Machine Learning
Machine Learning is the ability of machines to learn. Machine Learning is a branch of Artificial Intelligence. The various applications of Machine Learning are used in the Information Technology(IT), medical and education industries. For instance, in modern times, companies widely use Machine Learning for Data Analytics to get more customer engagement. Machine Learning works by learning patterns from a training dataset and applying it to an unseen real-world dataset. The five steps in a Machine Learning pipeline are defining the problem, building the dataset, training the model, evaluating the model, and finally using the model to generate predictions.
Introduction to every Machine Learning concept and application, including real-world examples, code implementation, and mathematical theory. Learn about the fundamental differences, types, and implementation.
What is Machine Learning
By aniket verma
● Published At Nov 2021
This article will explore what is machine learning and its applications. ... Keep reading ..
Data Science VS Artificial Intelligence VS Machine Learning VS Deep Learning
By Rajkeshav
● Published At Nov 2021
I hope you are doing well. In this blog, we will learn the aspects of data and the process which converts it into a structured format. We will also compare data science, Artificial Intelligence, Machine Learning, and Deep Learning. ... Keep reading ..
How does Machine Learning work?
By Anant Dhakad
● Published At Nov 2021
In this blog, we’ll learn about how machine learning works, its types, applications, and its importance.... Keep reading ..
Types of Machine learning
By Pratyksh
● Published At Nov 2021
This article explores and explains the types of machine learning ... Keep reading ..
Underfitting and Overfitting in ML
By Toohina Barua
● Published At Dec 2021
The objective of this blog is to understand what is underfitting and overfitting in ML. ... Keep reading ..
Tools for Machine Learning
Important tools for improving your performance while developing Machine Learning models and algorithms. Discover one of the most efficient IDEs, Data Science packages, and much more.
Google Colab
By Mayank Goyal
● Published At Jan 2022
This article will study the importance and efficiency of colab for ML and walk through some of its fundamentals.... Keep reading ..
Ultimate Package - Anaconda
By Mayank Goyal
● Published At Dec 2021
This article will look into Anaconda, what makes the conda environment peculiar, and some of the basic commands used in the conda environment.... Keep reading ..
What is AutoML in Machine Learning?
By soham Medewar
● Published At Jan 2022
This article will discuss autoML, its applications, and its features. Information about the organization that supports autoML. ... Keep reading ..
Applications of AutoML
By Akshat Chaturvedi
● Published At Dec 2021
In this blog, we’ll understand what AutoML is and AutoML’s advantages over the custom Machine Learning architecture; we’ll also see some of the AutoML Libraries.... Keep reading ..
Statistics & Probability
Statistics and probability are the best tools for solving problems, whether they are real-world problems or optimising machine learning models. Making sense of data and extracting meaningful information from it using statistical techniques, formulas, and variable moderation is an important first step in working on machine learning or deep learning systems.
Categorical Data
Categorical data is a subtype of data that is commonly discovered while extracting and working with datasets. The categorical data is made up of vario
Numerical Data
Another type of data that is commonly found is numerical data, which consists primarily of numbers. It focuses on quantitative data collection. It is
Probability is another important mathematical aspect that is usually required for data analysis, algorithm optimization, and model optimization. This
Introduction to statistics
By aniket verma
● Published At Dec 2021
The objective of this blog is to give an introduction to statistics and its importance in ML.... Keep reading ..
Scales of Measurement
By Mohammad Yawar
● Published At May 2022
This article talks about the different scales of measurement ... Keep reading ..
Variance and standard deviation
By Pradipta Choudhury
● Published At May 2022
This article begins with an introduction to variance and standard deviation, how to calculate them, and their implementation in python.... Keep reading ..
Skewness and Kurtosis
By Mayank Goyal
● Published At Jan 2022
This article will study two statistical concepts,i.e., Skewness and kurtosis, and their applications.... Keep reading ..
KickStart to Machine Learning
Gear to know about libraries and packages that are essential to perform Machine learning techniques and build models. Master code implementation of Numpy and Pandas with Linear Algebra including matrices, vectors, and statistics.
Numpy and Linear Algebra
Learn about the Numpy functions needed to get started with Machine Learning, as well as code snippets and mathematical concepts related to Linear Alge
Pandas and Datasets
Pandas is a very important and interesting Python library for data handling and dataset operations. Learn about its methods, applications, and impleme
SciPy Library
SciPy is a Python library for performing scientific and mathematical calculations. It covers everything from basic linear algebra to advanced calculus
scikit-learn Library
The scikit-learn package is a fascinating and valuable library for machine learning operations. It includes a wide range of supervised and unsupervise
Data Analysis
No Machine Learning model can function without proper analysis, preprocessing, and knowledge of the dataset. Data analysis is one of the most important techniques to perform before performing Machine Learning techniques because it is responsible for the model's better predictions and accuracy.
Data Wrangling
Wrangling data from the internet and converting it into a usable form is one of the first steps in developing Machine Learning models. Learn more abou
Data Visualization
One of the most important aspects of data analysis and extracting required solutions from a dataset through graphs, plots, and visuals is data visuali
Data Preprocessing
Clean and prepared data is a major factor in increasing the efficiency of any Machine Learning model. Data preprocessing allows one to convert any raw
Outliers in Data Analysis
How to handle and decipher data in datasets that are out of the expected range. Learn about outliers, their impact on any Machine Learning model, and
Data Analysis Introduction
By Ritik Arora
● Published At Nov 2021
In this blog, we will learn about the importance and applications of Data Analysis, and then we will learn about tools used for Data Analysis with a primary focus on python.... Keep reading ..
Deep Dive into Machine Learning
Learn about each technique or algorithm used in the Machine Learning domain for building models, solving real-world problems, and delving into one of the most exciting technological domains in the twenty-first century.
Supervised Learning
Learn about Supervised Learning, one of the most widely used Machine Learning techniques. This technique requires trained data and input-output pairs,
Unsupervised Learning
This is in contrast to Supervised Learning, which does not use trained data but has a long list of real-world applications and is widely used by many
Reinforcement Learning
Another machine learning technique that aids in achieving a better and more accurate output is reinforcement learning. This strategy improves a model'
Important Differences
Machine learning has many subcategories and techniques, each with some similarities and differences. It is critical to understand how these algorithms
Applications of Machine Learning
Discover how Machine Learning and its algorithms are used to solve real-world problems. Machine Learning has enabled everything from weather predictio
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