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Machine Learning with Python
  • Level Professional
  • المدة 33 ساعات hours
  • الطبع بواسطة IBM
  • Offered by

عن

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning. This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.

الوحدات

Welcome

    • 1 Videos
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1 Videos

  • Course Introduction

What is Machine Learning?

    • 4 Videos
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4 Videos

  • Welcome
  • Introduction to Machine Learning
  • Python for Machine Learning
  • Supervised vs Unsupervised

End of Module Review & Evaluation

    • 2 Assignment
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2 Assignment

  • Practice Quiz: Intro to Machine Learning
  • Graded Quiz: Intro to Machine Learning

Linear Regression

    • 5 Videos
    • 3 Readings
    • 2 ExternalTool
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2 External Tool

  • Lab: Simple Linear Regression
  • Lab: Multiple Linear Regression

5 Videos

  • Introduction to Regression
  • Simple Linear Regression
  • Model Evaluation in Regression Models
  • Evaluation Metrics in Regression Models
  • Multiple Linear Regression

3 Readings

  • Errata for "Evaluation Matrix in Regression Model" Video
  • Errata: Simple Linear Regression Video
  • Errata: Multiple Linear Regression Video

End of Module Review & Evaluation

    • 2 Assignment
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2 Assignment

  • Practice Quiz: Regression
  • Graded Quiz: Regression

K-Nearest Neighbours

    • 3 Videos
    • 1 ExternalTool
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1 External Tool

  • Lab: KNN

3 Videos

  • Introduction to Classification
  • K-Nearest Neighbours
  • Evaluation Metrics in Classification

Decision Trees

    • 2 Videos
    • 2 ExternalTool
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2 External Tool

  • Lab: Decision Trees
  • (Optional) Lab: Faster Credit Card Fraud Detection using Snap ML

2 Videos

  • Introduction to Decision Trees
  • Building Decision Trees

Regression Trees (optional )

    • 1 Readings
    • 2 ExternalTool
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2 External Tool

  • Lab: Regression Trees
  • (Optional) Lab: Faster Taxi Tip Prediction using Snap ML

1 Readings

  • Regression Trees

End of Module Review & Evaluation

    • 2 Assignment
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2 Assignment

  • Practice Quiz: Classification
  • Graded Quiz: Classification

Logistic Regression

    • 3 Videos
    • 1 ExternalTool
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1 External Tool

  • Lab: Logistic Regression

3 Videos

  • Intro to Logistic Regression
  • Logistic regression vs Linear regression
  • Logistic Regression Training

Support Vector Machine

    • 1 Videos
    • 1 ExternalTool
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1 External Tool

  • Lab: SVM (Support Vector Machines)

1 Videos

  • Support Vector Machine

Multiclass Prediction

    • 1 Readings
    • 1 ExternalTool
    • 1 Plugin
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1 External Tool

  • Lab: Multiclass Prediction

1 Readings

  • Multiclass Prediction

End of Module Review & Evaluation

    • 2 Assignment
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2 Assignment

  • Practice Quiz: Linear Classification
  • Graded Quiz: Linear Classification

k-Means Clustering

    • 3 Videos
    • 1 ExternalTool
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1 External Tool

  • Lab: k-Means

3 Videos

  • Intro to Clustering
  • Intro to k-Means
  • More on k-Means

End of Module Review & Evaluation

    • 2 Assignment
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2 Assignment

  • Practice Quiz: Clustering
  • Graded Quiz: Clustering

Final Exam

    • 1 Assignment
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1 Assignment

  • Final Exam

Congratulations!

    • 1 Readings
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1 Readings

  • Congratulations!

IBM Digital Badge

    • 1 Readings
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1 Readings

  • IBM Digital Badge

Final Project

    • 1 Readings
    • 1 PeerReview
    • 1 ExternalTool
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1 External Tool

  • Lab: Rain Prediction in Australia

1 Peer Review

  • Submit Your Work and Review Your Peers

1 Readings

  • Project Scenario

Auto Summary

Immerse yourself in the world of Machine Learning with Python through this comprehensive course designed for aspiring Data Scientists and AI enthusiasts. Led by expert instructors on Coursera, the course covers supervised and unsupervised learning, key classification and clustering techniques, and hands-on application using SciPy and scikit-learn. With a duration of 1980 minutes, it offers Starter and Professional subscription options. By the end, you'll have job-ready skills and a certificate to showcase your expertise. Perfect for advancing your career in Data Science and AI.

SAEED AGHABOZORGI
Instructors

SAEED AGHABOZORGI

Joseph Santarcangelo
Instructors

Joseph Santarcangelo