Introduction to Data Science and scikit-learn in Python
- Offered byCoursera
Introduction to Data Science and scikit-learn in Python at Coursera Overview
Duration | 14 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Data Science and scikit-learn in Python at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to your schedule. Earn a Certificate upon completion 100% online Start instantly and learn at your own schedule
Learn Employ artificial intelligence techniques to test hypothesis in Python
Introduction to Data Science and scikit-learn in Python at Coursera Course details
- This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis.
- We'll learn some of the most important libraries for exploratory data analysis (EDA) and machine learning such as Numpy, Pandas, and Sci-kit learn.
- After learning some of the theory (and math) behind linear regression, we'll go through and full pipeline of reading data, cleaning it, and applying a regression model to estimate the progression of diabetes.
- By the end of the course, you'll apply a classification model to predict the presence/absence of heart disease from a patient's health data.
Introduction to Data Science and scikit-learn in Python at Coursera Curriculum
Introduction to Python Programming for Hypothesis Testing
Course Introduction
Module Introduction
Python and Jupyter Notebook Basics
Setting Up the Environment
Lists
Dictionaries
Loops
Functions
Libraries and Modules
A Note on Coding Alongside the Videos
Jupyter Notebook Basics
Python Docs: Data Structures
List Comprehensions
Keyword Arguments
Practice Quiz: Data Structures
Introduction to Python
Creating a Hypothesis: Numpy, Pandas, and Scikit-Learn
Module Introduction
Deep Dive into Numpy (Part I)
Deep Dive Into Numpy (Part II)
Introduction to Pandas
Pandas Deep Dive
Joining and Manipulating Dataframes (I)
Joining and Manipulating Dataframes (II)
Joining and Manipulating Dataframes (III)
Numpy, Pandas, and scikit-learn
10 min to Pandas
Pandas Docs: Reshaping and Combining Data
Split-Apply-Combine
Python Docs: Sort_values()
Practice Quiz: Numpy Basics
Practice Quiz: Pandas
Practice Quiz: Combining Data
Numpy and Pandas Quiz
Scikit-Learn Revisited: ML for Hypothesis Testing
Module Introduction
Using the Scikit-Learn Docs
Loading and Analyzing datasets
Applying Linear Regression (I)
Math of Machine Learning
Scikit-Learn Conclusion
Introduction to scikit-learn
Train/Test Split and Cross-Validation
Least Squares
Practice Quiz: Using the scikit-learn Docs
Practice Quiz: Math of Linear Regression
Linear Regression with Scikit-Learn
Using Classification to Predict the Presence of Heart Disease
Predicting the Presence of Heart Disease
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Data ScienceData Science BasicsPython for data scienceIntroduction to Data Science and scikit-learn in Python