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It's continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.
It runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicates that it is the " Next Big Thing " and a must for Professionals in the Data Analytics domain.
Watch Lesson 1 (Recorded)
Module 1: Introduction to Python
It was a nice introduction class with lot of real life problem statement as example.Nice presentation of detailed data wrangling. We need some basic videos regarding the statistics and the support team can help on this regard.
Before enrolling to the course, we can ask 'n' number of doubts & I ensure you will get apt answers from the staff. Another thing which I liked about Edureka is, the batches are available in very convenient time slots. For Example : If one can devote daily 1-2 hours, can go for a daily batch. Never the less, professionals can go for a weekend batch. Also trainers are with excellent background from industry & contain good amount of experience. All the queries & doubts asked during as well as after the session are clarified, which helps to understand the concept in much deeper way.
My experience with edureka about Python is much impressed, with such quality of the training. We get access to each day class recorded sessions after the live class. Edureka is very quick reactive towards the queries of each single user, they answer and follow-up to ensure you get the correct response to resolve your query. Online recording of the course is very useful as you can go back and refer at any time. You have access to course and materials for ever, which is really helpful. More over 24/7 quality support, which is very much important to any of us. I must thank to Edureka for giving such support. Thanks guys, cheerÃ¢â¬Â¦
Python & Kafka was excellent by Gyanender Verma. All the concepts was covered with no compromise. Instructor was very well determined and Focussed with Clear Examples. This reduces my effort of reading the books and can start working immediately in the ongoing projects.
I would like to recommend any one who wants to be a Data Scientist just one place: Edureka. Explanations are clean, clear, easy to understand. Their support team works very well such any time you have an issue they reply and help you solving the issue. I took the Data Science course and I'm going to take Machine Learning with Mahout and then Big Data and Hadoop and after that since I'm still hungry I will take the Python class and so on because for me Edureka is the place to learn, people are really kind, every question receives the right answer. Thank you Edureka to make me a Data Scientist.
I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the customer of Edureka!". Instructors are knowledgeable and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry.
Industry: Social Media
Problem Statement: You as ML expert have to do analysis and modeling to predict the number of shares of an article given the input parameters.
Actions to be performed:
Load the corresponding dataset. Perform data wrangling, visualization of the data and detect the outliers, if any. Use the plotly library in Python to draw useful insights out of data. Perform regression modeling on the dataset as well as decision tree regressor to achieve your Learning Objectives. Also, use scaling processes, PCA along with boosting techniques to optimize your model to the fullest.
Problem Statement: You as an ML expert have to cluster the countries based on various sales data provided to you across years.
Actions to be performed:
You have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the final solution. But before that, you have to bring the exports (in tons) of all countries down to the same scale across years. Plus, as this solution needs to be repeatable you will have to do PCA so as to get the principal components which explain the max variance.
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