The entire idea of AI is sorting out manners by which we can train a PC to play out an assignment without expecting to give unequivocal guidelines. One more method for contemplating it is that we're attempting to "program" instinct in a PC. You and I can check out an email and effectively observe whether or not it's spam, yet how would you get a PC to do such an assignment? You could build an enormous tangled rationale foundation of "if.. then, at that point.." articulations to figure out the spam messages, yet it would be an aggravation to develop and presumably wouldn't function admirably. All things considered, the AI approach is to outfit the PC with abilities to acquire all alone and feed it a lot of models. AI is detonating as a field right now as individuals are understanding a huge number of errands that we can encourage PCs to perform by taking care of it enormous datasets.

Before we go into the specifics of learning by machines, let's list some applications of machine learning to whet our appetite for what's to come.

Here's a list of some popular applications of machine learning.

Tagging faces, objects, and places in pictures.

Driverless cars, drones, robotic vacuum cleaners, and intelligent game-playing bots.

Translating speech or text in one language into another.

Recommending shopping items, movies, books, songs, or new stories based on a person's interest.

Categorizing email, text-documents, movies, songs, new stories or books into pre-defined categories or genres.

Predicting the next earthquake or weather event such as rain, thunderstorm, drought, or snowfall.

Discovering faulty industrial components or predicting the time-to-failure.

Estimating housing prices, credit scores, mortgage risk, and stock market movements.

Gauging customer sentiment based on textual reviews or social interactions.

Recommending candidates suitable for a job opening.

Personalized search, education, healthcare, advertisements, travel, and shopping experiences.

Machine learning is gradually becoming ubiquitous and part of pretty much everything we do. Even if you do not intend to be a practitioner of machine learning, it is still an essential concept to understand and appreciate.

While AI calculations have been around for a really long time, they've achieved new fame as man-made brainpower has filled in noticeable quality. Profound learning models, specifically, power the present most exceptional AI applications.

AI stages are among big business innovation's most aggressive domains, with most significant sellers, including Amazon, Google, Microsoft, IBM and others, dashing to sign clients up for stage benefits that cover the range of AI exercises, including information assortment, information readiness, information arrangement, model structure, preparing and application organization.

As AI keeps on expanding in significance to business activities and AI turns out to be more viable in big business settings, the AI stage wars will just escalate.

Proceeded with examination into profound learning and AI is progressively centered around growing more broad applications. The present AI models require broad preparing to create a calculation that is exceptionally advanced to perform one assignment. In any case, a few specialists are investigating ways of making models more adaptable and are looking for strategies that permit a machine to apply setting gained from one assignment to future, various undertakings.

Advantages and Disadvantages of Machine Learning

There is an endless number of advantages of ML. We can take a look at the ones which are really helpful. The advantages of Machine Learning tell us how using ML would benefit us.

So, let’s have a look at the advantages of Machine Learning.

Machine Learning is responsible for cutting the workload and time. By automating things we let the algorithm do the hard work for us. Automation is now being done almost everywhere. The reason is that it is very reliable. Also, it helps us to think more creatively.

Due to ML, we are now designing more advanced computers. These computers can handle various Machine Learning models and algorithms efficiently. Even though automation is spreading fast, we still don’t completely rely on it. ML is slowly transforming the industry with its automation.

ML has a wide variety of applications. This means that we can apply ML on any of the major fields. ML has its role everywhere from medical, business, banking to science and tech. This helps to create more opportunities. It plays a major role in customer interactions.

Machine Learning can help in the detection of diseases more quickly. It is helping to lift up businesses. That is why investing in ML technology is worth it.

Machine Learning is the type of technology that keeps on evolving. There is a lot of scope in ML to become the top technology in the future. The reason is, it has a lot of research areas in it. This helps us to improve both hardware and software.

In hardware, we have various laptops and GPUs. These have various ML and Deep Learning networks in them. These help in the faster processing power of the system. When it comes to software we have various UIs and libraries in use. These help in designing more efficient algorithms.

Machine Learning has many factors that make it reliable. One of them is data handling. ML plays the biggest role when it comes to data at this time. It can handle any type of data

Machine Learning can be multidimensional or different types of data. It can process and analyze these data that normal systems can’t. Data is the most important part of any Machine Learning model. Also, studying and handling of data is a field in itself.

5. Best for Education and Online Shopping

ML would be the best tool for education in the future. It provides very creative techniques to help students study.

