As far as web and software development goes, Python, followed by Java, JavaScript, and C++, are among the most popular programming languages in today’s market.

Whether you’re looking to improve your coding skills or start a career in technology, learning a coding language can bring many benefits. It helps hone your technical and problem-solving skills and get a high-paying job.

What Are the Best Programming Languages to Learn in 2023?

1. Python

Python is a high-level, general-purpose programming language. It can be used for various tasks, from data analysis and visualization to web development, prototyping, and automation.

Python is popular among software developers since it works well as a scripting language. Moreover, it lets users utilize different programming paradigms, including the object-oriented, imperative, procedural, and functional programming languages.

Furthermore, this language is open-source, enabling programmers to modify its source code to fit their needs.

Python is easier to learn than other languages thanks to its syntax familiarity with English. If you want to kickstart your career as a software developer or engineer, learning to code in Python can be a great starting point.

On top of all, Python developers are in high and growing demand. Choose from various job roles, such as a data scientist, a software engineer, and an artificial intelligence (AI) researcher.

If you’re interested to learn Python, check out our top list of web developer certifications to scale your programming career.

2. C#

C# is an object-oriented programming language – a model that organizes software design around objects.

C# is one of the top programming languages due to its fast performance and improved stability. This coding language is also easier to learn than its predecessors, C and C++, as it has a simple syntax and well-defined class hierarchy.

Starting off as Microsoft’s language for its .NET framework, C# is now compatible with Windows and Linux. Therefore, it’s arguably the best programming language to learn for developing GUI-based desktop applications.

3. C++

C++ is the enhanced version of C and one of the most popular programming languages in computer science. What’s more, versatility makes C++ the best programming language to learn.

Its fast and powerful nature lets developers create applications with excellent performance, like video games, graphics software, and web browsers.

It combines low and high-level language characteristics, making it excellent for system programming while allowing abstraction for smaller projects. Also, C++ is a multi-paradigm coding language, supporting OOP, generic, and imperative programming.

4. JavaScript

This scripting language is often used to improve a webpage’s interactivity. It enables web developers to add dynamic elements on their landing pages, such as animated graphics, clickable buttons, and mouseover effects.

JavaScript is also an excellent language for developing web apps as it offers an intuitive design.

5. PHP

In terms of job opportunities, PHP is an in-demand open-source programming language despite being one of the oldest in the industry. PHP programmers can use this language to write command-line scripts as well as server-side scripts and develop desktop applications.

6. Swift

Swift is a general-purpose, multi-paradigm, compiled programming language developed by Apple Inc. and the open-source community. 

7. Java

Java is a high-level, class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible.

8. Go

Go is a statically typed, compiled programming language designed at Google by Robert Griesemer, Rob Pike, and Ken Thompson. It is syntactically similar to C, but with memory safety, garbage collection, structural typing, and CSP-style concurrency.

9. Sql 

Structured Query Language, abbreviated as SQL, is a domain-specific language used in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management system.

10. Ruby

Ruby is probably most famous for its use in web development, it has many other uses, too. Some of these include automation, command-line tools, static site generation, DevOps, web scraping, and data processing. Perhaps most importantly, Ruby is a highly versatile and portable language.

