Software Engineering is not limited to developing and implementing applications or tools anymore. With changing technology landscape, software Engineering has come a long way, thanks to the evolving intelligent systems. Machine Learning and Deep Learning Technologies have created avenues to execute tasks efficiently and more intelligently.

In this entire transformations, Machine learning and Deep Learning frameworks have played a huge role allowing innovation to take the centre stage.

In this blog we will unfold following aspects:

• What is Machine Learning?
• Machine Learning Real-World Examples
• How does Machine Learning Help us?
• Types of Machine Learning Algorithms
• Top 11 Machine Learning Frameworks and Some Other Frameworks Too

What is Machine Learning?

So much is said about Machine Learning and the multifaceted benefits it offers. But it is actually difficult to comprehend the advantages unless the fundamentals are laid out clearly. So before going into much detail, let’s understand what is Machine Learning?

Machine Learning is the field that deals with educating the machines to make them able to make decisions like humans.

To define it more specifically,

“Machine Learning is the field that is a subset of Artificial Intelligence, is a process that deals with educating a computer system so that it learns from its own feedback, instead of having to explicitly program it for every task.”

For an instance, when you buy something on Amazon, it starts showing you the similar products related to your last purchase and recommends you some additional products that you may prefer. All these are the tasks possible through Recommendation system that is executed through Machine Learning Algorithms.

Let’s take a look at the real-world examples of Machine Learning!

Machine Learning Real-World Examples

The modern time companies are harnessing the power of Machine Learning in innumerable ways. Some of them are as follow:

1. Financial Services: Financial Institutions use Machine Learning Algorithms for various reasons like Fraud Detection, Stock Market Trading, Risk Analysis, and much more. Banks and other financial bodies identify the fraudulent activities by identifying the faulty transactions, their IDs, locations, log in & log out time, etc. For these institutes it is important to profile the clients which appear to be defaulters so that appropriate actions can be taken at the right time.

Not just this, but investing money in the stock market, anticipating the time of boom, predicting the right funds to put the money in, so that maximum profit can be gained are also achieved with the help of Machine Learning.

2. Healthcare: Healthcare is a research-intensive industry that has been using modern technologies for decades. With Machine Learning and Deep Learning as the latest tools in the kit, Medical Scientists and Pharmaceutical Companies industries have been able to diagnose the patients, find the antibiotics and medicines, research about a disease, and much more.

3. Retail: Retail industry has witnessed tremendous shift of medium and crowd over the years, thanks to ecommerce. Though it has reduced the chain of supply but has increased the importance of smart technologies to take the centre stage. With Machine Learning being the most important factor behind them the ecommerce platforms are able to perform tasks such as product recommendation, maintaining inventory, formulating strategies for pricing and routing, etc.

4. Manufacturing: Manufacturing Industry has been benefitted by Machine Learning in innumerable ways. Whether it is about bringing improvement in the General Processes, Formulating the strategies for New Product Development or it is about quality control through waste reduction and practice of lean manufacturing. Machine Learning helps the companies detect the anomalies and monitor the performance through IoT devices.

5. Transportation: Transport Industry has been using Machine Learning Technologies up to a great extent. With digitization becoming the new medium, smart technologies have found a new way to proliferate. In the era of disruptive innovations like Uber, the features like reflecting the traffic congestions and shortest possible routes to be travelled in real-time, are applicable with the help of machine Learning. Also, finding the optimum price and price surge at particular situations are some of the complex Machine Learning Algorithms working at the back-end. Not only this but companies like Google and Tesla are coming up with driverless cars that are totally based on Machine Learning and Deep Learning Techniques.

6. Oil and Gas: Oil and Gas Industry has also been adopting Machine Learning for processes like Accurate Modeling and Drilling Automation. Machine Learning helps Oil and Gas firms to analyze what is beneath the surface and also monitor the operations.

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How does Machine Learning Help us?

