Is Data Scientist is Difficult to learn?

In this post you will know why becoming a data scientist is difficult. what are the reasons behind that. In my experience doing machine learning and data science, there are a lot of different elements you have to know quite well to be effective in your work. These include the following:

General programming familiarity. I consider data scientists to be engineers, where instead of coding a web app as a front-end engineer would do, they are responsible for architecting data processing pipelines, designing and implementing models, and developing infrastructure for system evaluation and metrics computation. As you can imagine, performing these tasks requires a reasonable amount of fluency with a high-level programming language (think Python, R, Matlab, or Julia), as well as data science specific libraries (think Pandas, Scikit-learn, Matplotlib, or Tensorflow). Developing this skill alone is something that can make up a year or more of an undergraduate computer science degree.

Mathematical maturity. Data scientists must be comfortable with a variety of mathematical topics. These include statistics, probability theory, and linear algebra, to name a few. Very often in a traditional data science role, you will have to read academic papers describing some model or dataset and be able to utilize and implement the key ideas of the paper. This requires being comfortable understanding mathematical concepts and notation, which can be quite hard since research papers are rarely written as if they are trying to be a New York Times popular science book, comprehensible by the general public. I would also argue that developing this skill is more important than the programming familiarity, since this is what makes data scientists…well scientists. You need to build intuition for the models you are implementing, data you are processing, and analyses you are performing, since this intuition will help you to truly do your job well.

Data insights. Using, understanding, and manipulating data is a very different skill from other traditional engineering jobs. I remember once walking into an interview for a data science role some years ago. After a brief introduction, the interviewer literally pulled up a CSV file in Sublime text with just a bunch of numbers in different columns and said “Here are our records for the past month of customers using our product. What would you do with this?” While this may seem like an unorthodox (and admittedly intimidating) interview, these are the types of situations data scientists have to deal with on a daily basis. You will often be presented with unfiltered, incomplete, and sparse datasets and expected to derive meaningful insights from them. This will require you to get comfortable asking a score of different questions about your data including:

• How do I fill in missing values?
• How do I deal with/remove outliers?
• How do I get more even label distributions to train my model?
• What kinds of features are most relevant for my model?
• How do I detect/deal with model overfitting/underfitting?
• What metrics are most useful to assess model performance?

As you can see, being a data scientist oftentimes amounts to being a jack-of-all-trades. You have to know a fair bit, which is why it seems that there is a pretty high barrier to entry. That being said, it is an extremely rewarding job, one where you are the person shedding light on the unknown, a data-whisperer of sorts :)

I’m also very confident that anyone with a disciplined course of study can achieve the skills necessary to become a data scientist, without having to spend years at some top university.

Best Projects to learn Machine learning and Data science

In this post you will know some the best projects i can do to learn Machine learning and data science.The best way to build trust with a hiring manager is to prove you can do the work that they need you to do. With data science, this comes down to building a portfolio of projects. The more interactive the projects are, the more the companies will trust that you'll be an asset to the business. And the greater are your chances of getting hired.

Considering the current industry trends, the best way to showcase your Data Science skills are these 4 kinds of projects:

Data Cleaning

Data Analysis

Interactive Data Visualizations

Machine Learning

Here are a few popular projects based on these above skills that you could include in your future portfolio—

Projects based on Data Cleaning: Data scientists approximately spend up to 80% of their working time on cleaning data. So, if you can show that you’re experienced at cleaning data, you’ll eventually be more valuable. To create a Data cleaning project, you first need to pick a messy dataset and start cleaning it by removing distortions or missing data points. You can try these datasets to begin with-

Ticket Sales Data- In this you will strengthen your skills by importing and cleaning some messy online ticket sales data. This includes removing redundant information, deleting duplicates, identifying event dates and sorting them etc.

World Food Facts- The data will include some popular food items worldwide with their nutritional values and place of origin. Your goal is to eliminate useless information, remove and sort pairs of items holding duplicate values and replacing missing values.

