Intro to Python
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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
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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.
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Probability
We use the intuitive concept of probability on a daily basis in our lives, just by the mere fact that we live in constant uncertainty. Data Science is one of the fields where we need to have conscious mastery and understanding of probability – the backbone of many important data science concepts. If you want to adopt the probabilistic mindset then check out our free pdf course notes which will teach you the basics of probability, combinatorics, Bayesian Notation, discrete distributions, continuous distributions, setting up and solving integrals and expressing complex formulas in Wolfram Alpha.
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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.
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Machine Learning with Decision Trees and Random Forests
Decision trees are a popular intuitive supervised machine learning algorithm, that is part of the sklearn library, and has wide areas of applications like- business growth opportunities evaluation, demographic-driven data client targeting, and strategic management planning. Every machine learner worth their salt needs to familiarize themselves with the decision trees machine learning model. These free machine learning with random forests and decision trees pdf course notes will teach you how do decision trees work, how they ensemble into the random forest algorithm, what are their pros and cons, which are the most commonly used performance metrics and much more.
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Intro to Data and Data Science
Rapid technological advancements and the invention of the world wide web have revolutionized the way we view and interact with data, turning data science into an integral part of the business landscape- 50% of the top companies implement data-driven practices. Therefore, if you want to make your first professional steps and find your bearings in the complex world of data science, we recommend you to download these free pdf course notes to Intro to Data and Data Science.
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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.
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Machine Learning with Ridge and Lasso Regression
One of the most common problems every data scientist faces when training machine learning models is overfitting—overcomplicating the model with irrelevant data, thereby reducing the model’s predictive and classification capabilities. This is where the regularization techniques -ridge and lasso come to the rescue, as they simplify the model and remove all the data noise. In these free pdf course notes, we will cover the basic concepts behind regression analyses, ridge vs lasso regression, cross validation for choosing a tuning parameter and relevant metrics for evaluating the model’s performance.
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