We do not realize but we are heavily dependent on Machine Learning on a daily basis. Machine Learning has infused into our lives to an extent that many of the activities are completely undertaken by these smarter technologies.

Take a look at the ways Machine Learning helps us:

1. Image Recognition: Beginning from identifying objects, suggesting auto-tagging and recommending friends to identifying similar pins, Machine Learning is applied in plenty of ways in the social media platforms and other applications. Some of the examples are Facebook and Pinterest.
2. Virtual Assistance: It is Machine Learning that has made virtual Assistants the intelligent technologies in this decade. Virtual Assistants are able to translate the natural language and respond to the queries asked to them in natural language itself. Whether it is Siri, Cortana, or Alexa, Virtual Assistants have empowered the conventional devices like Smartphones, Smart apps, and Smart Speakers.
3. Email Spam and Malware Filtering: Whenever a suspicious mail arrives it lands on Spam folder. Any mail that violates the filtering rules, Machine Learning Algorithms push them to junk folder. It also saves the users from unnecessary malware attacks.

4. Self-driving Cars: Companies like Google and Tesla are manufacturing Driverless cars that do not require human drivers. This is done by Machine Learning and Deep Learning Algorithms that help Cars to make decisions like humans.
5. Speech Recognition: Google’s voice-based search option works on Machine Learning and Deep Learning Algorithms. Understanding the Natural Language and fetching the web results based on indexed words from the lexicon 
6. Automatic Language Translation: Similar to Speech Recognition, Automatic Language Translation deals with Natural Language Processing and works on Machine Learning Algorithms.

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Types of Machine Learning Algorithms

In order to understand the concept of Machine Learning, one has to know the types of algorithms Machine Learning works on. Following are some of the most widely used Machine Learning Algorithms:

1. Supervised Learning: As the name suggests, Supervised Learning Algorithms involve direct supervision of the operation. In this type of algorithms the developer feeds the sample output along with the desired output that is expected. In Supervised Learning, a known set of input data and known responses to the data are used to train a prediction model for reasonable predictions of unknown input data. Supervised Algorithms include two major processes:
a) Regression Analysis: Regression Analysis is a predictive modeling technique that analyzes the relationship between two variables, dependent and independent, respectively. It is quite a reliable method of identifying the cause and effect of any variable on a dataset.
b) Classification: Classification is a Statistical technique that categorizes the data into various classes. The purpose of performing this analysis is to identify the category into which a particular variable falls. The two major classification techniques are Logistics Regression and Discriminant Analysis.
Some of the prime algorithms used in Supervised Learning Techniques are:
• Linear Regression;
• Logistical Regression;
• Random Forest;
• Gradient Boosted Trees;
• Support Vector Machines (SVM);
• Neural Networks;
• Decision Trees;
• Naive Bayes;
• Nearest Neighbor.

2. Unsupervised Learning: Unlike Supervised Learning, Unsupervised Learning seeks to find hidden trends and patterns in the datasets. Unsupervised Learning does not require direct supervision of the developer. This kind of algorithms feeds on unlabeled data as opposed to Supervised Learning. These are used for detecting patterns, extracting valuable insights, indentifying the structure of the information, etc.

Two major techniques used in Unsupervised Learning Algorithms are:
a) Clustering Analysis: Clustering Analysis is a form of unsupervised learning technique that seeks to analyze the similarities and differences in the attributes of data. It intends to segment the data into meaningful segments on the basis of their internal patterns without the knowledge of group credentials. Here, the group credentials are defined by the similarity or dissimilarity of attributes.
b) Association Analysis: Association Analysis is a technique used to identify hidden relationships within the large datasets. These associations are presented as Association Rules or sets of frequent items.

3. Semi-Supervised Learning: Semi-Supervised Learning is a combination of Supervised and Unsupervised Learning to produce the desired results. Semi-supervised Learning applies classification process for identifying the datasets and clustering process to segment them into different parts.

