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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.
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
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
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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Top Problems related to Machine Learning