An important method of data analysis that helps in automating the analytical model building is known as Machine Learning.
Machine Learning, abbreviated as ML, is a branch of Artificial Intelligence; and is based upon the idea that systems can make decisions, identify patterns, and learn from data with minimum human intervention.
History and Evolution of Machine Learning
1950 –The ‘Turing Test’ is created by Alan Turing to determine whether a computer has real intelligence or not.
A PC must be able to fool a human being into believing that it is also a human being, to pass a test.
1952 – The 1st computer program was written by Arthur Samuel.
1957 –Rosenblatt designed the 1st neural network for PCs that is able to simulate the thought process of the brain of a human being.
1967 – The ‘nearest neighbour’ algorithm was written to allow the PCs a very basic pattern recognition.
1979 –‘Stanford Cart’ was invented by the students at Stanford University that is able to navigate obstacles in a room on its own.
1981 –Gerald Dejong introduced the important concept of EBL, i.e. Explanation Based Learning.
In EBL, a PC analyses training information and creates a rule that it can generally follow by discarding unimportant information.
1985 – NetTalk was invented by Terry Sejnowski, which was aimed to pronounce words in the same manner a child/baby does.
1990s – Work on ML changed from a knowledge-driven approach to a more data-driven approach.
Scientists in this era started creating programs for PCs to analyse massive amounts of information and ‘learn’ or draw conclusions – from the results.
1997 –The chess world champion is defeated by IBM’s Deep Blue.
2006 –‘Deep Learning’ term was coined by Geoffrey Hinton for explaining new algorithms that allow computers distinguish and ‘see’ texts and objects in videos and images.
2010 –The Microsoft Kinect was able to track down twenty human being features at a rate of thirty times per sec.
It allowed individuals to interact with the PC via gestures and movements.
2011 – Watson of IBM defeats its human competitors at Jeopardy.
2011 – Google Brain is developed and its neural network is able to categorize and discover objects pretty much in the same manner a cat does.
2012 –X Lab of Google develop a ML algorithm which is itself capable for browsing videos of YouTube and can easily and quickly identify the YouTube videos that contain cats.
2014 –DeepFace is developed by Facebook which is a software algo that allows a person to verify or recognize individuals on photographs to the same level as human beings can do.
2015 –Amazon launches its own ML platform this year.
2015 – Distributed Machine Learning Toolkit is created by Microsoft, that enables the efficient distribution of ML problems across multiple PCs.
2015 – Over three thousand Robotics and AI researchers, who were endorsed by Steve Wozniak, Elon Musk, and Stephen Hawking, signed an open letter warning of the danger of autonomous weapons that engage and select targets without human beings intervention.
2016 –A professional player at the Chinese board game ‘Go’ is defeated by the AI algo of Google.
This Chinese board game is many times harder as compared to Chess and considered as world’s most complex board game.
Google Deepmind developed AlphaGo algo and it managed to win 5 out of 5 games in the Go competition.
Why Machine Learning?
The nearby limitless quantity of affordable data storage, available data, and the growth of more powerful and less expensive processing has propelled the growth of machine learning.
Now, there are several industries are developing more and more robust ML models capable of analysing more and bigger complex information while delivering more accurate, and faster results on vast scales.
ML tools enable industries to more quickly identify potential risks and profitable opportunities.
The practical applications of ML drive business results that can dramatically effect an industry’s bottom line.
New techniques in this field are expanding and evolving rapidly the application of ML to nearly limitless possibilities.
Companies which depend on large quantities of information – and require a system to analyse it accurately and efficiently, have embraced ML as the best way to plan, strategize, and build models.
Industries that use Machine Learning –
1 Healthcare
The proliferation of wearable devices and sensors which monitor everything from steps and pulse rates walked to sugar and oxygen levels and even patterns of sleeping have developed a significant amount of information which enables doctors to access the health of their patients in real-time.
1 new ML algo detects cancerous tumouron mammograms; another analyse retinal photographs to diagnose diabetic retinopathy, a third can identify skin cancer.