4. Reinforcement Learning: The Reinforced Learning seeks to train a machine on the basis of past experience and feedback received. Reinforcement Learning seeks to develop a system that is self-sustained which keeps improving itself by constant trails and fails based on the combination of labeled data and fresh inputs. These algorithms work on reward signals that serve as the navigation tool indicating right and wrong actions.

Some of the major Reinforcement learning algorithms are:
• Q-Learning
• Temporal Difference (TD)
• Monte-Carlo Tree Search (MCTS)
• Asynchronous Actor-Critic Agents (A3C)

11 Machine Learning Frameworks

Behind even a simple Machine Learning Models, there are complex programs involved which cannot be built from scratch every time. Therefore, Machine Learning Engineers use various Frameworks that reduce the burden to a great extent.

Machine Learning or Deep Learning Frameworks are those tools, interfaces, or libraries that allow building a model in lesser time without having to write complex lengthy programs each time through optimized components.

By mentioning Deep Learning here, we mean to specify the accuracy and efficiency that comes in with these techniques. All these frameworks serve the purpose both for Machine Learning and Deep Learning. Hence, we will mention both the terminologies in the following discussion.

Some of the top Machine/Deep Learning Frameworks popular among the Developers are discussed below!

TensorFlow

TensorFlow is an Open-Source Machine Learning and Deep Learning Framework owned and maintained by Google Brain Team. It is one of the most popular frameworks among the Machine Learning professionals. TensorFlow is used for Data Integration functions, inputting graphs, SQL tables along with the images.

Features of TensorFlow

1. It is based on JavaScript.
2. Python is the most preferred programming language to be used in TensorFlow.
3. TensorFlow has 4 variants that support different technologies as per necessity.
a) TensorFlow.js– Allows creating Machine Learning models using JavaScript.
b) TensorFlow Lite– Allows creating Machine Learning models on mobile and embedded devices lime Android, iOS, and Raspberry Pi.
c) TensorFlow Extended– Allows creating and deploying End-to-End Machine Learning components for production.
d) Swift for TensorFlow– Allows integrating TensorFlow directly with a general-purpose programming language.

Pros of TensorFlow

1. Ease of Model Building: The model building in TensorFlow is quite easy as it offers multiple levels of abstraction where the suitable one can be chosen as per the need. It also facilitates the developer with more flexibility, intuitive debugging, immediate iteration, and much more.
2. Robust: TensorFlow is a robust framework that lets the developers to build and deploy models irrespective of the platform, device, or language.
3. Speed and Performance: TensorFlow allows developing innovative and complex Machine Learning models without affecting speed and performance. It provides flexibility and control through various APIs.

Cons of TensorFlow

1. Demands Excessive Coding: TensorFlow requires excessive coding and works on static computation graph. Hence, defining the graph is necessary in order to run the calculations.
2. Requires Re-Training the Model: Any changes in the model architecture required the model to be re-trained.
3. Does Not Support Windows: TensorFlow does not support Windows operating system. However, it can be installed through Conda or Python Package Library ‘pip’.

PyTorch

Similar to Google’s TensorFlow, Facebook has also developed a framework called PyTorch, which is based on Torch library. The aim of PyTorch is to accelerate the entire process, beginning with research prototyping to production deployment.

Features of PyTorch

1. Offers Flexibility and Optimization: PyTorch offers flexibility in eager mode and allows seamless transition to graph mode, with the help of TorchScript.
2. Supports Mobile Deployment: PyTorch allows Machine Learning models to be deployed on mobile devices supporting iOS and Android. It facilitates preprocessing and integration required to incorporate Machine Learning in mobile applications.
3. Deployable at Scale: PyTorch can be deployed at scale using TorchServe. It includes features like logging, metrics, multi-model serving and development of RESTful endpoints.

