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Statistics & Machine Learning Techniques For Regression Analysis With Python
Introduction to the Data Science in Python Bootcamp
Welcome to the Course (1:40)
Data and Scripts for the Course
Introduction to the Python Data Science Tool (10:57)
For Mac Users (4:05)
Introduction to the Python Data Science Environment (19:15)
Some Miscellaneous IPython Usage Facts (5:25)
Online iPython Interpreter (3:26)
Conclusion to Section 1 (2:36)
Introduction to Pandas
What are Pandas? (12:06)
Read CSV Data in Python (5:42)
Read in Excel File (5:31)
Read HTML Data (12:06)
Read JSON Data (9:14)
Conclusions to Section 4 (2:06)
Data Pre-Processing/Wrangling
Remove NA Values (10:28)
Basic Data Handling: Starting with Conditional Data Selection (5:24)
Basic Data Grouping Based on Qualitative Attributes (9:47)
Rank and Sort Data (8:03)
Concatenate (8:16)
Merge (10:47)
Basic Statistical Data Analysis
What is Statistical Data Analysis? (10:08)
Some Pointers on Collecting Data for Statistical Studies (8:38)
Explore the Quantitative Data: Descriptive Statistics (9:05)
Group By Qualitative Categories (10:25)
Visualize Descriptive Statistics-Boxplots (5:28)
Common Terms Relating to Descriptive Statistics (5:15)
Data Distribution- Normal Distribution (4:07)
Check for Normal Distribution (6:23)
Standard Normal Distribution and Z-scores (4:10)
Confidence Interval-Theory (6:06)
Confidence Interval-Calculation (5:20)
Regression Modelling for Defining Relationship bw Variables
Explore the Relationship Between Two Quantitative Variables (4:26)
Correlation Analysis (8:26)
Linear Regression-Theory (10:44)
Linear Regression-Implementation in Python (11:18)
Conditions of Linear Regression-Check in Python (12:03)
Polynomial Regression (3:53)
GLM: Generalized Linear Model (5:25)
Logistic Regression (11:10)
Machine Learning for Data Science
How is Machine Learning Different from Statistical Data Analysis? (5:36)
What is Machine Learning (ML) About? Some Theoretical Pointers (5:32)
Machine Learning Based Regression Modelling
What is this section about (10:10)
Data Preparation for Supervised Learning (9:47)
Pointers on Evaluating the Accuracy of Classification and Regression Modelling (9:42)
Random Forest (RF) Regression (9:20)
Support Vector Regression (4:30)
kNN Regression (3:48)
Gradient Boosting-regression (4:46)
Theory Behind ANN and DNN (9:17)
Regression with MLP (3:48)
Theory Behind ANN and DNN
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