Top 10 Basic Computer Science Topics to Learn

computer architecture - basic computer topics to learn

Computer science is an expansive field with a variety of areas that are worth exploring. Whether you’re just starting out or already have some experience in computer science, there are certain topics that every aspiring software engineer should understand. This blog post will cover the basic computer science topics that are essential for any software engineer or software programmer to know. Computer Architecture Computer architecture is a course of study that explores the fundamental elements of computer building and design. It’s an important field of study for software engineers to understand, since it provides basic principles and concepts related to hardware and software interactions. Computer architecture courses typically cover a …

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Posted in Data Science, Software Engg.

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 learning. …

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Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Free Datasets for Machine Learning & Deep Learning

dataset publicly_available free machine learning

Are you looking for free / popular datasets to use for your machine learning or deep learning project? Look no further! In this blog post, we will provide an overview of some of the best free datasets available for machine learning and deep learning. These datasets can be used to train and evaluate your models, and many of them contain a wealth of valuable information that can be used to address a wide range of real-world problems. So, let’s dive in and take a look at some of the top free datasets for machine learning and deep learning! Here is the list of free data sets for machine learning & …

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Posted in Data Science, Deep Learning, Machine Learning. Tagged with , .

Challenges for Machine Learning / AI Projects

Challenges related to Machine Learning Projects Implementations

In this post, you will learn about some of the key challenges in relation to achieving successful AI / machine learning (ML) or Data science projects implementation in a consistent and sustained manner. As AI / ML project stakeholders including senior management stakeholders, data science architects, product managers, etc, you must get a good understanding of what would it take to successfully execute AI / ML projects and create value for the customers and the business.  Whether you are building AI / ML products or enabling unique models for your clients in SaaS setup, you will come across most of these challenges.  Understanding the Business Problem Many times, the nature …

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Difference between Online & Batch Learning

online learning - machine learning system

In this post, you will learn about the concepts and differences between online and batch or offline learning in relation to how machine learning models in production learn incrementally from the stream of incoming data or otherwise. It is one of the most important aspects of designing machine learning systems. Data science architects would require to get a good understanding of when to go for online learning and when to go for batch or offline learning. Why online learning vs batch or offline learning? Before we get into learning the concepts of batch and on-line or online learning, let’s understand why we need different types of models training or learning …

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Large language models: Concepts & Examples

large language models concepts examples

Large language models (LLM) have been gaining traction in the world of natural language processing (NLP). These models are trained on large datasets, which contain hundreds of millions to billions of words. LLMs, as they are known, rely on complex algorithms that sift through large datasets and recognize patterns at the word level. This data helps the model better understand natural language and how it is used in context. Through this understanding, these models can generate more accurate results when processing text. Let’s take a deeper look into understanding large language models and why they are important. What are large language models (LLM) and how do they work? Large language …

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Posted in Data Science, Machine Learning, NLP.

Most Common Machine Learning Tasks

common machine learning tasks

This article represents some of the most common machine learning tasks that one may come across while trying to solve machine learning problems. Also listed is a set of machine learning methods that could be used to resolve these tasks. Please feel free to comment/suggest if I missed mentioning one or more important points. Also, sorry for the typos. You might want to check out the post on what is machine learning?. Different aspects of machine learning concepts have been explained with the help of examples. Here is an excerpt from the page: Machine learning is about approximating mathematical functions (equations) representing real-world scenarios. These mathematical functions are also referred …

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Posted in AI, Big Data, Data Science, Machine Learning. Tagged with , .

Moving Average Method for Time-series forecasting

Moving average definition & examples

In this post, you will learn about the concepts of the moving average method in relation to time-series forecasting. You will get to learn Python examples in relation to training a moving average machine learning model.  The following are some of the topics which will get covered in this post: What is the moving average method? Why use the moving average method? Python code example for the moving average methods What is Moving Average method? The moving average is a statistical method used for forecasting long-term trends. The technique represents taking an average of a set of numbers in a given range while moving the range. For example, let’s say …

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Gradient Boosting Algorithm: Concepts, Example

gradient boosting algorithm error vs iterations

If you are a data scientist or machine learning engineer, then you know that Gradient Boosting Algorithm (GBA) is one of the most powerful algorithms in predicting results from data. This algorithm has been proven to increase the accuracy of predictions and is becoming increasingly popular among data scientists. Let’s take a closer look at GBA and explore how it works with an example.   What is a Gradient Boosting Algorithm? Gradient boosting algorithm is a machine learning technique used to build predictive models. It creates an ensemble of weak learners, meaning that it combines several smaller, simpler models in order to obtain a more accurate prediction than what an …

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Posted in Data Science, Machine Learning.