Pros of PyTorch

1. Suited for Both Research and Production: PyTorch is suited for both Research and Production as it has the native support of the execution of collective operations and peer-to-peer communication which is then accessible through Python and C++.
2. Supports Dynamic Graph Updating: PyTorch supports dynamic graph updating which implies that even during the training process changes to the model architecture can be made.
3. Useful for Small Projects: PyTorch is extremely used in the cases of natural language processing, and computer vision. It is also helpful for deploying small projects and prototypes.

Cons of PyTorch

1. No Official Version: PyTorch has still not been able to come up with an official version, which is why it is less supported.
2. Lack of Visualization: PyTorch has limited a visualization tool which is why the developers are bound to depend on the python data visualization tools.
3. Final Learning Development Tool not Present: PyTorch has a major disadvantage that it is not the final learning development tool and requires Python programs to be converted into other models to develop the applications based on real-time.

Caffe

Caffe is an open-source Deep Learning framework developed at BAIR (Berkeley AI Research) for the purpose of image processing. Caffe is known for its speed and compatibility with programming languages.

Features of Caffe

1. Flexible to Mobile Support: Though Caffe is good with deep learning algorithms, but when the developer is more concerned towards platforms like mobile, it can provide flexibility compared to TensorFlow.
2. Suitable for Image Recognition: Caffe was developed for image processing. However it can process almost $60 Million images easily in a day making it best suited for Image Recognition.
3. Speed is the USP: Caffe spends around 1ms/image for interference and 4ms/image for learning. This clearly hints how speed is the USP of Caffe.

Pros of Caffe

1. Supports Various Interfaces: Caffe supports many interfaces like C, C++, Python, MATLAB, as well as the Command Line interfaces (CLI). Hence it is compatible for everyone.
2. Ease of Training: Caffe has a robust architecture which also allows training the neural networks without having to write lengthy codes which accelerates active development.

Cons of Caffe

1. C++ is a Must: In order to work in Caffe, the developer needs to be familiar with C++.
2. Granular Networking Absent: The Granular Network layers are absent in Caffe, as opposed to TensorFlow and Microsoft Cognitive Toolkit.

Learn the fundamentals of Data Science, Machine Learning and Data Analytics in the blog: Data Science Vs Machine Learning Vs Data Analytics!

Scikit-learn

Scikit-Learn in Python is an open-source library that is used for developing Machine Learning models. Developed as part of Google’s summer of code project, Scikit-Learn contains an exhaustive set of tools used for statistical modeling and Machine Learning. It is a community-driven where anyone can contribute. Some of the top sponsors of Scikit-Learn are Microsoft, Intel, and NVIDIA. This library is written in Python and built upon SciPy, NumPy, and Matplotlib.

Features of Scikit-Learn

1. Supports Most of Supervised Learning Algorithms: Scikit-Learn supports most of the supervised Learning Algorithms. Whether it is Linear Regression, Decision Trees, Support Vector Machines, or Bayesian Methods, Scikit-Learn supports all of them. It also supports Unsupervised Learning Algorithms like Factoring, Cluster Analysis, Principal Component Analysis, and Unsupervised Neural Networks.
2. Feature Extraction and Cross Validation: Scikit-Learn allows extraction of features of texts as well as images. It also allows developers to verify the accuracy and validity of Supervised Models on unseen data.
3. Clustering and Ensemble Methods: It allows the predictions of several supervised models to be combined. Also, Scikit-Learn allows the unlabeled data to be grouped.

Pros of Scikit-Learn

1. Supports Real-World Scenarios: The Scikit-Learn library is versatile in nature and helps perform real-world applications like analyzing consumer behavior.
2. Includes Useful Utilities: It includes a collection of utilities that are highly useful for splitting data, computing statistical operations, and performing matrix operations.
3. Easy to Use: This library is easy to use and it integrates pretty well with NumPy.

Cons of Scikit-Learn

1. Lack of Flexibility: The model API for this library is not flexible enough and hence causes biasness.
2. Slow Speed: Does not use hardware acceleration which makes it slow at times.
3. Unfit for Deep Learning: Scikit-Learn is not the best choice for Deep Learning.