Data Analysis Projects: This involves drawing conclusions and generating questions that lead to interesting discoveries. Here are a few popular projects you should consider-

Movie Recommendation System- Recommender systems are considered valuable even for large companies like Google or Facebook. As it is important from a perspective of revenue and engagement. So no doubt in going for this project. Beginners are able to practice their analytical skills and can build a model of their personal movie recommendation system.

Diabetes Prediction- Make analyses based on the patient’s characteristic data set to predict whether he is diabetic or not. Your goal is to build a project to analyze datasets containing attributes like glucose level, blood pressure, age, etc.

Projects based on Visualisation: Data Visualisation requires you to have a real hang of some popular tools like Tableau, Plotly, Qlikview etc. Using these tools, you have to create some interactive visualizations for a better understanding of the analysis. You can consider these projects, to begin with-

Designing a business plan for insurance distribution- In this, you would be forecasting the business for the upcoming years by exploring the hidden trends, extrapolation, assumption and finally summarizing the solutions through visualization.

Real Estate price prediction- Make predictions using Real Estate market data containing values like crime rate, age, accessibility, population, etc. Built an interactive visualization for an effective stock price.

Machine Learning projects: A machine learning project is another important piece of your data science portfolio. Now you should not approach towards every algorithm, but rather pick some basic and widely used ones like Logistic Regression, K-means clustering, Naive Bayes etc.

Twitter Stream Project- A lot of companies monitors mentions from their customers on Twitter to react to the negative ones quickly. For example giants like Uber and Airtel respond to negative tweets fast and find out what the problem is and how they can solve it. You can make this project using a convenient Twitter API and sentiment analysis algorithms to detect such tweets in the whole stream.

Spam or Ham- One of the classic data science problems is a spam detection. You can build a model for detecting spam emails and messages. Additionally, you can also add spamming user comments to hide them in the browser. This will showcase your statistical skills and some classic machine learning.

All these are some out of the box projects which can show your skills as a complete Data Scientist. Having worked on these projects will provide you an edge over the peers. Also listing these in your data scientist resume can improve your chances of landing a high-paying job. You can easily get the data sets for these projects online or at Kaggle. Also you should try to work on end to end data science projects available on public domains like Datacamp, MNIST, Github etc.

Although all these are some unconventional and easy to build projects, you should mainly focus on projects based on Data Cleaning and Analysis. Since these are the primary skills that companies usually look for before hiring freshers for data science roles.

Also you can try Edwisor,which is a popular platform for working on some modern data science projects. Here you can get a curated career path for data science in which you can learn all the tools as well as work on many industry level projects. Alongside this, a lot of top companies like American Express, Data Peace, Paisa Bazaar and many startups hire data scientists from here on the basis of the projects they do. So get some great job opportunities too. So give it a try!

How do Start to learn Machine Learning?

In this post you will know how should i start to learn Machine Learning from scratch. ML has changed how data-driven business leaders make decisions, gage their businesses, study human behavior, and view predictive analytics. If your organization needs to unleash the benefits of this extraordinary field, you need the right minds—quants and translators.

With breakthroughs such as parallel computation that’s cheap, Big Data, and improved algorithms, utilitarian AI is what the world is moving toward. The increased need to handle huge amounts of data and the number of IoT connected devices that define the world today reinforce the importance of machine learning.

So as to learn machine learning, you should be better than average at math. Here are the maths you should learn keeping in mind the end goal to be prepared.

• Linear algebra-Linear Algebra– MIT 18.06 Linear Algebra by Gilbert Strang
• Probability theory-Probability and Statistics– MIT 6.041 Probabilistic Systems Analysis and Applied Probability by John Tsitsiklis
• Calculus
• Multivariate Calculus
• Graph theory
• Optimization methods
• Any programming language that is widely used for ML such as python, MATLAB or C++.

