About this Course

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Intermediate Level
Approx. 11 hours to complete
English
Subtitles: English

Skills you will gain

Decision TreeEnsemble LearningClassification AlgorithmsSupervised LearningMachine Learning (ML) Algorithms
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 11 hours to complete
English
Subtitles: English

Offered by

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IBM

Syllabus - What you will learn from this course

Week
1

Week 1

2 hours to complete

Logistic Regression

2 hours to complete
10 videos (Total 91 min), 6 readings, 3 quizzes
10 videos
Optional: How to create a project in IBM Watson Studio5m
Introduction: What is Classification?6m
Introduction to Logistic Regression2m
Classification with Logistic Regression12m
Confusion Matrix, Accuracy, Specificity, Precision, and Recall7m
Classification Error Metrics: ROC and Precision-Recall Curves10m
Logistic Regression Lab - Part 113m
Logistic Regression Lab - Part 216m
Logistic Regression Lab - Part 313m
6 readings
About this course3m
Optional: Introduction to IBM Watson Studio4m
Optional: Overview of IBM Watson Studio3m
Optional: Download data assets3m
Logistic Regression Demo (Activity)10m
Summary/Review4m
3 practice exercises
Logistic Regression4m
Logistic Regression Demo2m
End of Module10m
Week
2

Week 2

1 hour to complete

K Nearest Neighbors

1 hour to complete
7 videos (Total 50 min), 2 readings, 3 quizzes
7 videos
K Nearest Neighbors Decision Boundary3m
K Nearest Neighbors Distance Measurement8m
K Nearest Neighbors with Feature Scaling5m
K Nearest Neighbors Notebook - Part 19m
K Nearest Neighbors Notebook - Part 26m
K Nearest Neighbors Notebook - Part 311m
2 readings
K Nearest Neighbors Demo (Activity)3m
Summary/Review1m
3 practice exercises
K Nearest Neighbors3m
N Nearest Neighbors Demo5m
End of Module15m
2 hours to complete

Support Vector Machines

2 hours to complete
11 videos (Total 67 min), 2 readings, 4 quizzes
11 videos
Classification with Support Vector Machines2m
The Support Vector Machines Cost Function5m
Regularization in Support Vector Machines6m
Introduction to Support Vector Machines Gaussian Kernels2m
Support Vector Machines Gaussian Kernels - Part 14m
Support Vector Machines Gaussian Kernels - Part 24m
Implementing Support Vector Machines Kernel Models8m
Support Vector Machines Notebook - Part 18m
Support Vector Machines Notebook - Part 28m
Support Vector Machines Notebook - Part 310m
2 readings
Support Vector Machines Demo (Activity)3m
Summary/Review2m
4 practice exercises
Support Vector Machines5m
Support Vector Machines Kernels3m
Support Vector Machines Demo3m
End of Module10m
Week
3

Week 3

2 hours to complete

Decision Trees

2 hours to complete
8 videos (Total 60 min), 2 readings, 3 quizzes
8 videos
Building a Decision Tree6m
Entropy-based Splitting2m
Other Decision Tree Splitting Criteria4m
Pros and Cons of Decision Trees5m
Decision Trees Notebook - Part 16m
Decision Trees Notebook - Part 28m
Decision Trees Notebook - Part 315m
2 readings
Decision Trees Demo (Activity)10m
Summary/Review3m
3 practice exercises
Decision Trees4m
Decision Trees Demo3m
End of Module10m
2 hours to complete

Ensemble Models

2 hours to complete
15 videos (Total 93 min), 3 readings, 6 quizzes
15 videos
Ensemble Based Methods and Bagging - Part 21m
Ensemble Based Methods and Bagging - Part 33m
Random Forest7m
Bagging Notebook - Part 16m
Bagging Notebook - Part 26m
Bagging Notebook - Part 39m
Review of Bagging4m
Overview of Boosting3m
Adaboost and Gradient Boosting Overview7m
Adaboost and Gradient Boosting Syntax4m
Stacking7m
Boosting Notebook - Part 17m
Boosting Notebook - Part 215m
Boosting Notebook - Part 35m
3 readings
Bagging Demo (Activity)3m
Boosting and Stacking Demo (Activity)3m
Summary/Review10m
6 practice exercises
Bagging5m
Random Forest3m
Bagging Demo3m
Boosting and Stacking5m
Boosting and Stacking Demo5m
End of Module10m
Week
4

Week 4

2 hours to complete

Modeling Unbalanced Classes

2 hours to complete
6 videos (Total 30 min), 1 reading, 3 quizzes
6 videos
Upsampling and Downsampling6m
Modeling Approaches: Weighting and Stratified Sampling3m
Modeling Approaches: Random and Synthetic Oversampling5m
Modeling Approaches: Nearing Neighbor Methods4m
Modeling Approaches: Blagging5m
1 reading
Summary/Review10m
2 practice exercises
Modeling Unbalanced Classes4m
End of Module10m

About the IBM Introduction to Machine Learning Specialization

This specialization will help you realize the potential of machine learning in a business setting. There will be a focus on helping you gain the skills that will help you succeed in a career in machine learning and data science. You will be able to realize the potential of machine learning and artificial intelligence in different business scenarios. You will also be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You will also learn how to evaluate your machine learning models and to incorporate best practices....
IBM Introduction to Machine Learning

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  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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