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Practical Data Analysis and Visualization with Python

Data Analytics, Visualization and Data Science for Everyone. Build Machine Learning Models to Solve Day-to-Day Problems
Course from Udemy
 292 students enrolled
 en
Understand the logic behind machine learning models without strain
Build forecasting models with machine learning
Analyze customer satisfaction
Analyze bank statements
Classify images
Learn how to preprocess data
Develop classification models
Build Fraud Detection models
Logistic Regression models
k-nearest neighbor models
Random Forest Models
Support Vector Machines
Learn NumPy package
Learn Pandas package
Learn Scikit-learn
Filter and Slice datasets
Visualize data with Matplotlib
Visualize data with Seaborn
Get comfortable creating Pie charts, Donut charts, Bar charts, Line charts, Scatter plots and more
Read data from Google Sheets
Splitting data into training and test sets
Classify objects with Naive Bayes
Develop supervised learning models
Linear Regression models

The main objective of this course is to make you feel comfortable analyzing, visualizing data and building machine learning models in python to solve various problems. 

This course does not require you to know math or statistics in anyway, as you will learn the logic behind every single model on an intuition level. Yawning students is not even in the list of last objectives. 

Throughout the course you will gain all the necessary tools and knowledge to build proper forecast models. And proper models can be accomplished only if you normalize data. In view of that, there is a dedicated class that will guide you on how to avoid Garbage-In, Garbage-Out and feed the right data, which most courses skip for some reason.

Sample Datasets Used in This Course

  1. Weed Price
  2. Chopstick size and pitching efficiency
  3. Computer prices
  4. Baby Growth
  5. Unemployment Rate and Interest Rates
  6. US Spending on Science and Suicide by Hanging
  7. World Religions
  8. Divorce Statistics by Gender
  9. US Music Sales By Genre
  10. Bank Statement
  11. Customer Satisfaction Poll
  12. Boston House Prices
  13. Historical Speed Limits
  14. Iris flower dataset
  15. Handwritten digits dataset
  16. NYSE Sales Volume for 2016 and 2017

Required Python Packages for This Course

  1. Python 3.4 and above
  2. NumPy
  3. Pandas
  4. Scipy
  5. Scikit-learn
  6. Matplotlib
  7. Seaborn
Practical Data Analysis and Visualization with Python
$ 94.99
per course
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