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Once you’re confident in your abilities and portfolio, it’s time to think about how to get a job as a data analyst.
Be sure you have the required skills
Before starting your job hunting process, you may want to make a quick revision of your data analyst skills and compare it with the requirements for this role in the modern market.
A good place to start is to take a look at the descriptions of several job positions for a data analyst and write down the skills that are currently most in demand.
To get a job as a data analyst, here are the basic technical skills that companies usually expect to see:
Python or R (especially their specialized libraries for data analysis)
SQL
The command line
Statistics
Data cleaning and wrangling
Data analysis
Data visualization
Web scraping
Debugging
Data storytelling
Dealing with unstructured data
If the list above looks overwhelming to you, don’t feel discouraged; you likely won’t need all these skills for every data analyst job. Usually, each company looks for a different set of skills in a suitable candidate.
The best way to find out the specific requirements of a certain employer is to read the corresponding job description. If, at this point, you feel that you lack some crucial skills, consider upskilling.
Finally, don’t forget that there are also some important data analyst soft skills:
Analytical thinking
Multitasking
Curiosity
Creativity
Communication skills
Flexibility
Ability to work both independently and in a team
Decision making
Business domain knowledge
Create a professional data analyst resume
Now it’s time to write your resume. At a first glance, it may seem to be an easy task. However, in reality, it’s worth dedicating some time and effort to building a compelling and professional-looking resume that can capture the attention of recruiters.
Customize your data analyst resume to each job description you apply for. Read the job description carefully, figure out the requirements that the company is looking for in a candidate, and incorporate/highlight the necessary skills and keywords in your resume. In addition, you can explore the company’s website (its mission, values, and products), and refer to it to make your resume reflect that you are the perfect fit.
Be concise but informative.
Use plain but efficient language.
Check for errors and typos.
Consider including the following sections:
Contact Information
Objective
Work Experience
Projects
Skills
Education
The order of appearance of the last four sections depends on your real relevant experience and, hence, on what you want to showcase first.
Develop your online profiles
Your LinkedIn/Kaggle/Medium/GitHub or any other relevant professional profile should be in line with your data analyst resume or even represent your resume in miniature. The main goal here is to let your readers know that you are a data analyst, even if you don’t have real work experience yet in this sphere.
Below are some useful tips:
Keep your professional profile and portfolio of projects updated.
In the headline, write Data Analyst instead of your current profession, if you are a career-changer. Avoid adding the word aspiring to your headline.
Include your photo and maybe a data-related cover picture.
Provide a professional way to contact you.
Include any relevant licenses, certifications, skills, accomplishments, recommendations, and cross-links to your other professional profiles.
Some of the suggestions for writing an efficient resume are also applicable here. Be concise but informative, use plain but efficient language, check for errors and typos, double-check your contact details, and avoid including your skill level.
Become a Data Analyst With ONLEI Technologies
By now, you should know how to become a data analyst and what you need to do to make your goal a reality.
the prerequisites you need to have to start learning data analysis
how to select the optimal qualification
why individual projects are so important for your portfolio and where to search for datasets
other projects that can be added to your portfolio
the technical and soft skills companies usually look for in a data analyst
the nuances and tricks for creating an outstanding resume
the importance of your online presence, visibility, and interaction with data specialists
where and how to search for a job and how to keep optimistic and confident whilst applying
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Deep Learning Project 

A Gentle Introduction to Siamese Neural Networks Architecture 

What are Siamese Neural Networks?

Siamese Neural Networks, or SNNs, is one of the most popular neural network architectures that use this strategy and can predict multiple classes from very little data. This ability has made Siamese neural networks very popular in real-world applications in security, face recognition, signature verification, and more.

So how does the neural network architecture of Siamese networks make this possible?

Siamese Neural Networks: An Overview

A Siamese network consists of two or more identical subnetworks: neural networks with the same architecture, configuration, and weights. Even during training, parameter updates happen simultaneously for both neural networks with the same weights.

The purpose of having identical subnetworks is to train the model based on a similarity function that measures how different the feature vectors of one image are from the other. Because of this architecture, the model can be trained without much data.

Why use Siamese Neural Networks ?

With siamese neural networks, the common class imbalance problem can be addressed since the network does not need too many samples for a given class in the training data.

Moreover, a new class can be added without training the entire network from scratch after the siamese neural network has been trained and deployed. The model trains by learning how similar or dissimilar image pairs are, samples from a new class can be added to the trained siamese network, and training can be resumed since the network architecture will compare the new images with the rest of the classes and update the weights and the fully connected layer.

This behaviour is unique to a network architecture that uses one-shot learning since other categories of neural networks would have to be trained from scratch on a large, class-balanced dataset for significant performance.

But how does a siamese network learn from such a small set of samples? Let’s look at the architecture and how the training process in siamese neural networks works.

Siamese Neural Network Architecture Explained

As described above, the architecture below shows two identical subnetworks that make up a siamese neural network. Feature vectors from both networks are compared using a loss function L. There are two strategies for training the siamese network using different loss functions.

First, the feature vectors of similar and dissimilar pairs should be descriptive, informative, and distinct enough from each other so that segregation can be learned effectively.

