A Foundation For Machine Learning and Data Science

A Foundation For Machine Learning and Data Science, available at $54.99, has an average rating of 4.5, with 30 lectures, based on 2 reviews, and has 8 subscribers.

You will learn about A solid foundation for Machine Learning and Data Science Black-box ML concepts A high-level understanding of the 11 stages involved in developing and implementing ML projects Python for Machine Learning and Data Science Python data types and structures, NumPy data structures, and Pandas data structures Pandas data indexing and selection, Operating on Pandas data, Handling missing data, Hierarchical indexing/ multi-indexing Combining datasets, aggregation, and grouping Working with strings, list-set-dictionary comprehensions, functions, unpacking sequences, and so on How to use NumPy for numerical computing, vectorization, broadcasting, data transformation, and so on How to use Pandas for data analysis and data manipulation Jupyter Notebook commands and markdown codes Linear algebra including the types of linear regression problems and the types of classification problems, and so on Statistics including Why do we need to learn statistics? What are statistical models? What are the different types of statistics available? What are mean, median, mode, quartiles, and percentiles? What are range, variance, and standard deviation? What are skewness and kurtosis? What are the different types of variables we will be dealing with? How statistics is used in various stages of machine learning? and so on Probability theory including the language of Probability theory, Probability Tree, Types of probability, why we need to learn Probability? and so on Object-Oriented Programming An overview of important libraries used in ML and DS for data processing, data analysis, data manipulation, visualization, and other supporting libraries And, much more This course is ideal for individuals who are Beginners with little programming experience and basic mathematics or Experienced programmers who want to pursue a career in ML/ Data Science/ AI or People who have already taken other Machine Learning and Data Science courses who want to strengthen their foundational skills It is particularly useful for Beginners with little programming experience and basic mathematics or Experienced programmers who want to pursue a career in ML/ Data Science/ AI or People who have already taken other Machine Learning and Data Science courses who want to strengthen their foundational skills.

Enroll now: A Foundation For Machine Learning and Data Science

Summary

Title: A Foundation For Machine Learning and Data Science

Price: $54.99

Average Rating: 4.5

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: $27.99

Quality Status: approved

Status: Live

What You Will Learn

• A solid foundation for Machine Learning and Data Science
• Black-box ML concepts
• A high-level understanding of the 11 stages involved in developing and implementing ML projects
• Python for Machine Learning and Data Science
• Python data types and structures, NumPy data structures, and Pandas data structures
• Pandas data indexing and selection, Operating on Pandas data, Handling missing data, Hierarchical indexing/ multi-indexing
• Combining datasets, aggregation, and grouping
• Working with strings, list-set-dictionary comprehensions, functions, unpacking sequences, and so on
• How to use NumPy for numerical computing, vectorization, broadcasting, data transformation, and so on
• How to use Pandas for data analysis and data manipulation
• Jupyter Notebook commands and markdown codes
• Linear algebra including the types of linear regression problems and the types of classification problems, and so on
• Statistics including Why do we need to learn statistics? What are statistical models? What are the different types of statistics available?
• What are mean, median, mode, quartiles, and percentiles? What are range, variance, and standard deviation? What are skewness and kurtosis?
• What are the different types of variables we will be dealing with?
• How statistics is used in various stages of machine learning? and so on
• Probability theory including the language of Probability theory, Probability Tree, Types of probability, why we need to learn Probability? and so on
• Object-Oriented Programming
• An overview of important libraries used in ML and DS for data processing, data analysis, data manipulation, visualization, and other supporting libraries
• And, much more

Who Should Attend

• Beginners with little programming experience and basic mathematics
• Experienced programmers who want to pursue a career in ML/ Data Science/ AI
• People who have already taken other Machine Learning and Data Science courses who want to strengthen their foundational skills

Target Audiences

• Beginners with little programming experience and basic mathematics
• Experienced programmers who want to pursue a career in ML/ Data Science/ AI
• People who have already taken other Machine Learning and Data Science courses who want to strengthen their foundational skills