First you need to be trained with the appropriate basis in mathematics and computer science. In the case of deep learning, you can see part 1 of the MIT Press Deep Learning book (available online for now, eventually MIT Press will have a real paper book) to either brush up on these or see which areas of math and CS are most relevant. Then you need to read on machine learning (there are several good books, such as Chris Bishop's and Kevin Murphy's, online videos such as Andrew Ng's coursera's class and Hugo Larochelle's videos on neural networks, and you can get a summary of many of the basic issues in chapter 5 of the Deep Learning book). Then you need to start practicing, i.e., programming some learning algorithms yourself and playing with them on data, try to compete in some Kaggle competitions, for example.  Try to become an expert at optimizing hyper-parameters and choosing models appropriately. In parallel, continue reading. If you are interested in deep learning, part 2 of my book will give you the basis for the most common algorithms. At that point you should have enough background to start a steady regimen of reading papers that tickle your fancy.

 Take an online course

The main thing I advise somebody who needs to get into machine learning is to take Andrew Ng’s online course.

I believe Ng’s course is especially to-the-point and exceptionally efficient, so it is an extraordinary acquaintance for somebody needing with getting into ML. I am astounded when individuals disclose to me the course is “excessively fundamental” or “excessively shallow”.

On the off chance that they reveal to me that I request that they clarify the contrast between Logistic Regression and Linear Kernel SVM, PCA versus Matrix Factorization, regularization, or gradient descent. I have talked with hopefuls who asserted years of ML encounter that did not know the response to these inquiries. They are for the most part plainly clarified in Ng’s course.

Top 5 Technologies to learn in 2019

In this post you will know the best programming languages/technologies should learn in 2019 to survive in IT industry with high Package.In present IT,it is very difficult to determine one particular technology as the best among others,because everyday is a evolution in computing and every single paves a way for a new technology.

But,according to the present job scenario and stack overflow popularity,the below technologies have good growing opportunities:

1. Artificial Intelligence:

It covers technologies that are used for prediction purpose.The technology stack of AI constitutes

Machine Learning

Deep Learning

Computer Vision

Human Computer Interaction

Robotics

2. Data Science:

Data Science is all about cleaning,analyzing,organizing,preparing and visualizing the data.

It requires the following things to be included:

Statistics

Machine Learning

Data mining

Data Analytics

3. Big Data and Cloud Computing:

These are another boom areas to be considered as the trending technologies in the present IT sector . It is because of the importance of data in the life of every individual and consistent improvement in social networks and eCommerce traffic.

4. Android Development:

As the internet users are more comfortable with using android apps than websites,the demand of android development becomes very high.The two popular ways of building android apps are through:

Java

Kotlin

5. DevOps:

Devops is the combination of Development and operations team in a software organization,which is advanced version to agile development.

What is the hiring Process of Data Scientist?

In this post you will know the hiring process of Data Science or Data analyst job. There are 2 kinds of companies that are looking for data scientists- Startups and MNCs.Every company has a different stack and a different dataset. This means the technical portion of a data scientist job interview is the part that will vary most between companies, and is the hardest part for which to create universally relevant questions. That said, it’s also likely to be the easiest portion for which to conclude an objective fit: they can solve problems, or they can’t.

MNCs have a bit easy work culture while on the other hand, startups will make you work day and night relentlessly.

STARTUPS:

Generally they will have an Aptitude test which includes Probability, Linear Algebra, Statistics, Distance-Speed related problems and some logical reasoning questions.

This is coupled with a programming test to check your coding prowess in SQL, R or Python to ensure you have sufficient knowledge in machine learning. If selected you will move forward for a final F2F round where you can discuss the JD and further details of the profile. Some might even have a F2F technical round before this.

MNCs:

They start with an aptitude test which includes the syllabus mentioned above and if qualified you will have a technical F2F round with a senior data analyst who will analyze you for your knowledge in data science along with your approach to some analytical problems.

Getting a job in Data Science is not difficult considering the number of hirings currently happening. Always remember-PRACTICE IS THE KEY TO BEING A KICKASS DATA SCIENTIST, just hang in there while you get the right opportunity!

What are the Hottest Jobs in Technology Market?

In this post you will know the hottest jobs in Technology Market right now. We don’t know what these fields will be called 20 years down the line but right now they’re the most trending jobs in the IT market. Right now it’s Data Science. Call it Machine Learning, Deep Learning or anything you want.

Which jobs will be employable till 2025?

