The Boom of Machine Learning Frameworks is already in a way and it’s making a lot of progress in the Technological field and according to an IT Report.
Machine Learning and AI is going to create around 3 million Jobs by 2020 and this extreme growth has led to the evolution of various Machine Learning Tasks Using Frameworks.
In this Short Article Trip, we will cover the following topics:
- What is Machine Learning?
- Prerequisites for Machine Learning
- Machine Learning Frameworks
What is Machine Learning?
Machine Learning – a type of system that can learn from example through self-improvement and without being explicitly coded set down with the help of a programmer.
It is also a type of Artificial Intelligence. That allows software applications to learn from the available data and become more accurate in predicting outcomes without any human involvement.
It is a concept which allows the machine to learn from examples and previous experience. To make this happen we have a lot of Machine Learning Frameworks available today. We can say Machine Learning algorithms – an evolution (upgraded version ?) of normal algorithms. They make your programs smarter, simpler, less complex, easy debugging. By allowing them to automatically learn from the data you provide while coding.
Prerequisites for Machine Learning
If you’re a beginner who’s interested in starting with Machine Learning, it’s important you to know the some mathematical prerequisites you should aware off
To get started with Machine Learning Demands with the following concepts:
Programming languages
Like a compulsion, you should learn a programming language before you commence with ML programming languages – Python, R,Java and Javascript.
Python and R
Python and R languages allows the use for implementing the whole edge machine learning process both languages provide very huge built in libraries over the world. around 8.5 million people Ply python – primary language for the machine learning. Effortless to implement compared to other programming languages
Java and JavaScript
As we know earlier that mean before python getting into boom java is the most used language in the IT world a person who is from the java background need not learn new trending language for machine learning as Java also provides very huge third party libraries for example JAVAML is one inbuilt library for ML
Mathematics
Linear algebra
To perform or transform various operations on the Datasets Linear algebra like transformations, matrices, and vectors shall require.
Calculus
If you take any machine learning algorithm, you should definitely come across the calculus part as it plays a major role in mathematics.
As some of the datasets will be having multiple features to build the machine learning model then multivariable calculus plays a major role to build an efficient machine learning model apart from the calculus,integration and differentiation are also must to learn for machine learning.
Statistics
In the statistical part we will exercise using two types of tools. Like descriptive and inferential statistics, where descriptive – used to transform raw data. some useful information and inferential statistics – needful for retrieving imperative information from the sample data.
Probability
In fact we were able to say “Probability is BEDROCK for Machine Learning “. As probability helps is predicting the likelihood of occurrences.
so, it can be used for predicting the class membership for the classification models. Many algorithms also design and employ- Naive Bayes (Probability).
Machine Learning Frameworks
A Machine Learning Framework is an interface, library, Guide or tool which provides Generic Functionality. It allows developers to build machine learning models easily, without getting into the complexity depth of the underlying algorithms. Now let’s discuss the Top Best Machine Learning Frameworks in detail:
Sci-Kit Learn
Scikit-learn is well-known and mostly used and it is a free software machine learning library?. It features algorithms like Support Vector Machine (SVM), random forests and K-neighbours .It is Preferable for supervised and unsupervised learning calculations, choice trees, bunching, k-implies, Precedents implement direct and calculated relapses, etc.
Scikit-learn involves a lot of calculations for Data Mining assignments and Regular AI.
Torch and Pytorch
Torch and Pytorch is an open-source machine learning libraries , a scientific computing framework, and a script language based on the LuaJIT programming language.
However, It provides a wide range of algorithms for deep learning, and uses the scripting language LuaJIT?.
Whereas Torch provides Lua wrappers to the THHN library while Pytorch provides Python wrappers for the same.
Shogun(Toolbox)
Shogun is a free and open-source machine learning library written in C++. Whereas, It supports numerous data structures and algorithms and for machine learning problems.
So, It supports varied algorithms like Support Vector machines, Dimensionality reduction algorithms, Support Vector Regression, K-Nearest Neighbors.
TensorFlow
Tensorflow – one of the most popular frameworks today which is developed by Google’s Brain team.
it bundles both machine learning and deep learning (Neural Networks) algorithms It is also a free open source machine learning software library used for numerical computations using data flow graphs.
Amazon Machine Learning
Amazon Machine Learning (Amazon ML) provides visualization tools, is a robust, cloud-based service that makes it easy for developers of all skill levels to use machine learning technology. However, it helps in going through process of machine learning models without facing any complexity in learning Machine learning algorithms.
Google Cloud ML Engine
Google Cloud ML Engine is a suite of machine learning products that helps developers and data scientists to train high-quality models specific to their business needs. It also offers Training and prediction services which can be used both at a time or separately.
Additionally, utilized by many enterprises to solve problems in models like clouds in satellite images, ensuring food safety, responding four times faster to customer emails, etc.
Written By: Srikanth Bussa
reviewed By: Viswanadh
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