Course Notes
Enhance your learning journey with in-depth course notes that support your understanding of core data science concepts.Course notes are the perfect complementary educational resource in .pdf for anyone who wishes to go in-depth with fundamental to advanced data science concepts and get a taste of our e-learning style.
Machine Learning with Naïve Bayes
Naïve Bayes Classifier is a supervised classification machine learning algorithm inspired by the Bayes Theorem. Its ability to make intuitive real time-predictions from small non-linear sets makes it perfect for consumer behavior predictions, recommendation systems and text analysis - news article categorization, email category filtering and sentiment analyses. In the free Machine Learning with Naïve Bayes pdf course notes we are going to build upon your sklearn Naïve Bayes skills by going over the algorithm’s computational capabilities, outlining the 7 steps in creating a supervised machine learning model and identifying 6 relevant metrics to use for performance evaluation.
Learn MoreCheck out our most helpful downloadable resources according to 365 Data Science students and our expert team of instructors.
Statistics
Before we can build machine learning algorithms or extract valuable insight from our data we need to transform the raw data into an understandable format and check its quality. In other words, we need to do data preprocessing, which requires statistical knowledge every step along the way. Therefore, learning statistics is a must-have skill when doing data science. In these free pdf course notes, we will be covering the fundamentals of statistics, the different types of distributions, confidence intervals and respective formulas, calculation of covariance and correlation, hypotheses testing, and much more
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Intro to Python
Python is the top programming language in both the TIOBE and PYPL Index, making it one of the most popular programming languages worldwide. This is all thanks to its intuitive, beginner-friendly syntax and wide range of real-world applications like web development, scientific computing, game development, AI & machine learning, graphic design etc. But before you can dwell in this vast world of possibilities, you need to learn the basics of Python. We introduce you the free pdf Intro to Python course notes where you will learn basic Python syntax, how to create and use functions, conditional statements , iteration and much more.
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Data Strategy
Companies that sit on treasure troves of data but have no defined or bad data strategy not only fail to collect dividends from their wealth of data, but also incur great financial and reputational losses. In these free pdf course notes on Data Strategy, you will learn what is the purpose of a company’s data strategy, how data strategy helps build competitive advantage, how to create technology and data infrastructures that support business growth and much more.
Learn MoreMachine Learning with Naïve Bayes
Naïve Bayes Classifier is a supervised classification machine learning algorithm inspired by the Bayes Theorem. Its ability to make intuitive real time-predictions from small non-linear sets makes it perfect for consumer behavior predictions, recommendation systems and text analysis - news article categorization, email category filtering and sentiment analyses. In the free Machine Learning with Naïve Bayes pdf course notes we are going to build upon your sklearn Naïve Bayes skills by going over the algorithm’s computational capabilities, outlining the 7 steps in creating a supervised machine learning model and identifying 6 relevant metrics to use for performance evaluation.
Learn MoreMost Popular Course Notes
Check out our most helpful downloadable resources according to 365 Data Science students and our expert team of instructors.
Statistics
Before we can build machine learning algorithms or extract valuable insight from our data we need to transform the raw data into an understandable format and check its quality. In other words, we need to do data preprocessing, which requires statistical knowledge every step along the way. Therefore, learning statistics is a must-have skill when doing data science. In these free pdf course notes, we will be covering the fundamentals of statistics, the different types of distributions, confidence intervals and respective formulas, calculation of covariance and correlation, hypotheses testing, and much more
Learn MoreIntro to Python
Python is the top programming language in both the TIOBE and PYPL Index, making it one of the most popular programming languages worldwide. This is all thanks to its intuitive, beginner-friendly syntax and wide range of real-world applications like web development, scientific computing, game development, AI & machine learning, graphic design etc. But before you can dwell in this vast world of possibilities, you need to learn the basics of Python. We introduce you the free pdf Intro to Python course notes where you will learn basic Python syntax, how to create and use functions, conditional statements , iteration and much more.
Learn MoreData Strategy
Companies that sit on treasure troves of data but have no defined or bad data strategy not only fail to collect dividends from their wealth of data, but also incur great financial and reputational losses. In these free pdf course notes on Data Strategy, you will learn what is the purpose of a company’s data strategy, how data strategy helps build competitive advantage, how to create technology and data infrastructures that support business growth and much more.
Learn MoreMachine Learning with Naïve Bayes
Naïve Bayes Classifier is a supervised classification machine learning algorithm inspired by the Bayes Theorem. Its ability to make intuitive real time-predictions from small non-linear sets makes it perfect for consumer behavior predictions, recommendation systems and text analysis - news article categorization, email category filtering and sentiment analyses. In the free Machine Learning with Naïve Bayes pdf course notes we are going to build upon your sklearn Naïve Bayes skills by going over the algorithm’s computational capabilities, outlining the 7 steps in creating a supervised machine learning model and identifying 6 relevant metrics to use for performance evaluation.
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