Recently in China, a school has started to use ML to improve student focus. In online shopping, the ML model studies your searches. Based on your search history, it would provide advertisements. These will be about your search preferences in previous searches. In this, the search history is the data for the model. This is a great way to improve e-commerce with ML.

Now in TechVidvan’s advantages and disadvantages of Machine Learning article, we will see what are the drawbacks of Machine Learning. Let’s start:

Similar to the advantages of Machine Learning, we should also know the disadvantages of Machine Learning. If you don’t know the cons, you won’t know the risks of ML. So, let’s have a look at these disadvantages:

In ML, we can choose the algorithms based on accurate results. For that, we have to run the results on every algorithm. The main problem occurs in the training and testing of data. The data is huge, so sometimes removing errors becomes nearly impossible. These errors can cause a headache to users. Since the data is huge, the errors take a lot of time to resolve.

The selection of an algorithm in Machine Learning is still a manual job. We have to run and test our data in all the algorithms. After that only we can decide what algorithm we want. We choose them on the basis of result accuracy. The process is very much time-consuming.

In ML, we constantly work on data. We take a huge amount of data for training and testing. This process can sometimes cause data inconsistency. The reason is some data constantly keep on updating. So, we have to wait for the new data to arrive. If not, the old and new data might give different results. That is not a good sign for an algorithm.

Many ML algorithms might take more time than you think. Even if it’s the best algorithm it might sometimes surprise you. If your data is large and advanced, the system will take time. This may sometimes cause the consumption of more CPU power. Even with GPUs alongside, it sometimes becomes hectic. Also, the data might use more than the allotted space.

In this article, we will know about Machine Learning an overview

Machine Learning: An overview

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields (columns) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Key Differences Between Big Data and Machine Learning

Following is the key difference between Big Data and Machine Learning:

Both data mining and machine learning are rooted in data science. They often intersect or are confused with each other. They superimpose each other’s activities and the relationship is best described as mutualistic. It is impossible to see a future with just one of them. But there are still some unique identities that separate them in terms of definition and application. Here’s a look at some of the differences between big data and machine learning and how they can be used.

Usually, big data discussions include storage, ingestion & extraction tools commonly Hadoop. Whereas machine learning is a subfield of Computer Science and/or AI that gives computers the ability to learn without being explicitly programmed.

Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it. So, in big data analytics, the analysis is done on big data. Machine learning, in simple terms, is teaching a machine how to respond to unknown inputs and give desirable outputs by using various machine learning models.

Though both big data and machine learning can be set up to automatically look for specific types of data and parameters and their relationship between them big data can’t see the relationship between existing pieces of data with the same depth that machine learning can.

Normal big data analytics is all about extracting and transforming data to extract information, which then can be used to fed to a machine learning system in order to do further analytics for predicting output results.

Big data has got more to do with High-Performance Computing, while Machine Learning is a part of Data Science.

Machine learning performs tasks where human interaction doesn’t matter. Whereas, big data analysis comprises the structure and modeling of data which enhances decision-making system so require human interaction.

Data Use: Big data can be used for a variety of purposes, including financial research, collecting sales data etc. Big data can be used for a variety of purposes, including financial research, collecting sales data etc. Big data can be used for a variety of purposes, including financial research, collecting sales data etc.

Foundations for Learning: Big data analytics pulls from existing information to look for emerging patterns that can help shape our decision-making processes.

Pattern Recognition: Big data analytics can reveal some patterns through classifications and sequence analysis.

Data Volume: Big data as the name suggest tends to be interested in large-scale datasets where the problem is dealing with the large volume of data.

Purpose: Purpose of big data is to store large volume of data and find out pattern in data

Data Use: Machine learning is the technology behind self-driving cars and advance recommendation engines.

Foundations for Learning: On the other hand, Machine learning can learn from the existing data and provide the foundation required for a machine to teach itself.

Pattern Recognition: However, machine learning takes this concept a one step ahead by using the same algorithms that big data analytics uses to automatically learn from the collected data.

Data Volume: ML tends to be more interested in small datasets where over-fitting is the problem

Purpose: Purpose of machine learning is to learn from trained data and predicts or estimates future results.

