## Supervised & Unsupervised Learning Difference

Supervised and unsupervised learning are two different common types of machine learning tasks that are used to solve many different types of business problems. Supervised learning uses training data with labels to create supervised models, which can be used to predict outcomes for future datasets. Unsupervised learning is a type of machine learning task where the training data is not labeled or categorized in any way. For beginner data scientists, it is very important to get a good understanding of the difference between supervised and unsupervised learning. In this post, we will discuss how supervised and unsupervised algorithms work and what is difference between them. You may want to check …

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## Why & When to use Eigenvalues & Eigenvectors?

In this post, you will learn about why and when you need to use Eigenvalues and Eigenvectors? As a data scientist/machine learning Engineer, one must need to have a good understanding of concepts related to Eigenvalues and Eigenvectors as these concepts are used in one of the most popular dimensionality reduction techniques – Principal Component Analysis (PCA). In PCA, these concepts help in reducing the dimensionality of the data (curse of dimensionality) resulting in a simpler model which is computationally efficient and provides greater generalization accuracy.   In this post, the following topics will be covered: Background – Why need Eigenvalues & Eigenvectors? What are Eigenvalues & Eigenvectors? When to use Eigenvalues …

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## PCA Explained Variance Concepts with Python Example

In this post, you will learn about the concepts of explained variance which is one of the key concepts related to principal component analysis (PCA). The explained variance concepts will be illustrated with Python code examples. Check out the concepts of Eigenvalues and Eigenvectors in this post – Why & when to use Eigenvalue and Eigenvectors. What is Explained Variance? Explained variance is a statistical measure of how much variation in a dataset can be attributed to each of the principal components (eigenvectors) generated by a PCA. In very basic terms, it refers to the amount of variability in a data set that can be attributed to each individual principal component. …

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## Cash Forecasting Models & Treasury Management

As a business owner, you are constantly working to ensure that your company has the cash it needs to operate. Cash forecasting is one of the most important aspects of treasury management, and it’s something that you should be paying attention to. Cash forecasting is a great example of where machine learning can have a real impact. By using historical data, we can build models that predict future cash flow for a company. This enables treasury managers to make better decisions about how to allocate resources and manage risks. As data scientists or machine learning engineers, it is important to be able to understand and explain the business value of …

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## Accounts Payable Machine Learning Use Cases

The machine learning for accounts payable market is expected to grow from $6.1 million in 2016 to$76.8 million by 2021, at a compound annual growth rate (CAGR) of 53 percent. The software industry is rapidly embracing machine learning for account payable. As account payable becomes more automated, it also becomes more data-driven. Machine learning is enabling account payables stakeholders to leverage powerful new capabilities in this arena. In this blog post, you will learn machine learning / deep learning / AI use cases for account payable. What is Accounts Payable? Account payable is a crucial part of the business process because it helps to ensure that businesses have the …

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## 85+ Free Online Books, Courses – Machine Learning & Data Science

This post represents a comprehensive list of 85+ free books/ebooks and courses on machine learning,  deep learning, data science, optimization, etc which are available online for self-paced learning.  This would be very helpful for data scientists starting to learn or gain expertise in the field of machine learning / deep learning. Please feel free to comment/suggest if I missed mentioning one or more important books that you like and would like to share. Also, sorry for the typos. Following are the key areas under which books are categorized: Data science Pattern Recognition & Machine Learning Probability & Statistics Neural Networks & Deep Learning Optimization Data mining Mathematics Here is my post …

## Tensor Explained with Python Numpy Examples

Tensors are a hot topic in the world of data science and machine learning. But what are tensors, and why are they so important? In this post, we will explain the concepts of Tensor using Python Numpy examples with the help of simple explanation. We will also discuss some of the ways that tensors can be used in data science and machine learning. When starting to learn deep learning, you must get a good understanding of the data structure namely tensor as it is used widely as the basic data structure in frameworks such as tensorflow, PyTorch, Keras etc. Stay tuned for more information on tensors! What are tensors, and why are …

