theano

 * It defines multi-dimensional arrays
 * And math operations and expressions

10. TensorFlow

11. Keras

12. NLTK

13.Gensim

14. Scrapy

15. Statsmodels




refer for more :  https://medium.com/activewizards-machine-learning-company/top-15-python-libraries-for-data-science-in-in-2017-ab61b4f9b4a7

Comments

  1. Sadhana Rathore3 February 2019 at 21:42

    I gathered lots of information from your blog and it helped me a lot. Keep posting more.
    Python Training in Chennai
    ccna course in Chennai
    R Programming Training in Chennai
    AWS Training in Chennai
    DevOps Training in Chennai
    Angularjs Training in Chennai
    RPA Training in Chennai

    ReplyDelete
  2. Jackie Co Kad29 November 2019 at 22:26

    The development of artificial intelligence (AI) has propelled more programming architects, information scientists, and different experts to investigate the plausibility of a vocation in machine learning. Notwithstanding, a few newcomers will in general spotlight a lot on hypothesis and insufficient on commonsense application. IEEE final year projects on machine learning In case you will succeed, you have to begin building machine learning projects in the near future.

    Projects assist you with improving your applied ML skills rapidly while allowing you to investigate an intriguing point. Furthermore, you can include projects into your portfolio, making it simpler to get a vocation, discover cool profession openings, and Final Year Project Centers in Chennai even arrange a more significant compensation.


    Data analytics is the study of dissecting crude data so as to make decisions about that data. Data analytics advances and procedures are generally utilized in business ventures to empower associations to settle on progressively Python Training in Chennai educated business choices. In the present worldwide commercial center, it isn't sufficient to assemble data and do the math; you should realize how to apply that data to genuine situations such that will affect conduct. In the program you will initially gain proficiency with the specialized skills, including R and Python dialects most usually utilized in data analytics programming and usage; Python Training in Chennai at that point center around the commonsense application, in view of genuine business issues in a scope of industry segments, for example, wellbeing, promoting and account.

    ReplyDelete

Post a Comment

Popular posts from this blog

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 ...
Post a Comment
Read more

Converting DICOM images into JPG Format in Centos

Converting DICOM images into JPG Format in Centos I wanted to work with medical image classification using Deep learning. The Image data set was .dcm format. So to convert the images to jpg format following steps have performed. Used ImageMagick software. http://www.ofzenandcomputing.com/batch-convert-image-formats-imagemagick/ Installed ImageMagick in Centos by downloading the rom and installing its libraries : rpm -Uvh ImageMagick-libs-7.0.7-10.x86_64.rpm rpm -Uvh ImageMagick-7.0.7-10.x86_64.rpm After installation the the image which is to be converted is pointed in directory. Inside the directory executed the command: mogrify -format jpg *.dcm Now dcm image is converted to JPG format. 
Post a Comment
Read more