The perfect complementary educational resource in pdf for anyone who wants 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’s students and expert team of instructors.
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.
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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.
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 More
Machine Learning with K-Nearest Neighbors
K Nearest Neighbors - also known as KNN, is one of the most popular AI algorithms thanks to its simplicity of use and relatively high level of accuracy compared to more sophisticated algorithms. The KNN machine learning model has a very fast training process, making it a good machine learning algorithm to analyze multiclass datasets right off the bat. In these free Machine Learning with KNN pdf course notes, you will learn about the algorithm’s pros and cons, defining distance metrics, the important steps in creating a KNN model and the most commonly used performance metrics.
Learn MoreMost Popular Course Notes
Check out our most helpful downloadable resources according to 365 Data Science’s students and expert team of instructors.
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 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 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 MoreMachine Learning with K-Nearest Neighbors
K Nearest Neighbors - also known as KNN, is one of the most popular AI algorithms thanks to its simplicity of use and relatively high level of accuracy compared to more sophisticated algorithms. The KNN machine learning model has a very fast training process, making it a good machine learning algorithm to analyze multiclass datasets right off the bat. In these free Machine Learning with KNN pdf course notes, you will learn about the algorithm’s pros and cons, defining distance metrics, the important steps in creating a KNN model and the most commonly used performance metrics.
Learn More