2 Government
Systems that use ML enable the officers to use information to adapt to rapidly changing scenarios and to predict potential future situations.
ML can help improve cyber intelligence and cybersecurity, reduce failure rates, predictive maintenance, logistics management, optimize operational preparedness, and support counterterrorism efforts.
3 Marketing and Sales
ML is even revolutionizing the marketing industry as several companies have successfully implemented ML and AI (Artificial Intelligence) to enhance and increase consumer satisfaction by more than ten per cent.
According to Forbes, fifty-seven per cent of enterprise executives believe that the most significant growth advantages of AI and ML will be improving consumer support and experiences.
Social media websites and E-commerce use ML to analyse your search and buying history –and based on your past habits, make recommendations on other items to purchase.
Several experts theorize that the future of retail will be driven by ML and AI as deep learning business applications become even more adept at using, analysing, and capturing information to personalize people’s shopping experiences and create customized targeted campaigns of marketing.
4 Transportation
Accuracy and efficiency are 2 key points for profitability within the transportation sector, so is the ability to mitigate and predict potential problems.
Modelling functions and data analysis of machine learning perfectly dovetail with businesses within the freight transport, public transportation, and delivery sectors.
ML uses algo for finding factors that negatively and positively impact the success of supply chain, making ML an important component within supply chain management.
Within logistics, ML facilities the schedulers’ ability for optimizing QC processes, routing, rating, that improves efficiency and saves money.
The ability of ML to analyse 1000s of data points simultaneously and apply algo more easily and quickly than any human being enables ML for solving problems that individuals haven’t yet identified.
5 Financial Services
The insights that are provided by ML in financial services industry allow investors to know when to trade or to identify new opportunities.
Data mining informs cyber surveillance and pinpoints high-risk clients to mitigate and find signs of fraud. ML can help assess risk for insurance and loans underwriting and calibrate financial portfolios.
The future of ML and AI in financial services market include an ability to analyse stock market movement and to evaluate hedge funds to make financial recommendations.
ML may render security questions, passwords, and usernames by taking anomaly detection to the next level: voice or facial recognition, or other biometric data.
6 Oil and Gas
AI and ML are already working to streamline oil distribution to shrink costs and increase efficiency, predict refinery sensor failures, analyse mineral deposits in ground, and find new energy sources.
ML id revolutionary the oil and gas industry with its drill floor automation, reservoir modelling, and case-based reasoning, too. And above all, ML is making its efforts to make this dangerous industry safer.
7 Manufacturing
ML is no stranger to the vast manufacturing industry, either.
Its applications in this sector are about accomplishing the target of improving operations from conceptualization to the final delivery, significantly increasing inventory turn, improving predictive maintenance, and reducing error rates.
Unlike the transportation industry, ML has helped several companies improve logistical solutions that include inventory management, supply chain, and assets.
ML also plays a vital role to enhance OEE i.e. overall equipment effectiveness by measuring the quality, performance, and availability of assembly equipment.
Types of Machine Learning
As with any other thing in this world, there are many methods to train ML algo, each with their disadvantages and advantages.
We must first look at what kind of information each type of machine learning ingest for understanding pros and cons of them.
There are 2 kinds of data – unlabeled data and labeled data in machine learning.
Labeled data has both the output and input parameters in an entirely machine readable pattern, but to begin with, it requires a lot of human beings to label the data.
Unlabeled data is the data that has only 0 or 1 parameters in a machine readable form.
This requires more complex solutions but negates the need for human being labor.
There are some kinds of ML algo that are used in very specific use cases, but there are mainly 3 main types of machine learning which are used today.
1 Supervised Learning
One of the most basic types of machine learning is supervised learning.
The machine learning is trained on labeled data in this type of machine learning.
Supervised learning is exceptionally powerful when used in the correct circumstances, even though the data needs to be labeled accurately for this method to work appropriately.
The machine learning algo is given a small training dataset to begin with, in this type of machine learning.
This training dataset serves to give the algo a basic idea of the data points, solution, and problem to be dealt with; and this is a smaller set of the larger dataset.