Big data vs machine learning go hand-in-hand and it would benefit a lot to learn both. Both fields offer good job opportunities as the demand is high for professionals across industries while there is a lack of skilled professionals; machine learning professionals being in more demand when compared with big data analysts. When it comes to salary, both profiles enjoy similar packages and if you have skills in both of them, you are hot property in the field of analytics.

However, if you do not have the time to learn both, you can go for whichever you are interested in.

In this blog post, we will know the comparison between Big Data and Machine Learning.

The comparison between Big Data vs Machine Learning 

Machine learning is an application of Artificial Intelligence (AI) that gives systems the potential to routinely examine and enhance from experience without being explicitly programmed. Machine studying makes a specialty of the development of pc programs that can get admission to records and use it to study for themselves.

The process of learning begins with observations or statistics, together with examples, direct revel in, or training, to look for patterns in statistics and make better choices in the future primarily based on the examples that we provide. The number one intention is to allow the computers to examine mechanically without human intervention or help and modify movements consequently.

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value.

Data scientists are specialists who excel in converting raw data into critical business matters. These scientists are skilled in algorithmic coding along with concepts like data mining, machine learning, and statistics. Data science is used extensively by companies like Amazon, Netflix, the healthcare sector, in the fraud detection sector, internet search, airlines, etc. 

Difference between Data Science and Machine Learning

Data Science is a field about processes and systems to extract data from structured and semi-structured data.

Data in Data Science maybe or maybe not have evolved from a machine or mechanical process. 

Data Science as a broader term not only focuses on algorithms statistics but also takes care of data processing.	

It is a broad term for multiple disciplines.

Many operations of data science that is, data gathering, data cleaning, data manipulation, etc.	

Example: Netflix uses Data Science technology.	

Machine Learning is a field of study that gives computers the capability to learn without being explicitly programmed.

Machines utilize data science techniques to learn about the data.

It uses various techniques like regression and supervised clustering.

But it is only focused on algorithm statistics.

It is three types: Unsupervised learning, Reinforcement learning, Supervised learning.

Example: Facebook uses Machine Learning technology.

Today we discuss some differences between Data Science and Machine Learning.

Data Science VS Machine Learning

Find low-dimensional representations of the data

Find novel observations/ database cleaning

Numerous models, such as classifiers are strategically made and combined to solve a specific computational program which is known as ensemble learning. The ensemble methods are also known as committee-based learning or learning multiple classifier systems. It trains various hypotheses to fix the same issue. One of the most suitable examples of ensemble modeling is the random forest trees where several decision trees are used to predict outcomes. It is used to improve the classification, function approximation, prediction, etc. of a model.

In various areas of information science like machine learning, a set of data is used to discover the potentially predictive relationship known as ‘Training Set’. The training set is an example given to the learner, while the Test set is used to test the accuracy of the hypotheses generated by the learner, and it is the set of examples held back from the learner. Training sets are distinct from the Test set.

The different types of techniques in Machine Learning are:

Inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances, tries to induce a general rule.

KNN or K nearest neighbors is a supervised algorithm that is used for classification purposes. In KNN, a test sample is given as the class of the majority of its nearest neighbors. On the other side, K-means is an unsupervised algorithm that is mainly used for clustering. In k-means clustering, it needs a set of unlabeled points and a threshold only. The algorithm further takes unlabeled data and learns how to cluster it into groups by computing the mean of the distance between different unlabeled points.

Machine learning is all about algorithms that are used to parse data, learn from that data, and then apply whatever they have learned to make informed decisions.

Deep learning is a part of machine learning, which is inspired by the structure of the human brain and is particularly useful in feature detection.

A parametric learning algorithm has a finite set of parameters the learning algorithm estimates.

A non-parametric learning algorithm has a non-finite set of parameters. This means, that as the dataset grows, the learning algorithm can estimate more and more parameters from the dataset.

The different approaches in Machine Learning are

The standard approach to supervised learning is to split the set of example into the training set and the test.

Reinforcement learning is an algorithm technique used in Machine Learning. It involves an agent that interacts with its environment by producing actions & discovering errors or rewards. Reinforcement learning is employed by different software and machines to search for the best suitable behavior or path it should follow in a specific situation. It usually learns on the basis of reward or penalty given for every action it performs.

The different types of algorithm methods in machine earning are:

The possibility of overfitting occurs when the criteria used for training the model is not as per the criteria used to judge the efficiency of a model.

Data mining can be described as the process in which the structured data tries to abstract knowledge or interesting unknown patterns. During this process, machine learning algorithms are used.

