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Practical Data Analysis and Visualization with Python

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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

  • 4.6 (23 ratings)
  • Created by Bekzod Ruzmetov
  •  English
  • 4.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I Learn?

  • 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

Description
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

Who is the target audience?

  • Anyone who wants to learn data analysis and visualization
  • Anyone how has a will and / or a need for data analysis
  • Anyone who wants to build machine learning models to solve day-to-day problems
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