In this comprehensive course, students will master the basics of machine learning and get practical experience with different algorithms of machine learning. You will learn how to preprocess data, evaluate models, and deal with overfitting and underfitting. The course will explore practical uses of machine learning, such as natural language processing and picture recognition.
By the completion of the course, students will have the knowledge and abilities to use machine learning to address difficult issues and keep up with recent developments in the industry.
- Solid understanding of the concepts, principles, and techniques used in machine learning.
- Understanding and implementing machine learning algorithms.
- Techniques of evaluation, experimentation, and project deployment.
- Data science at scale with PySpark, AI with TensorFlow.
- Deploying machine learning models to the cloud (MLOps).
- Hands-on experience applying machine learning techniques to real-world datasets.
- Data preprocessing and feature engineering.
- Stay updated with the latest research and advancements.
- Lectures 0
- Quizzes 0
- Duration 29 Hours
- Skill level All levels
- Language English
- Students 27
- Assessments Yes