These are the skill areas that the experts recommend which also has the strongest job categories - according to Bureau of Labor Statistics

Technology

Software developers (Front-end, Back-end and MEAN Stack developers): software developer jobs will grow 18.8% between now and 2024, according to the BLS, while computer systems analystjobs will increase 20.9% by 2024. Market research analyst and marketing specialist jobs, which also require those analytical skills, will increase 18.6%.

Data Scientist: Forrester predicts that by 2025, the cognitive era will create 8.9 million new jobs in data science, robot monitoring, automation specialization, and content curation. Now more than ever, we need workers who understand the technology long before we can think about how they are going to be replaced by it. Read here..

Caregiving

Related jobs: Medical technicians, Physical therapists, Workplace ergonomists
Social intelligence and Media Literacy

Related jobs: Sales representatives, Marketing specialists, Customer service representatives
Lifelong Learning

Related jobs: Teachers and Trainers
Adaptability and Business Acumen

Related jobs: Management analysts, Accountants and Auditors


The SKILLS you need to LEARN to remain FUTURE SAFE today:

The jobs that are seen in the IT sector is beating the rest of the economy by 3:1 ratio, the technology sector has grown as a pace which is faster than the private sector as a whole and has proved more resilient through the recession-and-recovery period.

The best way that one can cope up with the world is by learning the next level technology that the IT industry demands right now. You must be aware of the happenings taking places in most of the work places in your field and be prepared for it. In this way you’ll be ahead of everyone and you’ll be prepared when technology changes around you.

Currently Full Stack/MEAN Stack developer and Data Scientist tops the list of the most demanded job role with the highest salaries. Click to know more..

Talking more about the domain:

Web development:

Front-end developer - HTML, CSS, Jquery , and Angular are used for developing dynamic website interfaces. The average salary ranges from 4–5 LPA.

Back-end developer - PHP, Django, Node JS, etc. and frameworks/technologies such as NodeJS, ExpressJS, MongoDB are used in back-end development. The average salary ranges from 5–6LPA.

Full Stack/MEAN Stack developer - MongoDB , ExpressJS, AngularJS , NodeJS. The average salary ranges from 5–7LPA.

Data Scientist - R, Python, Machine Learning, Predictive modeling, Data modeling, Data Visualization, Statistics, Data Mining, Data Analysis etc. The average salary ranges from 8–9LPA.

AND TILL 2035-

1. IoT
2. Artificial Intelligence
3. Driver-less Cars
4. Block Chain Technology
5. Edge Computing
6. Data Science will still remain till then
7. Human like Robots
8. Augmented Reality

What are Machine Learning Algorithms?

In this post you will know some the mostly used Machine Learning Algorithms. If I had to choose one, I'd say my favorite algorithm is the Ensemble, which I consider my own “Master Algorithm”. Whatever algorithm you start from, you can always use an ensemble to improve it. Ensembles won the Netflix Prize and routinely show their great performance, but they are also relatively easy to understand, optimize, and inspect.  My Second Algorithm is  Logistic Regression(LR )is a very simple but efficient and flexible algorithm that can be used for many applications, notably classification, but also ranking.

The following are some of the commonly used ML Algorithms:

1. Back-propagation algorithm - a relatively simple idea that made neural networks useful.
2. Dropout - if we can consider it an algorithm (I like to think about it as a useful idea to use on neural networks). It makes you create ensemble classifiers without making many models.
3. PCA - mathematics at its best (or thereabouts). Very useful a lot of times, although probably a bit overused.
4. Perceptrons - just to study the convergence theorem, a thing of beauty.
5. Linear regression - the mathematics behind how the cost function comes is so nice.
6. Logistic regression - well, we want a simple but powerful model to do classification.
7. KNN - why not? Dumb, but very useful.
8. K-means - one of the most famous algorithms ever. There are so many much more sophisticated clustering algorithms, and k-means is still as useful as it ever was.
9. Replicator dynamics - because game theory is interesting and fun. And because it can be used as a building tool in doing a lot of completely different things, like graph matching or clustering (dominant sets).
10. Szemeredi’s regularity lemma - not a machine learning algorithms, but something that can be used on machine learning (and in any graph problems when we want to reduce them). And more importantly, because I worked on it in my master thesis, and did my first ever paper on it.
11. Support Vector Machines - because the maths there is stunning. And because all those hours of learning function analysis, are finally put to work.
12. Regularization - because nothing works without it.

