Course details
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General Description of the Course
This beginner-friendly program was designed in collaboration between DeepLearning.AI and Stanford Online, and is led by renowned AI visionary Andrew Ng and his team. It provides the ideal starting point for anyone interested in mastering machine learning techniques. Here you will learn how to build and train powerful binary classification and prediction models using Python, leveraging popular libraries such as NumPy and scikit-learn. Whether you're looking to get into AI or improve your skills, this course equips you with practical knowledge to address real-world challenges and open the doors to a rewarding career in the dynamic field of machine learning.
Topics Covered:
- Building machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
- Building and training supervised machine learning models for binary classification and prediction tasks, including linear regression and logistic regression.
- Introduction to modern machine learning techniques, including supervised learning (multiple linear regression, logistic regression, neural networks and decision trees) and unsupervised learning (clustering, dimensionality reduction, recommender systems).
- Best practices used in Silicon Valley for innovation in Artificial Intelligence and machine learning, such as model evaluation and tuning and a data-centric approach to improve performance.
Course Features:
- Beginner-friendly program suitable for people with basic knowledge of coding (for loops, functions, if/else statements) and high school level mathematics (arithmetic, algebra).
- Approximately 33 hours to complete the course.
- 100% online, allowing students to start immediately and learn at their own pace.
- Includes hands-on learning projects through Coursera Labs to gain hands-on experience.
Key Learning Outcomes:
Upon completion of this course, you will have:
- Assimilated the key concepts of supervised machine learning and gained a solid understanding of how to build binary classification and prediction models.
- Gained proficiency in using popular machine learning libraries NumPy and scikit-learn in Python to build machine learning models.
- Developed skills in regularization techniques to avoid overfitting.
- Learned about gradient descent and its application in machine learning.
- Gained practical knowledge to apply machine learning to real world challenges.
Target audiences:
This course is ideal for:
- People interested in entering the field of Artificial Intelligence and machine learning.
- Professionals looking to build a career in machine learning and AI.
- Students and trainees with a basic understanding of coding and mathematics who wish to explore modern machine learning techniques.
Accreditation:
This course is offered by DeepLearning.AI in collaboration with Stanford Online. Students will receive a shareable certificate upon completion of the course, which demonstrates their mastery of supervised machine learning techniques and the ability to build machine learning models in Python.
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