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Python certification training

Python certification training
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About The Python Certification Training

Python course helps you gain expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers by transforming your career into Data Scientist role. You will use libraries like Pandas, Numpy, Matplotlib, Scipy, Scikit, Pyspark and master the concepts like Python machine learning, scripts, sequence, web scraping and big data analytics leveraging Apache Spark.

 


You will do your Assignments/Case Studies using Jupyter Notebook that is already installed on your Cloud Lab environment whose access details will be available on your LMS. You will be accessing your Cloud Lab environment from a browser. For any doubt, the 24*7 support team will promptly assist you.
CloudLab is a cloud-based Jupyter Notebook which is pre-installed with Python packages on the cloud-lab environment. It is offered by ProICT Training as a part of Python Certification Course where you can execute all the in-class demos and work on real-life projects in a fluent manner.
 
You’ll be able to access the CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support ninja team is ready to assist 24x7.
This course comprises of 40 case studies that will enrich your learning experience. In addition, we also have 4 Projects that will enhance your implementation skills. Below are few case studies, which are part of this course:
 
Case Study 1: Maple Leaves Ltd is a start-up company which makes herbs from different types of plants and its leaves. Currently, the system they use to classify the trees which they import in a batch is quite manual. A laborer from his experience decides the leaf type and subtype of plant family. They have asked us to automate this process and remove any manual intervention from this process.
 
You have to classify the plant leaves by various classifiers from different metrics of the leaves and to choose the best classifier for future reference.
 
 
Case Study 2: BookRent is the largest online and offline book rental chain in India.  The company charges a fixed fee per month plus rental per book. So, the company makes more money when the user rents more books. 
 
You as an ML expert and must model recommendation engine so that user gets a recommendation of books based on the behavior of similar users. This will ensure that users are renting books based on their individual taste. 
The company is still unprofitable and is looking to improve both revenue and profit. Compare the Error using two approaches – User Based Vs  Item Based
 
 
Case Study 3:  Handle missing values and fit a decision tree and compare its accuracy with random forest classifier.
 
Predict the survival of a horse based on various observed medical conditions. Load the data from ‘horses.csv’ and observe whether it contains missing values. Replace the missing values by the most frequent value in each column. Fit a decision tree classifier and observe the accuracy. Fit a random forest classifier and observe the accuracy.
 
Case Study 4:  Principal component analysis using sci-kit learn.
             
Load the digits dataset from sklearn and write a helper function to plot the image. Fit a logistic regression model and observe the accuracy.
Using scikit learn to perform a PCA transformation such that the transformed dataset can explain 95% of the variance in the original dataset. Compare it with a model and also comment on the accuracy. Compute the confusion matrix and count the number of instances that have gone wrong. For each of the wrong sample, plot the digit along with the predicted and original label.
 
Case Study 5:  Read the data file “letterCG.data” and set all the numerical attributes as features. Split the data in to train and test sets. 
 
Fit a sequence of AdaBoost classifier with varying number of weak learners ranging from 1 to 16, keeping the max_depth as 1. Plot the accuracy on the test set against the number of weak learners, using a decision tree classifier as the base classifier.
 

Project #1:

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.

 

Project #2: 

Industry: FMCG

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.

How soon after Signing up would I get access to the Learning Content?
As soon as you enroll in the course, your LMS (The Learning Management System) access will be functional. You will immediately get access to our course content in the form of a complete set of Videos, PPTs, PDFs, and Assignments. You can start learning right away.

What are the payment options?
For USD payment, you can pay by Paypal. 

What if I have more queries?
You can give us a CALL at +1 718-715-0914