The training dataset provides the algo with the labeled parameters needed for the problem, and it is also very similar to the final dataset with respect to characteristics.
The machine learning algo then finds the relationship between the parameters given, and establishes a cause and effect relationship between the variables that are present in the dataset.
The machine learning algo has an idea of the relationship between the output and the input and how the data works, at the end of the training.
The solution is then used for the final dataset, that it learns pretty much the same way as the training dataset.
This essentially means that the supervised machine learning algo will continue to improve even after they are deployed.
It then discovers new relationships and patterns as it trains itself on more new data.
2 Unsupervised Learning
This type of machine learning has the benefit of working with unlabeled data.
This implies that the labor of human beings isn’t required for making the dataset machine readable, which allow much bigger datasets to be worked on the program.
The labels allow the ML algo to find the exact nature of the relationship between any 2 data points in supervised learning.
But, unsupervised learning results in the creation of hidden structures as it does not have labels to work off of.
Relationships between several data points are established in an abstract way, with 0 input needed from humans.
The development and creation of hidden structures is what make the unsupervised learning algo more and more versatile.
This type of machine learning algo can adapt to the data by dynamically changing hidden structures, instead of a set and defined problem statement.
Unsupervised machine learning allows more post deployment development as compared to the supervised learning algo.
3 Reinforcement learning
This type of machine learning takes inspiration directly from how humans learn from data in their real life.
Reinforcement learning features an algo that learns from new scenarios using a trial and error method.
Non-favorable outputs are punished or discouraged and favorable outputs are reinforced or encouraged.
This type of machine learning works by putting the algo in a work environment with a reward system and an interpreter on the basis of the psychological concept of conditioning.
The result of the output is given to the interpreter in each iteration of the algo, that decides if the outcome is favorable or not.
The interpreter reinforces the solution by providing a reward to the algo in case the program finds the right solution.
In unfavourable case, the algo is forced to reiterate unless it is able to find a better solution.
Therefore, the reward system is directly tied to the effectiveness of the result in most of the cases.
In typical reinforcement learning use cases, the solution is not an absolute value such as finding the shortest distance i.e. displacement between 2 points on the map.
Instead, it is expressed in a % value as it takes on a score of effectiveness.
The higher this %, the more reward is given to such algo. Therefore, this program is suitable for giving out the best possible solution for the best possible reward.
Machine Learning Algorithms
When you are crunching information to model business decisions, you are most probably using unsupervised learning methods and supervised learning methods.
One of the most hot topics at the moment is semi-supervised learning methods in an area of photograph classification where there are big datasets with less labeled examples.
Mostly, algo are grouped in terms of their similar function. Let’s say, for instance, neural network and tree based methods inspired methods.
My opinion is that it is the most apt way of grouping algo and this same approach will be used in this article. But as anything in this world isn’t perfect, so is this method.
There are many algo that can easily fit into categories such as Learning Vector Quantization, which is both an instance-based method as well as a neural network inspired method.
There are also many categories present that have the same name which is used to describe the problem and the class of algorithm such as clustering or regression.
1 Regression Algorithms
Regression algorithms are concerned with modelling the relationships between different variables which is refined again and again by using an error measurement in the predictions made by the model.
These kind of methods have been co-opted into statistical machine learning and are a workhorse of statistics.
This may sound as confusing because we can use regression methods to refer to the class of algorithm and the class of problem.
The most famous regression algo are –
- LOESS i.e. Locally Estimated Scatterplot Smoothing
- MARS i.e. Multivariate Adaptive Regression Splines
- Stepwise regression method
- Logistic regression method
- Linear regression method
- OLSR i.e. Ordinary Least Squares Regression
2 Instance – based Algo
The instance-based learning is a model on problem with examples of training data which are deemed required or important to the model.
This method generally compare new data to the database using a similarity measure for finding the best match and making a prediction which helps in building up a database of example data.
Therefore, for this reason, this method is also known as memory-based learning and winner-take-all method.
The main focus in this model is put on the similarity measures that are used between instances and the representation of the stored instances.
