Practical Data Analysis and Visualization with Python

Rating 3.8 out of 5 (54 ratings in Udemy)

What you'll 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 …

Rating 3.8 out of 5 (54 ratings in Udemy)

What you'll 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

Weed Price
Chopstick size and pitching efficiency
Computer prices
Baby Growth
Unemployment Rate and Interest Rates
US Spending on Science and Suicide by Hanging
World Religions
Divorce Statistics by Gender
US Music Sales By Genre
Bank Statement
Customer Satisfaction Poll
Boston House Prices
Historical Speed Limits
Iris flower dataset
Handwritten digits dataset
NYSE Sales Volume for 2016 and 2017

Required Python Packages for This Course

Python 3.4 and above
NumPy
Pandas
Scipy
Scikit-learn
Matplotlib
Seaborn

Duration 4 Hours 58 Minutes

Paid

Self paced

All Levels

English (US)

296

Rating 3.8 out of 5 (54 ratings in Udemy)

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