Theano

Theano is a Python Library that is developed at LISA lab to help develop the machine learning algorithms quickly and efficiently. It allows evaluating mathematical operations and hence is a perfect choice for Deep Learning operations. Theano is known for its high speed and compatibility with large amounts of data.

Features of Theano

1. Automatic Differentiation: A developer has to implement only the forward part of the model and Theano Library will automatically calculate the gradients at various points.
2. Speed and Stability Optimization: Theano internally reorganizes and optimizes the computations to make them execute faster. For this purpose, Theano also tries to compile some of the operations in C.
3. Tightly Coupled with NumPy: Theano is tightly integrated with NumPy.

Pros of Theano

1. Supports both CPU and GPU: Theano works better on GPU than on CPU. The same code can be run on both CPU and GPU and Theano automatically figures out which part of the code to run on GPU.
2. Writes Customized Codes in C: Theano can generate customized codes in C for many mathematical operations.
3. Combination of Computer Algebra System and Compiler: Theano typically is a combination of Computer Algebra System and an Optimizing Compiler which is specifically useful in the cases where complex mathematical expressions are evaluated repeatedly.

Cons of Theano

1. Slow Speed: Theano takes time to run larger models and complex symbolic graphs.
2. Difficult to learn: Theano has a steep learning curve and hence it is difficult to learn.
3. Low Level: Theano uses low level APIs.

Amazon Machine Learning (Amazon ML)

Amazon Machine Learning (Amazon ML) is a cloud-based platform that allows the developers to develop machine learning models in a robust manner. It reads data through Amazon S3, Redshift, and RDS, which is then visualized through AWS Management Console and Amazon Machine Learning API.

Features of Amazon ML

1. Constructs Mathematical Models: Amazon ML algorithms discover the patterns in data and construct mathematical models using this data, which are used for making predictions.
2. Synchronizes Previous Data: It synchronizes previous data and uses it later to provide necessary information to the user.
3. Not Required to learn Complex ML: The visualization tools and wizards in-built in Amazon ML guide the developer through the process of creating Machine Learning models without having to learn complex ML algorithms.

Pros of Amazon ML

1. Quick Application Development: Amazon ML helps the developers to build Machine Learning applications quickly that are used for significant purposes like Fraud Detection, Demand Forecasting, Predictive Customer Support, etc.
2. Easy Prediction: As soon as the models are ready, Amazon ML obtains predictions easily for the applications with the help of simple APIs, without having to maintain any infrastructure.
3. Comprehensive Analytics: Amazon ML offers a range of services for Data Analytics, Data Storage, Business Intelligence, Batch Processing, Stream Processing, and Data Progress Orchestration.

Cons of Amazon ML

1. Limited variables in a Schema: Amazon ML does not support a schema that has more than 1000 variables.
2. File Size to be Limited: The data file size requires being limited to ensure speedy execution, else it gets failed.
3. Requires Considerable Size of Memory: Amazon ML requires 10 GB for real-time prediction which is a huge amount of memory.

RapidMiner

RapidMiner is a Data Science Platform that allows performing Data Preparation, Machine Learning, Deep Learning, Text Mining, and Predictive Analytics. It is extensively used for business and commercial purposes like training, research, education, application development, rapid prototyping, etc. It is based on an open core model. It is written in Java Programming Language.

Features of RapidMiner

1. Client-Server Model: RapidMiner works on a Client-Server Model, where the Server can be on-premise (Public or Private) or on Cloud.
2. Template-based Framework: It provides solution with the help of template-based frameworks that speed up the delivery and reduce the error, without having to write the code.
3. Facilitates Data Mining and Machine Learning: RapidMiner facilitates Data Mining and Machine Learning Procedures along with Data Loading and ETL, Data Preprocessing and Visualization, Predictive Analytics and Statistical Modeling, and Deployment.