Most elegant: The Perceptron algorithm. Developed back in the 50s by Rosenblatt and colleagues, this extremely simple algorithm can be viewed as the foundation for some of the most successful classifiers today, including suport vector machines and logistic regression, solved using stochastic gradient descent. The convergence proof for the Perceptron algorithm is one of the most elegant pieces of math I’ve seen in ML.  

Most useful: Boosting, especially boosted decision trees. This intuitive approach allows you to build highly accurate ML models, by combining many simple ones. Boosting is one of the most practical methods in ML, it’s widely used in industry, can handle a wide variety of data types, and can be implemented at scale. I recommend checking out XGBoost for really scalable implementation of boosted trees. Boosting also lends itselft to very elegant proofs.

Biggest comeback: Convolutional neural network deep learning This type of neural network has been around since the early 80s. Although there was a decline in interest in them from the late nineties to late 2000s, they have seen an amazing comeback in the last 5 years. In particular, convolutional neural networks form the core of the deep learning models that have been having a huge impact, especially in computer vision and speech recognition. 

Most beautiful algorithm: Dynamic programming (e.g., Viterbi, forward-backward, variable elimination & belief propagation algorithms). Dynamic programming is one of the most elegant algorithmic techniques in computer science, since it allows you to search through an exponentially-large space to find the optional solution. This idea has been applied in various ways in ML, especially for graphical models, such as hidden Markov models, Bayesian networks and Markov networks. 

Unbeatable baseline: Nearest-neighbor algorithm. Often, when you are trying to write a paper, you want to show that “your cuve is better than my curve”. :) One way to do that is to introduce a baseline approach, and show that your method is more accurate. Well… nearest-neighbor is the simplest baseline to implement, so often folks will try first, thinking they’ll easily beat it and show their method is awesome. To their surprise, nearest-neighbor can be extremely hard to beat! In fact, if you have enough data, nearest neighbor is extremely powerful! And, this method is really useful in practice.

How to become Master in Machine Learning?

In this post you will know how to master in Machine Learning.To master Machine Learning (ML) one has to be good at maths, programming and domain knowledge. Domain knowledge (Eg: how to deal with images, audio, financial time series etc) changes from one class of problem to another, so let us focus on first two.

Why Maths?:

we need maths to understand the machine learning algorithms/ models or to implement new ones. There are large number of models which are already built. Even when we are using existing models we need to understand the internal working of the algorithm so that we can tune the hyper parameters. Single model may not give best results for all the problems (no free lunch). Which model to use for the given problem is very important and to choose the right model, one needs to understand the internal working/ maths.

Thankfully you don’t need all the maths but only some subbranches:

• Linear algebra
• Probability theory
• Optimization
• Information theory and decision theory
• Calculus

Why Programming?

Programming is needed to use ML models (or build new one), get the data from various sources, clean the data, choose the right features and to validate if the model has learned correctly. Thankfully you don’t have to be an expert programmer. Some programming languages are preferred for doing ML than others because they have large number of libraries with most of the ML models already implemented.

Languages suited for ML

1. Python (best for both beginner and advanced level)
2. R (good but slow run time)
3. Matlab (good but costly and slow)
4. Julia (Future best! very fast, good, limited libraries as it is new)
5. C++ (difficult, very fast, used in production)

I recommend beginners to start with Python and learn only the required math from book and online courses. Some good books in ML and Deep Learning (DL)

Books:

• Elements of Statistical Learning
• Pattern Recognition and Machine Learning
• Deep Learning Book

Courses:

1. Machine Learning in Coursera
2. Deep Learning in Coursera

For advanced level you need to get the intuition of how the models work (not just reading math equations). For that you need to spend lot of time working on various ML/ DL problems. At the end of the day ML is more of an art, you become good by practice. You also need to read lot of papers.

What Should I learn first Machine Learning, AI or Data Science?

