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Course Overview
(1:00)
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01. Introduction to Machine Learning
(14:00)
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Gradient Descent & Cost Function
(12:00)
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Data Preprocessing
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Feature Scaling
13:00
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1. Feature Scaling
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Mobile Price Dataset
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03. Feature Selection
13:00
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2. Feature Selection
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Water Potability Dataset
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04. Encoding Categorical Data
14:00
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3. Encoding Categorical Data
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Banking Dataset
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05. Train Test Split
11:00
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4. Train Test Split
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Regression
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Introduction to Linear Regression
12:00
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Simple Linear Regression
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TV Marketing with Simple Linear Regression
19:00
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TV Marketing Files
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2 Gold Price Prediction Question
2:00
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Gold Price Prediction Files
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Gold Price Submission
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Multiple Linear Regression
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Used Cars Sale Price Prediction
19:00
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Car Price Prediction Files
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Housing Price Prediction Project Question
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Housing Price Prediction Files
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Housing Price Submission
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Regression Quiz
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Classification
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Introduction to Classification Algorithms
13:00
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Logistic Regression
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Introduction to Logistic Regression
5:00
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Titanic Survival Prediction Part 1
2:00
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Titanic Survival Prediction Part 2
21:00
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Titanic Survival Prediction Files
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Advertisement Prediction Project Question
2:00
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Advertising Prediction Files
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Advertising Prediction Submission
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Support Vector Machine
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Introduction to Support Vector Machine
4:00
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Drinking Water Potability Prediction
17:00
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Drinking Water Potability Prediction Files
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Diabetes Prediction Project Question
2:00
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Diabetes Prediction Files
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Diabetes Prediction Submission
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Decision Tree Classifier
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Introduction to Decision Tree Classifier
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Graduate Admission Prediction
15:00
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Graduate Admission Prediction Files
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Bank Marketing Project Question
1:00
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Bank Marketing Files
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Bank Marketing Submission
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Random Forest Classifier
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Introduction to Random Forest Classifier
3:00
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Mobile Price Classification 1
2:00
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Mobile Price Classification 2
10:00
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Mobile Price Classification Files
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Airline Passenger Satisfaction Prediction Question
2:00
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Airline Passengers Satisfaction prediction Files
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Airline Passengers Satisfaction Submission
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K-Nearest Neighbors Classifier
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Introduction to K-Nearest Neighbors
5:00
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Heart Failure Prediction Part 1
1:00
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Heart Failure Prediction Part 2
20:00
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Heart Failure Prediction Files
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Payment Fraud Prediction Question
2:00
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Payment Fraud Detection Files
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Payment Fraud Submission
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Naive Bayes Classifier
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Introduction to Naive Bayes Classifier
7:00
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Glass Classification
10:00
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Glass Classification Files
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Income Prediction Question
2:00
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Income Evaluation Files
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Income Evaluation Submission
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Classification Algorithms Quiz
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Clustering
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Introduction to Clustering
3:00
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K-Means Clustering
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Introduction to K-Means Clustering
9:00
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Mall Customers Clustering Part 1
1:00
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Mall Customers Clustering Part 2
12:00
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Mall Customers Clustering Files
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Credit Card Customer Clustering Question
1:00
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Credit Card Customers Clustering Files
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Credit Card Customers Submission
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Clustering Quiz
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Principle Component Analysis
(7:00)
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Principle Component Analysis (PCA) Notes
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Model Fine Tuning
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K-Fold Cross Validation
10:00
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K-Fold Cross Validation Notes
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Hyper Parameter Tuning
9:00
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Hyper Parameter Tuning(GridSearchCV)
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Save a model to a File
(9:00)
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Saving The Model File
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Model Deployment
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Model Deployment with Tkinter Part 1
9:00
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Model Deployment with Tkinter Part 2
24:00
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ML Model Deployment Files
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Capstone Projects
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Employee Attrition Classification Question
2:00
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Employee Attrition Files
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Employee Attrition Submission
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Customer Behaviour Classification Question
2:00
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Customer Behaviour Classification Files
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Customer Behavior Submission
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Credit Risk Analysis Question
2:00
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Credit Risk Analysis Files
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Credit Risk Analysis Submission
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Machine Learning Capstone Quiz
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