Machine learning represents the study, design, and development of the algorithms which provide the ability to the processors to learn without being explicitly programmed.

Machine Learning involves algorithms that learn from patterns of data and then apply it to decision making. Deep Learning, on the other hand, is able to learn through processing data on its own and is quite similar to the human brain where it identifies something, analyse it, and makes a decision.
The key differences are as follow:

The manner in which data is presented to the system.

Machine learning algorithms always require structured data and deep learning networks rely on layers of artificial neural networks.

A. Supervised learning: [Target is present]
The machine learns using labelled data. The model is trained on an existing data set before it starts making decisions with the new data.
The target variable is continuous: Linear Regression, polynomial Regression, quadratic Regression.
The target variable is categorical: Logistic regression, Naive Bayes, KNN, SVM, Decision Tree, Gradient Boosting, ADA boosting, Bagging, Random forest etc.

B. Unsupervised learning: [Target is absent]
The machine is trained on unlabelled data and without any proper guidance. It automatically infers patterns and relationships in the data by creating clusters. The model learns through observations and deduced structures in the data.
Principal component Analysis, Factor analysis, Singular Value Decomposition etc.

C. Reinforcement Learning:
The model learns through a trial and error method. This kind of learning involves an agent that will interact with the environment to create actions and then discover errors or rewards of that action.

By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it. But if you have a small database and you are forced to come with a model based on that. In such a situation, you can use a technique known as cross-validation.

We can use data augmentation techniques or regularization.

we can try to simplify our model structure.

Following are the different techniques of unsupervised machine learning:

Clustering: It includes the data that must be divided into subsets. These subsets are also known as clusters. Diverse clusters disclose details about objects, unlike regression or classification.

Association: In the association problem, we can recognize the association patterns between different items and variables. For instance, e-commerce can indicate other items for us to buy according to our previous purchases.

Following are the three stages of model building:

Model Building: In this stage, we will choose the ideal algorithm for the model, and we will train it based on our requirements.

Model Testing: In this stage, we will check the model's accuracy by using test data.

Applying Model: After testing, we have to make the changes, and then we can use the model for real-time projects.

In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally observed, because of having too many parameters concerning the number of training data types. The model exhibits poor performance which has been overfitted.

Bias: Bias can be defined as a situation where an error has occurred due to the use of assumptions in the learning algorithm.

Variance: Variance is an error caused because of the complexity of the algorithm that is been used to analyze the data.

Variance is the measure of how much the prediction would vary if the model was trained on a different dataset, drawn from the same population. Can be also thought of as the “flexibility” of the model.

Bias is the amount our predictions are systematically off from the target. Bias is the measure of how “inflexible” the model is.

Machine learning is a branch of computer science which deals with system programming to automatically learn and improve with experience. For example, Robots are programmed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data.

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Machine Learning

I am relatively new to machine learning/python/ubuntu.

I have a set of images in .jpg format where half contain a feature I want caffe to learn and half don't. I'm having trouble in finding a way to convert them to the required lmdb format.

My question is can anyone provide a step by step guide on how to use convert_imageset.cpp in the ubuntu terminal?

A guide to convert_imageset.cpp

I have a pandas dataframe and I wish to divide it to 3 separate sets. I know that using train_test_split from sklearn.cross_validation, one can divide the data in two sets (train and test). However, I couldn't find any solution about splitting the data into three sets. Preferably, I'd like to have the indices of the original data.

I know that a workaround would be to use train_test_split two times and somehow adjust the indices. But is there a more standard / built-in way to split the data into 3 sets instead of 2?

How to split data into 3 sets (train, validation and test)?

The presence of autocorrelation drastically reduces the model performance but why and how it reduces? 

How Autocorrelation decreases the linear model performance?

I trained a H2O GBM model (H2O version 3.22.0.1) using many categorical columns but when i try to predict on test data it is giving me warnings for unknown categorical levels in test. Does H2O GBM not handle then unseen categorical levels automatically? or is there a specific version where this capability is added? 

Getting UserWarning: Test/Validation dataset column 'browser_name' has levels not trained on: [Android, Midori, Opera Mini, Other] for H2O GBM model

I have time-series data: timestamp, 5 crypto tools prices, 5 other features, 5 very noisy output values to be predicted. The train data has output values, the test data is not. Which algorithm of machine learning to use? How to deal with noise in output? 

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