Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
0 Hour(s) 32 Minute(s)
Language
English
Taught by
Peter Winkler
Rating
9 views
Course Overview
Machine Learning, Deep Learning and Beyond
Data Science and Artificial Intelligence: The Complete Course
Learn the skills you need to become a data scientist or artificial intelligence engineer
In-depth coverage of all the essential topics in data science and artificial intelligence
Hands-on exercises and projects to help you apply your skills
Learn from experienced instructors who are experts in the field
This course is perfect for anyone who wants to learn about data science and artificial intelligence. Whether you are a beginner or an experienced professional, you will find this course to be informative and engaging.
Here are some of the topics you will learn in this course:
Introduction to data science and artificial intelligence
Data collection and preprocessing
Exploratory data analysis
Statistical modeling
Machine learning
Deep learning
Natural language processing
Computer vision
Ethics and bias in AI
By the end of this course, you will be able to:
Understand the definitions of data science and artificial intelligence
Collect and preprocess data
Explore data and identify patterns
Fit statistical models and evaluate their performance
Train and evaluate machine learning models
Train and evaluate deep learning models
Implement natural language processing and computer vision
Understand the ethical considerations of AI
This course is taught by experienced instructors who are experts in the field of data science and artificial intelligence. They will share their knowledge and expertise with you, and help you to learn the skills you need to succeed in this field.
If you are interested in learning about data science and artificial intelligence, then this course is for you. Sign up today and start learning!
Course Content
- 10 section(s)
- 11 lecture(s)
- Section 1 Lesson 1: Introduction to Data Science and Artificial Intelligence
- Section 2 Lesson 2: Data Collection and Preprocessing
- Section 3 Lesson 3: Exploratory Data Analysis
- Section 4 Lesson 4: Statistical Modeling
- Section 5 Lesson 5: Machine Learning
- Section 6 Lesson 6: Deep Learning
- Section 7 Lesson 7: Natural Language Processing (NLP)
- Section 8 Lesson 8: Computer Vision
- Section 9 Lesson 9: Ethics and Bias in AI
- Section 10 Lesson 10: Project
What You’ll Learn
- Understand the definitions of data science and artificial intelligence
- Learn about the history of data science and artificial intelligence
- Explore the different applications of data science and artificial intelligence
- Learn how to collect data
- Learn how to clean data
- Learn how to preprocess data
- Learn how to explore data
- Learn how to identify patterns in data
- Learn how to visualize data
- Understand the definition of statistical modeling
- Learn about different types of statistical models
- Learn how to fit statistical models
- Learn how to evaluate statistical models
- Understand the definition of machine learning
- Learn about different types of machine learning algorithms
- Learn how to train machine learning models
- Learn how to evaluate machine learning models
- Understand the definition of deep learning
- Learn how deep learning works
- Learn about different types of deep learning models
- Learn how to train deep learning models
- Learn how to evaluate deep learning models
- Understand the definition of natural language processing
- Learn how natural language processing works
- Learn about different tasks in natural language processing
- Learn how to implement natural language processing
- Understand the definition of computer vision
- Learn how computer vision works
- Learn about different tasks in computer vision
- Learn how to implement computer vision
- Understand the ethical considerations of AI
- Learn how to avoid bias in AI
- Apply the skills learned in the course to a real-world project
Reviews
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RRobert Rucker
Very well done