And secondly, the feature vectors of similar image pairs should be similar enough, and those for dissimilar pairs should be dissimilar enough so that the model can quickly learn semantic similarity.

To make sure the model can learn these feature vectors quickly, the loss function should incentivize both learning the similarity and dissimilarity of things heavily enough. Here is where the siamese neural network strategy helps – by comparing one image with all the other images, the model learns what “similar” is and how to define and recognize dissimilar pairs.

To gain this kind of information, the cross-entropy loss cannot help as it works on a class prediction basis. Mean squared errors also do not give enough information needed for our goal. The most commonly used loss functions are a Contrastive loss function and a Triplet loss function. Let’s look at each of them in detail.

Contrastive Loss Function

The contrastive loss function is a distance-based loss function that updates weights such that two similar feature vectors have a minimal Euclidean distance. In comparison, the distance is maximized between two different vectors.

In the equation shown below, y represents whether or not the vectors are dissimilar, and Dw is the Euclidean distance between the vectors. When the vectors are dissimilar (y=1), the loss function minimizes the second term — for which Dw must be maximized (encourage more distance between dissimilar vectors). We want these vectors to have a distance of more than at least m, and we avoid computation if the vectors are already m units apart by defaulting to 0.

Similarly, if the vectors are similar (y=0), the loss function must minimize Dw.

Contrastive Loss Function in Siamese Neural Networks

However, because of the binary nature of this function to bring the vectors either close or far from each other, we cannot learn how similar two vectors are to each other. Thus, another loss function helps us learn both similarity score and dissimilarity in a better way.

Triplet Loss Function in Siamese Network

By using triplet loss, we can tell how similar an image looks to the others (within or outside its class) when compared. The siamese network learns the similarity ranking using the score computed in this fashion.

For this, the loss is computed by comparing a given image (called anchor image) with a positive image (which is similar to the anchor image) and a negative image (which is dissimilar to it). Computing the intra-distance for each of these pairs, the model knows what similarity looks like and how different the given image must be from the other classes.

So, in the equation below, f(A) is the anchor image, and f(P) and f(N) is the positive image and negative image, respectively. Again, for the loss function to minimize the RHS, the term with f(N) would have to be maximized and that with f(P) minimized. This aligns with the strategy that we want similar pairs closer and dissimilar pairs further apart. α is just a regularizing parameter.

Triplet Loss Function in Siamese Networks

Read here for further explanation on the Triplet Loss Function in Siamese Networks

Pros and Cons of Siamese Neural Networks

As we saw when getting introduced to siamese neural networks they offer many benefits over conventional CNNs in certain specific tasks.

Advantages of Siamese Network

Semantic Similarity: Firstly, siamese networks do not learn from training errors or mispredictions but from semantic similarity. This encourages the model to learn better and better embeddings that represent images from the support set and bring related concepts close in the feature space. By learning such a feature space, similar to how textual models learn word embeddings, the model learns concepts and attempts to understand why certain images are more similar than others instead of just extracting static features using convolutions.

Class Imbalance: The biggest benefit directly applicable to the real world is the capability of giving benchmark performance on very little data. With the data requirement reduced, the problem of class imbalance also vanishes.

Siamese Neural Network for Face Recognition 

Face recognition is nothing but another image recognition or classification task. One-shot learning is particularly applicable to this task because it is impossible to have sufficient samples of one person’s face (one label) in practical cases. Face recognition is often used as an attendance system or security measure to restrict access to buildings and offices to employees only.

In this case, not only is it impractical to get many images of one person to get a decent success rate but adding access to an incoming new employee would mean training the entire CNN from scratch and risking the existing performance.

Siamese Neural Network for Image Classification 

Signature verification is a commonly found use of image classification in the context of one-shot learning. A signature verification system checks the authenticity of a given signature against the one existing in a dataset. Based on the sign’s similarity, the sample can be classified as real or fake. With this task widely prevalent in banks and financial institutions worldwide, Siamese networks quickly became the go-to solution for this otherwise manually laborious task.

Is a Siamese Network Supervised?