The most famous instance-based algo are –
- SVM i.e. Support Vector Machines
- LWL i.e. Locally Weighted Learning
- SOM i.e. Self-Organising Map
- LVQ i.e. Learning Vector Quantization
- kNN i.e. k-Nearest Neighbour
3 Regularization Algorithm
The regularization algo is an extension made to other method which penalizes models that are highly complex, and favors models that are simpler and better at generalizing.
The most famous regularization algo are –
- LARS i.e. Least-angle regression
- Elastic Net
- LASSO i.e. Least Absolute Shrinkage and Selection Operator
- Ridge Regression
4 Decision Tree Algorithm
This algorithm construct a decisions model that is based on the actual values of attributes in the information and data.
Decision fork under a prediction decision in tree structures is made for a given record.
Such decision trees are trained on information for regression and classification problems.
Decision trees are a big favorite in machine learning as they are often accurate and fast.
The most famous decision tree algo are –
- Conditional Decision Trees
- M5
- Decision Stump
- CHAID i.e. Chi-squared automatic interaction decision
- C5.0 and C4.5 – These are different versions of a powerful approach.
- ID3 i.e. Iterative Dichotomiser 3
- CART i.e. Classification and Regression Tree
5 Bayesian Algorithm
Bayesian algo are those which explicitly apply Bayes’ Theorem such as regression and classification.
The most famous Bayesian algo are –
- BN i.e. Bayesian Network
- BBN i.e. Bayesian Belief Network
- AODE i.e. Averaged One Dependence Estimators
- Multinomial Naive Bayes
- Gaussian Naive Bayes
- Naive Bayes
6 Clustering Algorithm
Clustering algo such as regression describes the class of methods and the class of problems.
Such methods are generally organized by the approaches of modelling like hierarchical and centroid-based.
All such methods are attentive for using the inherent structures that are present in the information or data to best organize such data into groups of max commonality.
The most famous type of clustering algo are –
- Hierarchical Clustering
- EM i.e. Expectation Maximisation
- k – Medians
- k – Means
7 Association Rule Learning Algorithm
These extract rules which best explain observed relationships between the different variables present in data.
Such rules can discover crucial and commercial useful associations in big multi dimensional data sets which can be easily exploited by an institution.
The most famous association rule learning algo are –
- Eclat algo
- Apriori algo
8 Artificial Neural Network Algorithm
Such algo are inspired by the function and the structure of biological neural networks.
These are a class of pattern matching which are mostly used for classification and regression problems but are also a massive subfield consists of 100s of variations and algorithms for all types of problems.
The most famous types of artificial neural network algo are –
- RBFN i.e. Radial Basis Function Network
- Hopfield Network
- Stochastic Gradient Descent
- Back – Propagation
- MLP i.e. Multilayer Perceptron
- Perceptron
9 Deep Learning Algorithm
The modern update to artificial neural network which exploit massive cheap computation are deep learning methods.
These are concerned with building more complex and much larger neural networks and different methods are concerned with very big data sets of labelled analogue info, such as video, audio, text, and images.
The most famous types of deep learning algo are –
- DBN i.e. Deep Belief Network
- DBM i.e. Deep Boltzmann Machine
- Stacked Auto – Encoders
- RNNs – Recurrent Neural Networks
- CNN – Convolutional Neural Network
10 Dimensionality Reduction Algorithm
Dimensionality Reduction Algorithm seek and exploit the inherent structure that is present in the information and data, but if we talk about this case, an unsupervised way to describe or summarize information using less data.
This can prove beneficial in simplifying data or visualizing dimensional data that can be used in a supervised learning method.
Many such methods can be used in regression and classification.
- FDA i.e. Flexible Discrimant Analysis
- QDA i.e. Quadratic Discriminant Analysis
- MDA i.e. Mixture Discriminant Analysis
- LDA i.e. Linear Discrimant Analysis
- Projection Pursuit
- MDS i.e. Multidimensional Scaling
- Sammon Mapping
- PLSR i.e. Partial Least SquaresRegression
- PCR i.e. Principal Component Regression
- PCA i.e. Principal Component Analysis
11 Ensemble Algorithm
Ensemble algo works on the models comprised of many weaker models whose predictions are combined in some way for making the complete prediction and are independently trained.