In this post you will understand which course you should learn first, how to learn these course and what is average salary of these courses.Machine Learning is a specific set of techniques that enable machines to learn from data, and then make predictions. What you must be concern right now is to first understand the point in time when machines reach a higher level of intelligence than humans and will in turn take over the world -

However, before getting into Deep Learning and AI, you better learn Machine learning first.

You can think of Deep learning, Machine Learning and Artificial Intelligence associated within each other. If you look at it, Deep learning is a subset of Machine Learning and Machine Learning is a subset of AI, you can call it an umbrella term because it is a computer program that does something smart.

Machine Learning is basically a current application of AI based around the idea that we should be able to give machines access to data and let them learn for themselves. In addition to this, another field of AI – Natural Language Processing (NLP) – has become a source of hugely exciting innovation in recent years which heavily relies on ML.

Deep Learning is a subset of Machine Learning, mostly when people use the term deep learning they are referring to deep artificial neural networks. Deep artificial neural networks are sets of algorithms that sets new records in accuracy for many important problems i.e. image recognition, sound recognition, recommender systems, etc.

Artificial Intelligence is defined as the engineering and the science that is required to build intelligent machines.

All these are basically the branches of data science and how it is compared with fields mentioned above, e.g. machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.

So, if you’re looking to get into machine learning/AI or Deep Learning, my best suggestion for you is to first learn data science, then maybe you could get into depth with machine learning and deep learning.

So how can I start with data science?

The first step to learning data science is by asking yourself, “how do I actually learn data science?”

In order to learn data science you must know these:

Do you love data or numbers? - You must love numbers if you plan on taking data science. Without motivation you might end up halfway there believing you can’t do it.

You can learn data science by doing it - 90% of the work will be data cleaning. Having a grasp of few algorithms is better than not knowing anything at all.

You should know how to code using R and Python.

Where can you learn data science?

These are some helpful resources:

Dataquest - you can learn data science in your browser, work on projects and build a portfolio.

Elements of statistical learning - good machine learning books.

Udemy - online courses but only certification.

Khan Academy - good statistics and algebra content.

Udacity - upgraded online courses but just nano degree and certificates and no job assurance.

edWisor - online career path, good learning skills, job assurance - job oriented platform.

Resources on their own aren't useful you need to find a context for them - research on all the above resources and choose the best option.

What is Machine Learning?

In this post you will learn about Machine Learning.Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that’s gaining fresh momentum.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.

Resurging interest in machine learning is due to the same factors like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage have made more popular than ever.

All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. Machine learning studies computer algorithms for learning to do stuff.The emphasis of machine learning is on automatic methods. In other words, the goal is to devise learning algorithms that do the learning automatically without human intervention or assistance. The machine learning paradigm can be viewed as “programming by example.” Often we have a specific task in mind, such as spam filtering. But rather than program the computer to solve the task directly, in machine learning, we seek methods by which the computer will come up with its own program based on examples that we provide. Machine learning is a core subarea of artificial intelligence. It is very unlikely that we will be able to build any kind of intelligent system capable of any of the facilities that we associate with intelligence, such as language or vision, without using learning to get there. These tasks are otherwise simply too difficult to solve. Further, we would not consider a system to be truly intelligent if it were incapable of learning since learning is at the core of intelligence. Although a subarea of AI, machine learning also intersects broadly with other fields, especially statistics, but also mathematics, physics, theoretical computer science and more.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:

• The heavily hyped, self-driving Google car? The essence of machine learning.
• Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
• Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
• Fraud detection? One of the more obvious, important uses in our world today.

Best Programming Languages for AI Development

In this post you will know the top programming languages for Artificial Intelligent Development. Artificial Intelligence (AI) is the study of computer science which focuses on developing software or machines that exhibit human intelligence. Developing computer systems that equal or exceed human intelligence is the crux of artificial intelligence. AI has had some success in limited domains.

The central goals of AI research are reasoning, knowledge, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field’s long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI which includes various versions of search and mathematical optimization, logic, and methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology.

