By the end of this course, you will be able to
By the end of this level, you will be able to: Understand the differences between supervised and unsupervised learning models, and define the basics of Naïve Bayes classifier as an example of probabilistic classification techniques.
Learn about different methods for evaluating machine learning models, and demonstrate the usefulness of different time series prediction methods including ARIMA and FBProphet.
Demonstrate how to use clustering techniques including K-means and hierarchical clustering, and implement a project using Python that summarizes the different phases of data science as applied to a specific problem.
Course details
- 3 h 16 m/27 lessons
- Last updated: 27/10/2022
- Course completion certificate
Course Content
Introduction to Machine Learning 1
Introduction to Machine Learning 2
Types of Machine Learning Algorithms
Introduction to Machine Learning 1
Introduction to Machine Learning 2
Types of Machine Learning Algorithms
Unsupervised Learning 1
Unsupervised Learning 2
Supervised Learning
Classification
Naive Bayes 1
Naive Bayes 2
Practical Examples of Classification 1
Practical Examples of Classification 2
Practical Examples of Classification 3
The Naive Classifier in Python Programming Language
Model Evaluation
Time Series Analysis 1
Time Series Analysis 2
Linear Regression
Linear Regression in Python Programming Language
ARIMA Model
Using ARIMA Model in Python Programming Language
Using FB Prophet in Python Programming Language
Clustering 1
Clustering 2
Clustering 3
Hierarchical Clustering
Hierarchical Clustering in Python Programming Language
Course Recap
About this course
This level provides an introduction to machine learning as one of the important fields of AI used by data scientists. It explains examples of supervised learning techniques including classification, regression, and time series prediction. Additionally, it gives examples of unsupervised clustering techniques while demonstrating their uses.
Course requirements and prerequisites
- Graduate of any university (Engineering is not mandatory)
- Previous programming experience of any language is a big plus
- Knowledge of Linear algebra is a big plus
Mentor
Sherif Gamal
Engineer and Senior Member of IEEE
3,396 Learners
5 Courses