The many famous types of ensemble algo are –
- Random Forest
- GBRT i.e. Gradient Boosted Regression Trees
- GBM i.e. Gradient Boosting Machines
- Stacked Generalization
- Weighted Average (Blending)
- AdaBoost
- Bootstrapped Aggregation (Bagging)
- Boosting
Applications of Machine Learning
1 Image Recognition
One of the most common and prominent application of ML in today’s world is Image Recognition.
It is used for identifying digital images, places, persons, identify objects etc.
The most famous use case of face detection and image recognition is Automatic friend tagging suggestion.
FB provides us with this feature that whenever we upload a picture with our friends, it automatically get a suggestion for tagging for the people in the picture.
The technology behind this is machine learning’s recognition algo and face detection.
This technique of FB’s image recognition is based on its project ‘Deep Learning’, that is responsible for person identification and face recognition in the image.
2 Speech recognition
While you are using the search engine Google, we always get an option of ‘Search by Google’.
This feature comes under speech recognition, and is a very famous application of machine learning.
The process of converting instructions of voice to text, is known as ‘Computer Speech Recognition’ and ‘Speech to text’.
These days ML algo are used in several applications of speech recognition.
Alexa, Cortana, Siri and Google Assistant are using this technology for following the voice instructions.
3 Traffic Prediction
Whenever we want to visit a new place, all of us take help of the Google Maps, that helps us in showing the shortest route to your destination and predicts the traffic conditions of various routes.
Traffic conditions such as whether the traffic is heavily congested, slow-moving, is the traffic cleared; are shown with the help of 2 methods –
- The average time that has been taken by people on the same route on the past days and at the same time.
- Real time location of the vehicle form google map sensors and apps.
Every person who is using this app is helping Google Map make it better.
It takes data from the users and sends it back to the database for improving its database.
4 Product recommendations
ML is widely used by several entertainment and e-commerce companies like Netflix and Amazon for product recommendations to the users.
You must have seen that when you search for a product on E-Commerce websites such as Amazon, then you start seeing similar ads of products from different companies on the browser you are using for surfing; and this is because of ML.
The search engines understand the interest of the users using several ML algo; and then they suggest the products as per consumer interest.
Similarly, we find recommendations for movies, TV shows, and web series when we view a movie, TV show, and Web series belonging to a particular genre.
5 Self-driving cars
One of the most interesting applications of ML are self-driving cars and ML plays a big, big role in this aspect.
Tesla, the company of Elon Musk is working on the self-driving cars and are using the unsupervised way of learning for training cars to detect objects and people while driving.
6 Malware Filtering and Email Spam
Whenever we receive an email, it is automatically filtered as spam, normal, or important.
We always receive spam email messages in the spam box, and important email messages in the ‘Important’ folder.
This is done by ML and some of the filters used by Gmail are –
- Permission filters
- Rules-based filters
- General backlists filter
- Header filter
- Content filter
7 Virtual Personal Assistant
Today, we have several virtual personal assistants such as Alexa, Cortana, Google Assistant, and Siri.
As the name implies, such virtual personal assistants help us in finding info by using our voice instructions.
These personal assistants help us by following our voice instructions and commit jobs such as scheduling an appointment, opening an email, calling someone, or playing music, etc.
For such operations, ML algo play a very important role.
These personal assistants record our voice, send it on the cloud over a server, and decode it using machine learning algo and act accordingly.
Real Implementation of Machine Learning
1 Medical diagnosis
ML can be effectively used in the tools and techniques which can help in the diagnosis of diseases.
It is also used for clinical parameters analysis and its combination for the prognosis prediction of disease progression for the outcome research, for patient monitoring and for therapy planning.
This is of the most successful real-time implementation of the ML methods.
It can also prove helpful in the integration of computer based methods in the healthcare sector.