Here are the top 3 programming languages for building AI :

1. Python:

One of the leading languages used for developing AI Projects is Python which is because of its simplicity and easy coding behavior and is especially used in health-related startups involving AI.The Python community has developed a lot of libraries and extensions to handle most common tasks used in AI projects like Plotting Graphs, Processing Data etc easily. There are also packages available for almost all AI components ranging for Bayesian Networks to Artificial Neural Networks. Mostly AI developers suggest Python for Artificial Intelligence development.

Best features of Python:

• Practical versions of libraries. (tensorflow, numpy, VTK, Simple AI)
• Open Source Libraries. (Numpy, Matplotlib)
• Great Framework. (scikit-learn)
• Effortless prototyping and building of apps.
• Easy to learn.
• Open Source.
• Modular programming.
• Fast testing

2. JAVA

Java is in the top five because of its familiarity and easy to use features. This OOP language allows easy coding of algorithms which covers the major part of AI. It is portable and it offers inbuilt garbage collection. AI is closely connected with search algorithms, genetic programming and the use of artificial neural networks. Java in artificial intelligence sphere may be more than useful. Programming AI in Java has many benefits as it is easy to use, easy to debug, facilitates visualization and has better user interaction. Another reason for programming AI in Java is the incorporation of Swing and SWT. These features make graphics and interfaces look appealing and sophisticated. Java is good but not as fast as C ++.

Best features of JAVA:

• Strong Libraries. (Weka, Mahout)
• Easy usability.
• Scalability.
• In-built Garbage Collection.
• User interaction.
• Fast.

3. C++

The very reason C++ is used in AI solutions is its speed; it is probably the fastest language out of all. Therefore, whenever speed is the prime concern of any AI developer, C++ is opted.Statistical AI techniques such as neural networks are common examples of this. Its ability to talk at the hardware level enables developers to improve their program execution time. C++ is extremely useful for AI projects. Algorithms are written extensively in the C++  for speed execution. AI in games is mostly coded in C++ for faster execution and response time.

Best features of C++:

• Speed.
• Easy to learn.
• Real world implementations.
• Genetic Algorithms
• Neural Network Implementations.
• Mission Critical Systems

How to prepare a career in Machine Learning

In this post you will know Tips to prepare a career in Machine Learning. Well, the good news is that a lot of people are interested in machine learning. It is the topmost career choice for many people today. Its amazing capabilities of making machines understand data are impeccable, and if you are looking for any in-demand career option, there isn't a better option than machine learning. It's exploding everywhere in emails and smartphones as smart algorithms are being used in every field.

The following Tips to Prepare a career in Machine Learning:

1. Understand The Concept

More than anything, you need to first learn what machine learning is and how it works. In simple words, it is a process in which computers are enabled to learn automatically, make decisions and increase experience without human supervision. The main aim behind machine learning and all its algorithms is to let computers learn and evolve automatically without needing human guidance.
Although it may sound a lot similar to data analysis, these are two different yet related fields. Data analysis involves analyzing data to provide insights and both the receiving and working ends of this process involve humans. In machine learning, computers have to analyse data, interpret trends, make decisions and act accordingly.
 
2. Encourage Your Curiosity

Whatever you have heard, read or seen about machine learning, it is only going to get bigger in the future. Our coming generations will see a completely new era of machine learning and artificial intelligence. To adjust to these growing demands, you need to encourage your curiosity to constantly keep learning and most importantly keep inventing.
Some curiosity is good as it will help you learn more about new technologies, new inventions and understand why some things happen in the way they happen.
 
3. Learn To Translate Business Problems Mathematically

One thing that you always need to keep in mind is that machine learning is much more than it seems. It is not a bunch of engineers trying to work their way through artificial intelligence. It takes logical minds with extreme knowledge and interest in maths, data analysis and technology.
You need all of these together, and if you miss even one of the elements, it won't work fine. Knowledge of these three things will help you transform business problems into machine problems with mathematics.

4. Learn To Work In A Team

Machine learning may bring to mind a picture of one person working in a big lab containing machines and computers. This may have been true some years ago, but, now, machine learning is teamwork. You need a team to work on various aspects, and it is not possible to work solo. So, you need to be able to work in a team for this career.

