Machine Learning with Python

BY
IBM via Coursera

Master key skills in Machine Learning using the highly-popular Python programming language by enrolling for Machine Learning with Python offered by IBM.

Lavel

Intermediate

Mode

Online

Duration

2 Weeks

Fees

Free

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based
Learning efforts 10 Hours Per Week

Course overview

The Machine Learning with Python certification course offered by IBM is a comprehensive program designed to introduce learners to the basics of machine learning using the Python programming language. Machine Learning with Python programme offered by IBM is the 13 hours (approx.) duration certification course having 6 moudules in total where students can learn on their own pace.

The Machine Learning with Python  certification course, offered by IBM via Coursera, is the intermediate level programme where students will learn 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.  Machine Learning with Python certification course offered by IBM helps the students to learn new concepts from industry experts and develop job-relevant skills with hands-on projects along with shareable career certificate from IBM.

The highlights

  • Offered by IBM
  • 100% online mode of learning
  • Learning with IBM instructors
  • Approximately 20 hours of videos and readings
  • Flexible deadlines in accordance with your schedule
  • Shareable certificate for online profile/CV
  • IBM Digital Badge upon completion

Program offerings

  • Audio-visual
  • Course resources and readings
  • Shareable certificate
  • Interactive quizzes

Course and certificate fees

Type of course

Free

The fees for the course Machine Learning with Python is -

HeadAmount
1 month (20+ hours/week)
Rs. 1,676
3 month (14 hours/week)
Rs. 3,369
6 month (7 hours/week)
Rs. 5,029

 

certificate availability

Yes

certificate providing authority

Coursera +1 more

What you will learn

Machine learning Knowledge of python Knowledge of scikit-learn

Upon completion of Machine Learning with Python certification course offered by IBM , the participants will be able to learn about the description of the various types of Machine Learning algorithms and when to use them  and comparing and contrasting linear classification methods, including multiclass prediction, support vector machines, and logistic regression.

Students will also get knowldege about how to write Python code that implements various classification techniques, including K-Nearest neighbors (KNN), decision trees, and regression trees and to evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics after completing Machine Learning with Python certification course offered by IBM via Coursera.

The syllabus

Module 1: Introduction to Machine Learning

Videos
  • Course Introduction
  • IBM AI Engineering PC Overview 
  • An Overview of Machine Learning
  • Machine Learning Model Lifecycle
  • A Day in the life of a Machine Learning Engineer
  • Data Scientist vs AI Engineer
  • Tools for Machine Learning
  • Scikit-learn Machine Learning Ecosystem
Assignments
  • Graded Quiz: Introduction to Machine Learning
  • Practice Quiz: Exploring Machine Learning Concepts
  • Practice Quiz: Understanding ML Engineering and AI differences
  • Practice Quiz: Essential Tools and Ecosystems for ML
readings
  • Course Overview
  • Helpful Tips for Course Completion
  • Module 1 Summary and Highlights

Module 2: Learning and logistic Regression

Videos
  • Introduction to Regression
  • Introduction to Simple Linear Regression
  • Introduction to Multiple Linear Regression
  • Polynomial and Non-Linear Regression
  • Introduction to Logistic Regression
  • Training a Logistic Regression Model
Readings
  • Module 2 Summary and Highlights
  • Cheat Sheet: Linear and Logistic Regression
Assignments
  • Graded Quiz: Linear and Logistic Regression
  • Practice Quiz: Linear Regression
  • Practice Quiz: Logistic Regression
App Items
  • Lab: Simple Linear Regression
  • Lab: Multiple Linear Regression

Module 3: Building Supervised Learning Models

Videos
  • Classification
  • Decision Trees
  • Regression Trees
  • Supervised Learning with SVMs
  • Supervised Learning with KNN
  • Bias, Variance, and Ensemble Models
Reading
  • Errata: Regression Trees Video
  • Module 3 Summary and Highlights
  • Cheat Sheet: Building Supervised Learning Models
Assignments
  • Graded Quiz: Building Supervised Learning Models
  • Practice Quiz: Classification and Regression
  • Practice Quiz: Other Supervised Learning Models
App Items
  • Lab: KNN
  • Lab: Decision Trees
  • (Optional) Lab: Faster Credit Card Fraud Detection using Snap ML
  • Lab: Regression Trees
  • (Optional) Lab: Faster Taxi Tip Prediction using Snap ML

Module 4: Building Unsupervised Learning Models

Videos
  • Clustering Strategies and Real-World Applications
  • K-means and More on K-means
  • DBSCAN and HDBSCAN Clustering
  • Clustering, Dimension Reduction, and Feature Engineering
  • Dimension Reduction Algorithms
Reading
  • Module 4 Summary and Highlights
  • Cheat Sheet: Building Unsupervised Learning Models
Assignments
  • Graded Quiz: Building Unsupervised Learning Models
  • Practice Quiz: Clustering
  • Practice Quiz: Dimension Reduction & Feature Engineering
App Items
  • Lab: Logistic Regression
  • Lab: SVM (Support Vector Machines)
  • Lab: Multiclass Prediction

Module 5: Evaluating and Validating machine Learning

Videos
  • Classification Metrics and Evaluation Techniques
  • Regression Metrics and Evaluation Techniques
  • Evaluating Unsupervised Learning Models: Heuristics and Techniques
  • Cross-Validation and Advanced Model Validation Techniques
  • Regularization in Regression and Classification
  • Data Leakage and Other Pitfalls
Assignments
  • Graded Quiz: Evaluating and Validating Machine Learning Models
  • Practice Quiz: Evaluating Machine Learning Models
  • Practice Quiz: Best Practices for Ensuring Model Generalizability
App Item
  • ab: Evaluating Classification Models
  • Lab: Evaluating Random Forest Performance
  • Lab: Evaluating K-means Clustering
  • Lab: Regularization in Linear Regression
  • Lab: Machine Learning Pipelines and GridSearchCV

Module 6: Final Exam and Project

Readings
  • Congratulations
  • IBM Digital Badge
  • Project Scenario
Assignments
  • Final Exam
App Item
  • Lab: Rain Prediction in Australia

Instructors

Mr Joseph Santarcangelo

Mr Joseph Santarcangelo
Data Scientist
IBM

Ph.D

Mr Saeed Aghabozorgi

Mr Saeed Aghabozorgi
Senior Data Scientist
IBM

Ph.D

FAQs

None

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