Yes, siamese networks are trained in a supervised fashion. It needs labeled information to know whether the images it compares are similar. However, one can also tune siamese networks to learn in a self-supervised (SSL: self-supervised learning) manner.

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These seven trending data science skills represent those that are some of the most searched by Coursera’s community of million global learners. To prepare for a new career in the high-growth field of data analysis, start by developing these skills.   

Let’s take a closer look at what they are and how you can start learning them.

1. SQL

Structured Query Language, or SQL, is the standard language used to communicate with databases. Knowing SQL lets you update, organize, and query data stored in relational databases, as well as modify data structures (schema). 

Since almost all data analysts will need to use SQL to access data from a company’s database, it’s arguably the most important skill to learn to get a job. In fact, it’s common for data analyst interviews to include a technical screening with SQL. 

Luckily, SQL is one of the easier languages to learn.

2. Statistical programming

Statistical programming languages, like R or Python, enable you to perform advanced analyses in ways that Excel cannot. Being able to write programs in these languages means that you can clean, analyze, and visualize large data sets more efficiently.

Both languages are open source, and it’s a good idea to learn at least one of them. There’s some debate over which language is better for data analysis. Either language can accomplish similar data science tasks. While R was designed specifically for analytics, Python is the more popular of the two and tends to be an easier language to learn (especially if it’s your first).

3. Machine learning

Machine learning, a branch of artificial intelligence (AI), has become one of the most important developments in data science. This skill focuses on building algorithms designed to find patterns in big data sets, improving their accuracy over time.

The more data a machine learning algorithm processes, the “smarter” it becomes, allowing for more accurate predictions. 

4. Probability and statistics

Statistics refers to the field of math and science concerned with collecting, analyzing, interpreting, and presenting data. That might sound familiar—it closely matches the description of what a data analyst does. 

With a strong foundation in probability and statistics, you’ll be better able to: 

Identify patterns and trends in the data

Avoid biases, fallacies, and logical errors into your analysis

Produce accurate and trustworthy results

5. Data management

Data management refers to the practices of collecting, organizing, and storing data in a way that is efficient, secure, and cost effective. While some organizations will have roles dedicated to data management—data architects and engineers, database administrators, and information security analysts—data analysts often manage data in some capacity.

6. Statistical visualization

Gleaning insights from data is only one part of the data analysis process. Another fundamental part is telling a story with those insights to help inform better business decisions. That’s where data visualization comes in. As a data analyst, you can use charts, graphs, maps, and other visual representations of data to help present your findings in an easy-to-understand way. 

7. Econometrics

With econometrics, analysts apply statistical and mathematical data models to the field of economics to help forecast future trends based on historical data. Understanding econometrics is key for data analysts looking for jobs in the financial sector, particularly at investment banks and hedge funds.

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The following segments will focus on the topics of Machine learning {ML}:
What is Machine Learning?
History of Machine Learning
How does Machine Learning work?
Course Objective
Who Should Attend
WHY ONLEI Technologies FOR MACHINE LEARNING TRAINING?
WHAT ARE THE BENEFITS OF DOING A MACHINE LEARNING COURSE?
What is machine learning?
Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behaviour. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.Idea engines are an average use case for AI. Other notable uses fuse distortion acknowledgment, spam filtering, malware peril revelation, business process automation (BPA), and judicious help.Machine Learning (ML) is a kind of Artificial Intelligence (AI) that licences programming applications to end up being more exact at predicting results without being explicitly changed to do all things considered.