2 Statistical Arbitrage
Arbitrage means automatic trading strategies which involve a high no of securities and are of a short-term.
In this, the user focuses on implementing the trading algo for securities on the basis of quantities such as the general economic variables and historical correlations.
The methods of ML are applied for obtaining an index of arbitrage strategy.
We apply the Support Vector Machine and linear regression to the prices of a stream of stocks.
3 Learning associations
The process of developing insights into the several associations between the items or the products are known as learning associations.
A good example of the same is how many unrelated products can be associated with each other.
One of the major applications of ML is studying the relations between several products that people buy.
If a user purchases a particular product, then he/she should be shown similar products as per his/her choices and preferences between both products.
Whenever a new product is launched in the marketplace, these are associated with the old products for increasing the sales of the new ones.
4 Prediction
ML can be effectively used in the prediction system.
Consider an example of loan system, for computing the probability of a fault, the system might need to classify the information that is available in groups.
The probability of the fault can be easily calculated once the classification has been done. Therefore, making predictions is one of the most useful applications of ML.
5 Extraction
The process of extracting structured info from unstructured data is known as extraction and is one of the most useful and beneficial real-time implementations of machine learning.
Emails, business reports, blogs, articles, and web pages are some examples.
A relational database is used for maintaining the output that is produced by the extraction of information.
The extraction process takes documents as inputs and outputs the structured info.
Future of Machine Learning
- Machine Learning will be a very important part of all the Artificial intelligence systems whether small or large.
- There is a very strong possibility that machine learning might be offered aa a cloud-based service which can be called as Machine Learning-as-a-Service i.e. MLaaS. This is because of machine learning assuming increased value in business applications.
- Connected artificial intelligence systems will enable machine learning algo for continuously learning, based on the newly emerging info on the world wide web.
- There will be a very huge rush among the vendors of hardware for enhancing CPU power and accommodating the machine learning data processing. Most probably, vendors of hardware will be redesigning their machines for doing justice to the power of Machine Learning.
- ML will be helping machines for making better sense to the meaning and context of data.
- Use of multiple technologies in machine learning – It is no secret that the emergence of Internet of Things has proved advantageous to machine learning in several ways. The use of various technological strategies is in current use of Machine Learning; and in the coming time; more and more collaborative learning by using different technologies is probable.
- Personalized Computing Environment –Developers might have access to various API kits for designing and delivering more intelligent apps. Therefore, this effort is similar to assisted programming in a way. Developers will easily embed vision, speech, or facial-recognition features into the systems with the help of these API kits.
- With the help of quantum computing, the speed of the execution of the machine learning algo will be increased greatly. This should be the next conquest in the field of the research of machine learning.
- Higher business outcomes will occur due to the future advancements in unsupervised machine learning algo.
- Tuned Recommendation Engines – The services that will be using machine learning will become more relevant and accurate. For instance,the recommendation engines will be more closer and relevant to a person’s personal tastes and preferences.
Software for Machine Learning
1 TensorFlow
TensorFlow is the standard name that is given to the machine learning in the data science industry.
It facilitates building of both deep learning and machine learning solutions with the help of its extensive interface of CUDA GPUs.
A multi-dimensional data array i.e. Tensor is the most basic type of data of TensorFlow.
It can be seen as an open source tool kit which can be used for building ML pipelines.
This can help you in building scalable systems for processing information.
It also provides functions and support for several applications of machine learning like Reinforcement learning, NLP and computer vision. It is one of the most popular tools of ML for beginners.
2 Shogun
An open-source and famous ML software is Shogun.
It is written in C++ language, and also supports several other languages such as Ruby, C#, Scala, R, and Python. Some of the algo that are supported by Shogun are –
- Linear Discriminant Analysis
- Hidden Markov Models
- Clustering Algo
- Dimensionality reduction
- Support Vector Machines
3 Apache Mahout
This is an open-source ML software that is focused on both classification and collaborative filtering; and such implementations are an extension of the platform of Apache Hadoop.