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## How to become a Data Analyst? Skills & Experience

Do you want to become a data analyst? It’s a great career choice! Data analysts are in high demand, and with the right skills, you can make a good living doing something you love. In this blog post, we will discuss the skills required for data analysis, and provide some tips on how to acquire them. We will also recommend some courses and books that can help you get started on your data analyst career path! What is data analysis and what do data analysts do? Data analysis is the process of inspecting, cleansing, transforming, and creating data visualizations with the goal of discovering useful information, suggesting conclusions, and supporting …

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## Hypothesis Testing Steps & Examples: What, Why & How

Hypothesis testing is a technique that helps scientists, researchers, or for that matter, anyone test the validity of their claims or hypotheses about real-world or real-life events. Hypothesis testing techniques are often used in statistics and data science to analyze whether the claims about the occurrence of the events are true, whether the results returned by performance metrics of machine learning models are representative of the models or they happened by chance. This blog post will cover some of the key statistical concepts including steps and examples in relation to what is hypothesis testing, and, how to formulate them. The knowledge of hypothesis formulation and hypothesis testing holds the key …

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## Supply chain management & AI / Machine Learning

As supply chains become more complex, businesses are looking for new ways to optimize and automate their supply chain operations. One area that is seeing a lot of growth is the use of artificial intelligence (AI) and machine learning in supply chain management. There are many different applications for these technologies in supply chain management, from forecasting demand to optimizing inventory levels. In this blog post, we will explore some of the most interesting use cases for AI and machine learning in supply chain management. What is supply chain management and what are its key components? Supply chain management is the process of coordinating and controlling the flow of goods, …

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## Supplier Relationship Management & Machine Learning / AI

Supplier relationship management (SRM) is the process of managing supplier relationships to develop and maintain a strategic procurement partnership. SRM includes focus areas such as supplier selection, procurement strategy development, procurement negotiation, and performance measurement and improvement. SRM has been around for over 20 years but we are now seeing new technologies such as machine learning come into play. What exactly does advanced analytics such as artificial intelligence (AI) / machine learning (ML) have to do with SRM? And how will AI/ML technologies transform procurement? What are some real-world machine learning use cases related to supplier relationships management? What are a few SRM KPIs/metrics which can be tracked by leveraging …

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## Cohen Kappa Score Python Example: Machine Learning

Cohen’s Kappa Score is a statistic used to measure the performance of machine learning classification models. In this blog post, we will discuss what Cohen’s Kappa Score is and Python code example representing how to calculate Kappa score using Python. We will also provide a code example so that you can see how it works! What is Cohen’s Kappa Score or Coefficient? Cohen’s Kappa Score, also known as the Kappa Coefficient, is a statistical measure of inter-rater agreement for categorical data. Cohen’s Kappa Coefficient is named after statistician Jacob Cohen, who developed the metric in 1960.   It is generally used in situations where there are two raters, but it …

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## Accuracy, Precision, Recall & F1-Score – Python Examples

Classification models are used in classification problems to predict the target class of the data sample. The classification model predicts the probability that each instance belongs to one class or another. It is important to evaluate the performance of the classifications model in order to reliably use these models in production for solving real-world problems. Performance measures in machine learning classification models are used to assess how well machine learning classification models perform in a given context. These performance metrics include accuracy, precision, recall, and F1-score. Because it helps us understand the strengths and limitations of these models when making predictions in new situations, model performance is essential for machine …

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## Feedforward Neural Network Python Example

A feedforward neural network, also known as a multi-layer perceptron, is composed of layers of neurons that propagate information forward. In this post, you will learn about the concepts of feedforward neural network along with Python code example. We will start by discussing what a feedforward neural network is and why they are used. We will then walk through how to code a feedforward neural network in Python. In order to get good understanding on deep learning concepts, it is of utmost importance to learn the concepts behind feed forward neural network in a clear manner. Feed forward neural network learns the weights based on backpropagation algorithm which will be discussed …