Autoplay
Autocomplete
Previous Lesson
Complete and Continue
AI: Data Mining with Python
Section 1 Introduction to Data Mining
Lecture 1 Welcome (2:29)
Lecture 2 Brief Introduction (4:33)
Lecture 3 Data Mining Basic Concepts and Applications (6:52)
Section 2 Setting Up the Data Mining Python Packages Environment
Lecture 4 Why Python (3:25)
Lecture 5 Basics of Python (5:43)
Lecture 6 Installing IPython (2:01)
Lecture 7 Installing the Numpy Library (4:25)
Lecture 8 Installing the pandas Library (5:25)
Lecture 9 Installing Matplotlib (2:34)
Lecture 10 Installing scikit-learn (2:24)
Section 3 Cleaning Data and Preprocessing Techniques
Lecture 11 Data Cleaning (5:30)
Lecture 12 Data Preprocessing Techniques (5:07)
Section 4 Linear Regression Model
Lecture 13 Linear Regression Basic Model Approach (7:57)
Lecture 14 Evaluating Regression Models (5:25)
Lecture 15 Basic Regression Model Implementation to Predict House Prices (9:07)
Lecture 16 Regression Model Implementation to Predict Television Show Viewers (9:45)
Section 5 Classification Concepts and Summary
Lecture 17 Logistic Regression (3:50)
Lecture 18 K – Nearest Neighbors Classifier (5:40)
Lecture 19 Support Vector Machine (5:33)
Lecture 20 Logistic Regression Model Implementation (10:31)
Lecture 21 K – Nearest Neighbor Classifier Implementation (10:45)
Working Files
Working Files
Lecture 16 Regression Model Implementation to Predict Television Show Viewers
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock