About This Course
In this machine learning course, you will dive into the exciting field of artificial intelligence and learn the foundations of machine learning algorithms and techniques. Through hands-on projects and practical examples, you will gain a solid understanding of key concepts such as supervised and unsupervised learning, model evaluation, feature engineering, and model deployment. By the end of the course, you will be equipped with the skills and knowledge to build and deploy machine learning models for a wide range of real-world applications.
Learning Objectives
Material Includes
- Datasets
Requirements
- Strong understanding of mathematics, including linear algebra and calculus
- Proficiency in programming, preferably with Python
- Familiarity with basic statistics concepts
- Knowledge of data manipulation and analysis
- Prior experience with Python libraries such as NumPy and Pandas is helpful
- Understanding of fundamental machine learning concepts is beneficial but not required
Target Audience
- Data scientists
- Machine learning engineers
- Software developers interested in machine learning
- Data analysts
- Researchers in artificial intelligence
- Professionals working with big data
- Students pursuing a career in machine learning
- Anyone interested in exploring and applying machine learning techniques
Curriculum
Concepts and Preprocessing
Core Concepts
Data Preprocessing
ML001
Supervised Learning
Unsupervised Learning
Time Series
Machine Learning Final Exam
Your Instructors
Edulearnia
R&D Engineer
I am in love with artificial intelligence, machine learning, deep learning and especially data science. I like challenges and a job well done. I am sure to participate in the local and international development of artificial intelligence. I like reading, writing, soft music. I like to be in a friendly relationship with my entourage, I like to be helpful.
Material Includes
- Datasets
Login with Google