Ratings
Level
Learning hours
Learners
Skills you’ll Learn
Curriculum
In this module, you will learn about data science, terminologies and concepts in data science; data privacy in data science; data ethics in data science, artificial intelligence and machine learning; setting up the Python programming environment using different integrated development environments like Pycharm, Juypter notebook and Google Colab. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about machine learning, the key concepts, terminologies, types of machine learning techniques; machine learning pipeline, its applications and limitations; understand the required mathematics and statistics concepts for data science; and Python programming skills for data science. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about the steps in machine learning applications for the supervised learning and unsupervised learning; the application of regression model for supervised learning; the application of classification using random forest for supervised learning; and the K-means clustering for unsupervised learning. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
Hello
- Lecturer: group d
Ratings
Level
Learning hours
Learners
Skills you’ll Learn
Curriculum
In this module, you will learn about data science, terminologies and concepts in data science; data privacy in data science; data ethics in data science, artificial intelligence and machine learning; setting up the Python programming environment using different integrated development environments like Pycharm, Juypter notebook and Google Colab. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about machine learning, the key concepts, terminologies, types of machine learning techniques; machine learning pipeline, its applications and limitations; understand the required mathematics and statistics concepts for data science; and Python programming skills for data science. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about the steps in machine learning applications for the supervised learning and unsupervised learning; the application of regression model for supervised learning; the application of classification using random forest for supervised learning; and the K-means clustering for unsupervised learning. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
Hello
- Lecturer: group f
Module Overview: This module examines various governance frameworks and best practices that organizations can adopt to enhance accountability and transparency. Effective governance is crucial for organizational success and sustainability, ensuring that decisions are made in the best interests of stakeholders while adhering to legal and ethical standards.
- Lecturer: group c
Ratings
Level
Learning hours
Learners
Skills you’ll Learn
Curriculum
In this module, you will learn about data science, terminologies and concepts in data science; data privacy in data science; data ethics in data science, artificial intelligence and machine learning; setting up the Python programming environment using different integrated development environments like Pycharm, Juypter notebook and Google Colab. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about machine learning, the key concepts, terminologies, types of machine learning techniques; machine learning pipeline, its applications and limitations; understand the required mathematics and statistics concepts for data science; and Python programming skills for data science. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about the steps in machine learning applications for the supervised learning and unsupervised learning; the application of regression model for supervised learning; the application of classification using random forest for supervised learning; and the K-means clustering for unsupervised learning. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
Hello
Ratings
Level
Learning hours
Learners
Skills you’ll Learn
Curriculum
In this module, you will learn about data science, terminologies and concepts in data science; data privacy in data science; data ethics in data science, artificial intelligence and machine learning; setting up the Python programming environment using different integrated development environments like Pycharm, Juypter notebook and Google Colab. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about machine learning, the key concepts, terminologies, types of machine learning techniques; machine learning pipeline, its applications and limitations; understand the required mathematics and statistics concepts for data science; and Python programming skills for data science. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about the steps in machine learning applications for the supervised learning and unsupervised learning; the application of regression model for supervised learning; the application of classification using random forest for supervised learning; and the K-means clustering for unsupervised learning. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
Hello
- Lecturer: group d
Ratings
Level
Learning hours
Learners
Skills you’ll Learn
Curriculum
In this module, you will learn about data science, terminologies and concepts in data science; data privacy in data science; data ethics in data science, artificial intelligence and machine learning; setting up the Python programming environment using different integrated development environments like Pycharm, Juypter notebook and Google Colab. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about machine learning, the key concepts, terminologies, types of machine learning techniques; machine learning pipeline, its applications and limitations; understand the required mathematics and statistics concepts for data science; and Python programming skills for data science. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about the steps in machine learning applications for the supervised learning and unsupervised learning; the application of regression model for supervised learning; the application of classification using random forest for supervised learning; and the K-means clustering for unsupervised learning. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
Hello
- Lecturer: Group E
Ratings
Level
Learning hours
Learners
Skills you’ll Learn
Curriculum
In this module, you will learn about data science, terminologies and concepts in data science; data privacy in data science; data ethics in data science, artificial intelligence and machine learning; setting up the Python programming environment using different integrated development environments like Pycharm, Juypter notebook and Google Colab. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about machine learning, the key concepts, terminologies, types of machine learning techniques; machine learning pipeline, its applications and limitations; understand the required mathematics and statistics concepts for data science; and Python programming skills for data science. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about the steps in machine learning applications for the supervised learning and unsupervised learning; the application of regression model for supervised learning; the application of classification using random forest for supervised learning; and the K-means clustering for unsupervised learning. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
Hello
- Lecturer: Group A
- Lecturer: Bytes and Beyond
- Lecturer: Ben TITO
Ratings
Level
Learning hours
Learners
Skills you’ll Learn
Curriculum
In this module, you will learn about data science, terminologies and concepts in data science; data privacy in data science; data ethics in data science, artificial intelligence and machine learning; setting up the Python programming environment using different integrated development environments like Pycharm, Juypter notebook and Google Colab. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about machine learning, the key concepts, terminologies, types of machine learning techniques; machine learning pipeline, its applications and limitations; understand the required mathematics and statistics concepts for data science; and Python programming skills for data science. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
In this module, you will learn about the steps in machine learning applications for the supervised learning and unsupervised learning; the application of regression model for supervised learning; the application of classification using random forest for supervised learning; and the K-means clustering for unsupervised learning. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
Hello
- Lecturer: group f