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.

Course Notestheory

Backpropagation Algorithm

The backpropagation algorithm is the fundamental building block of neural networks and is used to effectively train them through the chain rule method- a technique used to find the derivatives of cost, considering any variable in a nested equation. While most packages already contain backpropagation algorithms in them, knowing the math behind them and how they work will help you better understand more advanced algorithms as well as handle vanilla ones with ease. Check out these free short pdf course notes on the backpropagation algorithm to learn some of the useful formulas and finding the results for backpropagation for the output layer and hidden layer.

Currently, the closest technology to mimicking the human brain and learning process is deep learning. It uses mathematical functions to map the input to the output and form patterns out of the data. Computationally superior to machine learning, deep learning can even analyze huge sets of unstructured data. At the heart of deep learning are neural networks which mimic the neuron activity in the human brain, enabling us to learn the structure of data by performing various tasks, without the need for human intervention. Check out these short free pdf course notes to find out the three layers of deep neural networks and the activation functions.

Machine Learning is the field concerned with building computational models that can execute high-level tasks using human-like reasoning. In other words, machine learning models possess the ability to learn autonomously. The recent introduction of neural networks has opened a new set of possibilities for machine learning and deep learning. Before you explore them, check out these free pdf course notes on intro to neural networks and get to know the building blocks of a machine learning algorithm, and the two types of supervised learning- regression and classification.

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.

Python is a high-level multipurpose programming language that is popular among all levels of programmers/data practitioners working in the field of education, big tech, transportation, science, agriculture and last but not least- finance. The plethora of Python libraries, its intuitive syntax, and wide range of tools allow Python to provide data-driven solutions to financial institutions of all sizes. In these free pdf course notes, we will explore the power of Python for finance, covering tools and techniques used by financial professionals such as calculating and comparing rates of return in Python, measuring investment risk, performing regression analyses, using Monte Carlo simulations as decision-making tools and much more.

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

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.

The data science career path has become increasingly competitive over the years, as the supply of data scientists is slowly outpacing the demand. Landing a data scientist job now is harder than 5 years ago, and employers have become more selective in the recruiting process. Businesses are looking for competent data scientists with solid preparation. Therefore, in these free pdf course notes you will be taught how to create a professional data science project portfolio, build a job-landing resume, ace the data science interview questions, solve the take-home test and ultimately start a career in data science.

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.

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.

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.

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.

Data has become a universal language for modern businesses and organizations who strive to use it strategically to gain better insights about the market. If you aspire to become proficient in the language of data and be at the forefront of business growth, the first step is to familiarize yourself with the concepts in the free pdf course notes on introduction to data literacy. Learn what defines a data literate individual, the purpose of data literacy in the modern data ecosystem, what data-driven decision making looks like and what are the benefits of working with data.

Data literacy is the ability to work with, comprehend and communicate data that generates logarithmic amount of value across all business departments. In the global workplace, where data usage and automation are rapidly positioning themselves at the heart of business operations, the need for a data literate workforce has never been higher. Studying these free pdf course notes on data literacy by top-data executive Oliver Maugain will help you leverage your newly learned data skills in this highly rewarding job market, which is experiencing a systemic data talent shortage.

By now you should have a solid understanding of data terminology, data quality assessment, different data storage systems, AI applications and Machine Learning techniques. We are going to conclude the series of free pdf data literacy course notes with data interpretation- a key component in the evaluation of financial assets on the financial market, prediction of market behavior, problem-solving in the fields of medicine and etc.

The consequences of businesses who do not perform data quality assessments can be devastating to business growth. Poor data quality can lead to executing the wrong business strategies, reduced efficiency, increased financial costs, damaged customer relationships and ultimately to bankruptcy. Therefore, in the third part of the free pdf course notes on data literacy, we provide information on how to read data, by distinguishing between good and bad data, what are the negative impacts of poor data quality, and the various descriptive methods that characterize a dataset.

AI is the modern day facilitator of human productivity that allows for human intelligence to execute feats that would have been considered impossible a few decades ago. As modern businesses integrate AI and its Machine Learning capabilities with their decision-making and operational activities, the need for data competent individuals who can help with the integration process has never been higher. Therefore, in the Using Data free pdf course notes we will familiarize you with the practical applications of AI and ML, the types of supervised and unsupervised machine learning techniques and natural language processing abilities of AI.

Thanks to the mass digitalization of the world, information has become the oil of the 21st century that sustains the engines of modern businesses. As of 2021, there are approximately 4,66 billion internet users! Imagine the amount of data, that consumers leave behind that just waits to be stored and processed by businesses. Therefore, in these free pdf course notes on Understanding Data Literacy, we are going to identify various types of data, the three defining properties of Big Data and the different methods for storing data.

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.

Check out our most helpful downloadable resources according to 365 Data Science’s students and expert team of instructors.

Course Notestheory

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.

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.

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.

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.

Check out our most helpful downloadable resources according to 365 Data Science’s students and expert team of instructors.

Course Notestheory

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.

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.

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.

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.