Pros of RapidMiner

1. Easy to Use Visual Environment: RapidMiner consists of easy to use visual environment to build analytical processes, such as Graphical Design Environment and Visual Representation with Annotations.
2. Supports Scripting Environments: It also supports scripting environments like R, or Groovy for ultimate extensibility.
3. GUI for Workflows: It facilitates the developers with a GUI to design and execute Analytical Workflows, which are called ‘Processes’ that consist of multiple operators.

Cons of RapidMiner

1. Difficult for Coders: It isbased on flow programming which does not allow to use coding, despite having coding modules in Java and Python.
2. Confusing: Includes a lot of modules, which is confusing at times.
3. Limited Scope: RapidMiner offers limited use cases.

Apache Mahout

Apache Mahout is an open-source scalable library for machine learning algorithms. It was initially a subproject of Apache Lucene, which is a high performance text search engine library, but later on it became one of the top Apache projects. Apache Mahout is usually used with Apache Hadoop and Apache Hive.

Features of RapidMiner

1. Supports Distributed Network: Apache Mahout works properly on distributed environment as it is built on top of Hadoop. Because of this, it is easy to scale using Mahout on Cloud.
2. Ready-to-Use Framework: Apache Mahout provides a ready-to-Use framework for performing data mining tasks on huge volumes of data.
3. Supports MapReduce Clustering Implementations: RapidMiner consists of MapReduce enables clustering implementations like K-means, Fuzzy K-Means, Canopy, Mean Shift, etc.

Pros of RapidMiner

1. Fast Platform: Applications can be executed and analyzed in a much faster way using Mahout.
2. Great Community: Mahout has a huge community support which is rarely found for other open-source machine learning libraries.
3. In-built fitness for Distributed Environment: Apache Mahout has in-built fitness for Distributed Environment which helps in evolutionary programming.

Cons of RapidMiner

1. Poor Visualization: Mahout lacks in proper data visualization support and also in case of scientific libraries.
2. Legacy Dependencies: Mahout consists of legacy dependencies which may create troubles in case of running Spark jobs.

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KNIME

KNIME is an open Data Analytics platform suitable for small businesses. It facilitates analytics through drag-and-drop options. It supports machine learning, statistical calculations, ETL. Various companies use KNIME to understand data and design data science workflows.

Features of KNIME

1. GUI for Development: KNIME provides a GUI for development process, where a user just has to define the workflow between the predefined nodes. Therefore no programming knowledge is required for working in KNIME.
2. Nodes for Tasks: KNIME facilitates the developers with predefined components that are called nodes for performing various tasks, such as reading data, fetching data in various formats, etc.
3. Supports all the OSs: KNIME supports all the three major OS, i.e., Windows, Linux, MacOS.

Pros of KNIME

1. Free of Cost: KNIME is absolutely free of cost platform that provides plethora of free extensions for executing every single process.
2. Provides Extensions and Integrations: KNIME provides a number of community provided extensions and integrations that greatly enrich the software functionalities, allowing advanced machine learning algorithms to be developed.
3. Automating Workflow Deployment: The higher tiers of KNIME server allow the developers to use REST API and Web Portal that in turn automate the workflow deployment and execute workflows remotely.

Cons of KNIME

1. Inefficient Visualization: The visualization in KNIME is not up to the mark. Other BI tools like Power BI and Tableau have better visualization functionalities.
2. Difficult for Non-Technical People: KNIME is difficult for Non-Technical people as it requires the knowledge of R or Python for statistical analysis.
3. Not coupled with Jupyter: KNIME is not integrated with Jupyter Notebook. Also, the tools used for writing and developing script are difficult to use and they do not have many features.

Weka

Weka is an open-source collection of machine learning algorithms used for data mining tasks. This platform provides tools for data reprocessing, implementations of ML algorithms, and visualization to develop Machine Learning techniques to real-world situations. These algorithms can be applied directly to a dataset or can be called from a Java code.