Feature Scaling in Machine Learning: Python Examples

In this post you will learn about a simple technique namely feature scaling with Python code examples using which you could improve machine learning models. The models will be trained using Perceptron (single-layer neural network) classifier. First and foremost, lets quickly understand what is feature scaling and why one needs it? What is Feature Scaling and Why does one need it? Feature scaling is a method used to standardize the range of independent variables or features of data. In data processing, it is also known as data normalization or standardization. Feature scaling is generally performed during the data pre-processing stage, before training models using machine learning algorithms.  The goal is to …

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Data Ingestion Types – Concepts & Examples

data ingestion types

Data ingestion is the process of moving data from its original storage location to a data warehouse or other database for analysis. Data engineers are responsible for designing and managing data ingestion pipelines. Data can be ingested in different modes such as real-time, batch mode, etc. In this blog, we will learn the concepts about different types of data ingestion with the help of examples. What is Data Ingestion? Data ingestion is the process of extracting data from its source and loading it into a data storage system. The data source can be either structured or unstructured. Structured data sources are typically found in relational databases, while unstructured data sources …

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Data Warehouse Concepts & Examples

data warehouse concepts and examples

A data warehouse is a system used for reporting and data analysis, and is considered a core component of business intelligence. Data warehouses are centralized repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that can be used to answer business questions. Data warehouses are used to support business intelligence applications. Business intelligence applications are used to make decisions about the operation of the business.  A data warehouse is usually populated with data from an operational database, which contains transactions. The process of populating the data warehouse is called Extract, Transform, and Load (ETL). This process cleans, transforms, and …

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Data Models Types, Uses & Examples

relational data model

A data model is a collection of concepts that can be used to describe the structure of a database. When it comes to data modeling, there are several different types of models that data analysts and data modelers can use. There are several different types of data models, and each has its own strengths and weaknesses. Some of most popular types of data models are the relational model, the dimensional model, and the hierarchical model. In this blog post, we will provide a brief overview of different types of data model and when you might use each one with the help of real world examples. The Relational Data Model The …

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Drivetrain Approach for Machine Learning

drivetrain approach for machine learning

In this post, you will learn about a very popular approach or methodology called as Drivetrain approach coined by Jeremy Howard. The approach provides you steps to design data products that provide you with actionable outcomes while using one or more machine learning models. The approach is indeed very useful for data scientists/machine learning enthusiasts at all levels. However, this would prove to be a great guide for data science architects whose key responsibility includes designing the data products.  Without further ado, let’s do a deep dive. Why Drivetrain Approach? Before getting into the drivetrain approach and understands the basic concepts, Lets understand why drivetrain approach in the first place? …

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Business Analytics vs Business Intelligence

business analytics vs business intelligence

If you work in the field of data analysis, you’ve probably heard the terms “business analytics” and “business intelligence” used interchangeably. However, although they are similar, there are some important differences between the two concepts. In this blog post, we’ll take a closer look at business analytics and business intelligence and explore the key ways in which they differ. What is Business Analytics? Business analytics is a set of analytical methods and tools / technologies for analyzing and solving business problems by gathering, analyzing, understanding, discovering and communicating significant patterns in the data. In other words, it is a process or set of methods / steps for exploring and uncovering …

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Machine Learning Models Evaluation Techniques

AUC-ROC curve

Machine learning is a powerful machine intelligence technique that can be used to develop predictive models for different types of data. It has become the backbone of many intelligent applications and evaluating machine learning model performance at a regular intervals is key to success of such applications. A machine learning model’s performance depends on several factors including the type of algorithm used, how well it was trained and more. In this blog post, we will discuss  essential techniques for evaluating machine-learning model performance in order to provide you with some best practices when working with machine-learning models. The following are different techniques that can be used for evaluating machine learning …

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