While this software is still in the progress mode, the no of algo which are supported by this software have grown significantly.
It uses the Map/Reduce paradigms as it is implemented on the top of Hadoop. Some of the most unique features of this software are –
- It provides native solvers for CUDA, GPUs, and CPUs accelerators.
- It also provides a distributed linear algebra framework and expressive Scala DSL for deep learning computations.
4 Apache Spark MLlib
Spark provides many advanced ML features with the help of its ML library and is also a very robust data streaming platform. Spark provides a scalable ML platform with the help of its several APIs which allow users for implementing ML on real-time information.
Any Hadoop source can be seamlessly integrated with the help of its Machine learning library and algorithms.
You are also allowed to perform iterative computations with the help of Spark, through which you can definitely achieve good results for your algo. Some of the algo that are supported by it are –
- Random Forests, Decision Trees, etc.
- Topic Modelling, LDA, Gradient Boosting
- Survival Analysis, Linear Regression
- Logistic Regression, Naive Bayes, Classification
5 Oryx 2
Oryx 2 uses of Lambda Architecture for large scale and real-time ML processing.
Oryx 2 was built on top of the Apache Spark architecture which involves functions for building applications and rapid-prototyping. This software facilitates end-to-end model development for clustering, regression, classification, collaborative filtering operations.
This software has the following 3 tiers –
- The 1st tier is of a generic lambda tier which provides serving and speed layers, which are not specific to procedures of ML.
- The 2nd tier gives machine learning abstractions to select the hyper parameters.
- The 3rd tier gives an end to end implementation of the machine learning apps.
6 H20.ai
The deep learning platform of H20.ai gives a scalable multi layer artificial neural network.
This ANN consists of various parameters and components that can be changed according based on the info provided.
It also comprises of adaptive learning rate and rate annealing for yielding highly predictive output.
Other versions of H20.ai support Recurrent Neural Networks and Convolutional Neural Networks.
7 Pytorch
Pytorch is an advanced deep learning framework developed by FB. The most important features of Pytorch include Tensors and Deep Neural Networks.
A person can develop rapid prototyping for research with the help of Pytorch. Moreover, you can also build pipelines of software using Pytorch.
Uber’s probabilistic programming language is built with this software for machine learning.
Using this software, you can also develop dynamic graphs for accelerating your ML processes.
It also provides your code the ability of information parallelism.
8 RapidMiner
RapidMiner gives a comprehensive and integrated environment for carrying out various tasks such as predictive analysis, text mining, deep learning, machine learning, and data preparation.
It is also famous for its fast speed to avoid risks, reduce costs, and drive revenues.
Limitations of Machine Learning
Machine Learningisn’t perfect with all its benefits to its popularity and powerfulness. The following are the limitations of machine learning –
1 Data Acquisition
ML requires big data sets to train on, and these should be of good quality, and should be unbiased or inclusive.
There may be times where it must wait for new information or data sets to be generated.
2 Resources and Time
Machine learning needs enough time to let the algo develop and learn enough for fulfilling their purpose with a considerable amount of relevancy and accuracy.
It also requires huge resources for functioning and this essentially means additional requirements of computer power for an individual.
3 Results interpretation
Other major challenge that ML faces is the ability to precisely interpret results generated by the algo.
An individual should carefully select the algo for your purpose.
Conclusion
Most experts say ‘YES’ when posed with the question that ‘Is ML really worth all the hype surrounding it?’
Understanding the basics of AI and ML is a must, must thing for any person who is working in the tech domain these days.
The complete working knowledge of this technology is needed to stay relevant in this ever-changing digital world, due to the pervasiveness of machine learning.
Big companies are now in the middle of the adoption curve for ML and AI, mainly due to the exponential advancements and accessible cloud platforms in this field.
This makes machine learning an exciting career option for those people who have the experience and capability to take it up.
Since this field works as a combination of logical thinking, computer science, and statistics; it is heavily varied in what it can offer to new variants.
Moreover, a wide range of positions such as AI developers, machine learning engineers, and data scientists offer choices to aspirants across different verticals.
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