Practical Data Analysis and Visualization with Python
Practical Data Analysis and Visualization with Python, available at $19.99, has an average rating of 4, with 42 lectures, based on 55 reviews, and has 301 subscribers.
You will learn about 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 This course is ideal for individuals who are Anyone who wants to learn data analysis and visualization or Anyone how has a will and / or a need for data analysis or Anyone who wants to build machine learning models to solve day-to-day problems It is particularly useful for Anyone who wants to learn data analysis and visualization or Anyone how has a will and / or a need for data analysis or Anyone who wants to build machine learning models to solve day-to-day problems.
Enroll now: Practical Data Analysis and Visualization with Python
Summary
Title: Practical Data Analysis and Visualization with Python
Price: $19.99
Average Rating: 4
Number of Lectures: 42
Number of Published Lectures: 42
Number of Curriculum Items: 42
Number of Published Curriculum Objects: 42
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
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
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Instructor's message
Lecture 3: Setup working environment
Chapter 2: NumPy
Lecture 1: Section Overview
Lecture 2: Create arrays and matrices
Lecture 3: Arithmetic operations with NumPy
Lecture 4: Slicing and filtering matrices
Lecture 5: Section recap
Chapter 3: Series
Lecture 1: Section overview
Lecture 2: Create and update Series
Lecture 3: Slice and filtere Series
Lecture 4: Arithmetic operations with Series
Chapter 4: DataFrames
Lecture 1: Section Overview
Lecture 2: Create DataFames
Lecture 3: Slicing and filtering DataFrames
Lecture 4: Updating and merging DataFrames
Lecture 5: Apply and Map functions
Lecture 6: Read from Google Spreadsheet
Chapter 5: Data Visualization
Lecture 1: Section overview
Lecture 2: Line charts
Lecture 3: Scatter plots
Lecture 4: Pie and Donut charts
Lecture 5: Bar and Column charts
Lecture 6: Histograms and KDE
Lecture 7: Multiple plots with subplot2grid
Chapter 6: Practical Data Analysis
Lecture 1: Bank statement analysis
Lecture 2: Customer satisfaction analysis
Chapter 7: Machine Learning
Lecture 1: Data Preprocessing
Lecture 2: Split data into training and testing sets
Lecture 3: Linear regression: Introduction
Lecture 4: Linear regression: Predict House Prices
Lecture 5: Linear regression: Improve model's accuracy
Lecture 6: Logistic Regression: Introduction
Lecture 7: Logistic Regression: Predict Gender
Lecture 8: Logistic Regression: Improve model accuracy
Lecture 9: Multi-class classification: Binary and Multinomial Regression
Lecture 10: Multi-class classification: Neighbors (k-Nearest and Radius)
Lecture 11: Multi-class classification: SVM
Lecture 12: Multi-class classification: Naive Bayes
Lecture 13: Random Forest and Decision Tree
Lecture 14: Classify images with different models
Lecture 15: IsolationForest: Fraud Detection
Instructors
-
Bekzod Ruzmetov
PMP
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 4 votes
- 3 stars: 10 votes
- 4 stars: 15 votes
- 5 stars: 25 votes
Frequently Asked Questions
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