History of Machine Learning
The term machine learning was conceived in 1959 by Arthur Samuel, an American IBMer, and pioneer in the field of PC gaming and fake knowledge. An agent book of AI research during the 1960s was Nilsson’s book on Learning Machines, overseeing AI for plan characterization. Interest related to plan affirmation continued into the 1970s, as portrayed by Duda and Hart in 1973. In 1981 a report was given on using showing methods so a neural association sorts out some way to see 40 characters (26 letters, 10 digits, and 4 phenomenal pictures) from a PC terminal.
Tom M. Mitchell gave a comprehensively referred to, more regular importance of the computations packed in the AI field: “A PC program is said to acquire in actuality E concerning some class of tasks T and execution measure P if its show at endeavours in T, as assessed by P, improves with experience E. This significance of the tasks where AI is concerned offers a basically useful definition as opposed to describing the field in scholarly terms. This follows Alan Turing’s recommendation in his paper “Figuring Machinery and Intelligence”, in which the request is “Can machines accept?”. Is displaced with the request: Can machines do what we (as theory components) can do?
Progressing machine learning has two targets, one is to bunch data reliant upon models which have been made, the other plan is to make assumptions for future outcomes subject to these models. A theoretical estimation to gathering data may use PC vision of moles joined with controlled figuring out how to set it up to describe the cancer-causing moles. Whereas, a machine learning estimation for stock trading might enlighten the shipper in regards to future expected forecasts.
ONLEI is one of the Machine Learning Online Training Institutes in India . Machine Learning Training is provided according to current industry standards which enables one to land their dream jobs in top companies of the world.

HOW MACHINE LEARNING WORKS
The starting periods of machine learning (ML) saw tests including hypotheses of PCs seeing plans in data and acquiring from them. Today, in the wake of developing those fundamental examinations, Machine learning (ML) is really puzzling. But it becomes easy for you if you select the Best Machine Learning Training Institute in Noida . Machine Learning (ML) is a sort of artificial intelligence (AI) that urges PCs to think thusly about how individuals do: Learning and improving past experiences. It works by researching data and recognizing plans and incorporating unimportant human intercession.
Machine learning {ML} utilizes two principal procedures:

Supervised learning permits you to gather information or produce an information yield from a past ML organization. Administered learning is energizing since it works similarly people really learn. In administered undertakings, we present the PC with an assortment of named information focuses called a preparation set (for instance, a bunch of readouts from an arrangement of train terminals and markers where they had delays over the most recent three months).
Unsupervised learning assists you with discovering a wide range of obscure examples of information. In unaided learning, the calculation attempts to get familiar with some innate construction of the information with just unlabelled models. Two normal solo learning undertakings are bunching and dimensionality decrease.
Course objectives
An overview of how humongous data is getting generated, how to draw meaningful business insights, techniques used to analyze structured & unstructured data, latest machine learning algorithms used to build advanced prediction models & how to visualize data. All these are learned from the perspective of solving complex business problems & making organizations profitable. Practical case studies which are industry relevant have been making our students stand-out from the rest & achieve outstanding accolades from the best companies of the globe. Our students have been leaving new footprints in the corporate world by becoming industry-ready as soon as they graduate from colleges.

Who should attend
Candidates aspiring to be a Data Scientist, Big Data Analysts, Analytics Manager/Professionals, Business Analyst, Developer
Graduates who are looking to build a career in Data Science and Machine Learning
Employees – Organization is planning to shift to Big data tools
Mid-level Executives
Managers with Knowledge of basic programming

WHY ONLEI Technologies FOR MACHINE LEARNING TRAINING?
There are numerous reasons which make ONLEI Technologies one of the best Machine Learning Training Institute in Noida. Some of the reasons include-

Has been training and placing candidates in reputed companies for more than 15 years.
Our curriculum is designed to match growing corporate needs. Our lectures are supplemented with industry-ready assignments and tasks.
We provide facility of world-class lab facility. Our experts will provide you opportunity to work on curated case studies and selected industry problems.
Our teams of trainers hail from IT Industry with more than 15+ years of experience in teaching and guiding students in latest technologies.
International level Machine Learning certification will be provided.
Apart from Classroom Training, online training is also provided.
Flexible Batch Size and Timings
1-year membership card
The course content is completely upgraded with the latest upgraded improvements so that students may lead the race.

WHAT ARE THE BENEFITS OF DOING A MACHINE LEARNING COURSE?
You will get better knowledge of programming and how to implement it for actual development requirements in the industrial projects and applications.
Enhanced knowledge on the web development framework. Using this framework, you can develop dynamic websites swiftly.
You will learn how to design, develop, test, support and deploy desktop, custom web, and mobile applications.
Design and improve testing and maintenance activities and procedures.
Design, implement and develop important applications in a Machine Learning environment.
Increased chances of working in leading software companies like Infosys, Wipro, Amazon, TCS, IBM and many more.

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