Features of Weka

1. Based on Java: Weka is written in Java and provides a API that is well documented and allows integration into own applications.
2. GUI-Based Platform: It is a GUI-based platform that allows completing Machine Learning Projects without having to write code.
3. Command Line Interface: All the features of the platform can be used from Command Line Interface, which is useful for write scripts for large jobs.

Pros of Weka

1. No Programming Required: Weka is extremely helpful for the beginners as they can develop machine learning models using GUI without having to write codes.
2. Supports Main Platforms: Weka supports three major platforms, I.e., Windows, OS X, and Linux.
3. Large Number of Tools: Weka has a large number of Classification and Regression Tools.

Cons of Weka

1. Less Active Community: Compared to R Programming, Weka has less active community.
2. Not fit for Unprepared Data: Weka is good when the data is clean and prepared which is the case in real-life. Hence it is not good for data that is unprepared.
3. Cannot Handle Large Datasets: Weka is certainly not suitable for large datasets.

H20.ai

H2O.ai is a collection of open source AI and Machine Learning products that works to promote the use of AI in the industries. They have a large community of data scientists and organizations to accelerate the growth and adoption of AI in various sectors.

Features of H2O.ai

The product suite of H2O.ai includes:

1. H2O: It is an open-source, in-memory, distributed platform allowing the developers to build Machine Learning Models.
2. Deep Water: It includes H2O along with a tight integration wit tensorFlow, Caffe, etc.
3. Sparkling Water: It includes H2O along with a tighter integration with Spark.
4. Steam: An enterprise version where scientists can train and deloy Machine Learning Models and make that available through APIs to be integrated into applications.
5. Driverless AI: Allows an enterprise’s nontechnical employees prepare data, calibrate parameters, and determine optimal algorithms to tackle particular business problems using ML.

Pros of H2O.ai

1. Speed: This is a fast platform that supports distributed environment which stores the data across clusters and in memory in a compressed columnar format, which allows reading the data simultaneously.
2. Identifies the Data: It has built-in intelligence to identify the schema for the incoming dataset and supports data ingest from various sources and formats.
3. Strong Performer: Most of the AI frameworks can be used for automation work and it is a strong performer in Predictive Analytics and Machine Learning.

Cons of H2O.ai

1. Not User-Friendly: H2O.ai is not user-friendly.
2. Not Well Documented: It is not maintained properly. The platform is not well documented, and it becomes really difficult to build something on top of it.

Other Frameworks

There are some other frameworks which are also used widely by the companies. Take a look!

1. Spark ML lib: It is a Machine Learning component of Apache Spark. The computations in Apache Spark can be scaled massively, which is why it is good for Machine Learning Algorithms.
2. Keras: It is built on TensorFlow and requires less coding. Keras is designed for fast experimentation and is perfectly suitable for Deep Learning.
3. Shogun: It is an open-source Machine Learning Library which provides a wide collection of efficient Machine Learning methods.
4. Azure ML Studio: It is a cloud-based Machine Learning framework that saves time and cost by making the Application Development easy. It allows users to connect with sources like Hive Query, Azure SQL, On-premise data sources, etc.
5. Microsoft Cognitive Toolkit: It is Microsoft product to handle deep learning and can process unstructured data for machine learning models also. It is customizable and supports multi-machine back-ends.
6. MXNet: It is an open-source deep learning software framework which is used to train and deploy Deep Neural Networks. It is a scalable framework that allows fast training of the models and supports multiple languages like C++, Python, Java, MATLAB, etc.
7. Deeplearning4j: It is open-source software which is relied on Java and also includes an API for Scala. It is widely used in commercial and academic applications. It has an extensive set of visualization tools.
8. Apache Singa: It is a general distributed Deep Learning platform to train big deep learning models over large datasets. It supports synchronous, asynchronous, and hybrid training frameworks.

Endnote:There are a plethora of opportunities for aspirants who wish to make it big in Machine Learning domain. With changing technology dynamics, the requirement for skilled workforce is imminent, creating a huge pool of high-paying jobs for aspirants

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