IBM - Machine Learning with Python Offered by IBM
- Offered byCoursera
Machine Learning with Python Offered by IBM at Coursera Overview
Duration | 17 hours |
Start from | Start Now |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website |
Credential | Certificate |
Machine Learning with Python Offered by IBM at Coursera Highlights
- Flexible deadlines
- 100% online
- Graded quizzes and assignments
- Taught by top companies and universities
- Learn on your own schedule
- Apply your skills with hands-on projects
Machine Learning with Python Offered by IBM at Coursera Course details
- Data Scientists
- Data Analysts
- Machine Learning Engineers
- Risk Managers
- Data Engineers
- Learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You'll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.
- Learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy.
- Learn about classification technique. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, you learn about pros and cons of each method, and different classification accuracy metrics.
- Learn about different clustering approaches. You learn how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations. You understand 3 main types of clustering, including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering.
- Learn about recommender systems. First, you will get introduced with main idea behind recommendation engines, then you understand two main types of recommendation engines, namely, content-based and collaborative filtering.
- Project and report submission.
- This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components:
- First, you will be learning about the purpose of Machine Learning and where it applies to the real world.
- Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
Machine Learning with Python Offered by IBM at Coursera Curriculum
Introduction to Machine Learning
Welcome
Introduction to Machine Learning
Python for Machine Learning
Supervised vs Unsupervised
Regression
Introduction to Regression
Simple Linear Regression
Model Evaluation in Regression Models
Evaluation Metrics in Regression Models
Multiple Linear Regression
Non-Linear Regression
1 practice exercise Regression
Classification
Introduction to Classification
K-Nearest Neighbours
Evaluation Metrics in Classification
Introduction to Decision Trees
Building Decision Trees
Intro to Logistic Regression
Logistic regression vs Linear regression
Logistic Regression Training
Support Vector Machine
Clustering
Intro to Clustering
Intro to k-Means
More on k-Means
Intro to Hierarchical Clustering
More on Hierarchical Clustering
DBSCAN
Recommender Systems
Intro to Recommender Systems
Content-based Recommender Systems
Collaborative Filtering
Final Project
OPTIONAL: Signing-up for a Watson Studio Account
OPTIONAL: Sharing Notebooks on Watson Studio