5. Get Familiar With Data Analysis

As already discussed, data analysis is related to machine learning. If you already have experience in data analysis, you are automatically crossing one thing from your list of things you need to have knowledge of. Additionally, it will offer you an analytical view of things. You'll be able to figure out patterns, read data for insights and get an overall clear visual of what is happening.

6. Learn Python And Understand ML Libraries Usage

In the 3rd point we talked about how you need to know maths, have knowledge of data analysis and knowledge of technology. Learning python and machine learning libraries is the technical part. At some point, you should be able to implement your theoretical knowledge to machines and make these machines learn and improvise. For that to happen, learning programming and understanding machine learning libraries is a must.

7. Catch Up On Industry Updates

Any data-driven job role has some specific goals relevant to the industry. Same is the case with machine learning. You will have specific goals to fulfil relevant to the industry you want to work in. Hence, to build a career in machine learning, stay updated with industry details. Gain more knowledge of specific requirements and stay up to date with what is going on in the industry including new inventions.

8. Try Online Courses And Classes

After all these things, checkout some online courses and classes and start learning. Don't wait for someone to come by and teach you everything; teach yourself. Learn as much as you can. Meet people from the same field and ask them what knowledge you need.
Take up unpaid internships under successful machine learning engineers even if that means only observing what they do. You'll learn more from this than any book. So, put on your work shoes and get going without any delay.

9.Understanding Machine Learning With An Example

For instance, if you run a grocery store and you need to figure out how to distribute coupons based on a customer's previous purchase pattern, a data analyst will sit with previous data and analyse the patterns. This will go on for every customer manually.
A machine learning person, on the other hand, will form an intelligent system to automatically generate coupons with data interrelationships. This will enable everything to run smoothly without human intervention.

Difference Between Artificial Intelligence ,Machine Learning and Deep Learning

In this post you will learn what is AI and Machine Learning and Difference between them. As we reached the digital era,where computers became an integral part of the everyday life style. Have you heard about Artificial Intelligence? How about Machine Learning? These words are familiar to us and can be used interchangeably, The more people used it, the more confusing it gets.

What is Artificial Intelligence?

AI is the like creating intelligence artificially. Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans. AI has had some success in limited, or simplified, domains. It includes all kinds of tasks such as doing creative work, planning , moving around, speaking, recognizing objects and sounds, performing social or business transactions and a lot more. 

What is Machine Learning?

A type of AI that can include but isn’t limited to neural networks and deep learning. Generally, it is the ability for a computer to output or do something that it wasn’t programmed to do. More and more plans to try different approaches to use AI leads to the most promising and relevant area which is the Machine Learning. The most common way to process Big Data is called Machine Learning.

Difference Between AI ,Machine Learning and Deep Learning:

 Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart.we can consider Machine Learning as a subset of AI. When Artificial intelligence is the whole idea of a technology that behaves like a human, machine learning algorithms are about finding patterns and invariant in big data. These self-learning algorithms enable the machines to learn from data sets (inferential statistics) and they are a series of steps that lead to the completion of a task.

Artificial intelligence encompasses other areas apart from machine learning such as knowledge bases, natural language processing, robotics etc. Also symbolic logic (rules engines, knowledge bases, expert systems etc.), Bayesian statistics, robotics and evolutionary algorithms can be described as AI, however none of them are machine learning.

Digital era, brought an explosion of data in all forms and from every region of the world. This data, known simply as Big data, is drawn from sources like social media, internet search engines, e-commerce platforms etc.. This enormous amount of data is readily accessible and can be shared through various applications like cloud computing. However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to Artificial Intelligence(AI) systems for automated support.

The most common way to process Big data is called Machine Learning. It is a self-adaptive algorithm that gets better and better analysis and patterns with experience or with newly added data.

For example, if a digital payments company wanted to detect the occurrence of or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set, and point out any anomaly detected by the pattern.

A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensure, while a deep learning non-liner technique to weeding out a fraudulent transaction would include time, geographic location, IP address, type of retailer, and any other feature this is likely to make up a fraudulent activity.