Category: Analytics

Before discussing the ways in which you can learn more about machine learning, we would like to discuss, what the subject matter actually is. Machine learning is basically teaching a computer how to make decisions with the help of relevant data. It is very important for the computer to be able to understand patterns without being fully programmed. The demand for machine learning is an all-time high. It is a skill set which you want to possess, especially in this computer savvy era. No matter if you want to be a software engineer, a business analyst or for that matter, a data scientist machine learning will lay a strong foundation for all that and more.
It is very important to note, that data is king, from the smallest of companies to giant conglomerations, everyone wants to harness their data, so a course in machine learning will help you bag a good job and if not that, then an internship at a good firm. This a one-of-a-kind course, which can be a lot of fun if you go about it right, so pull up your socks and we will point out 7 lessons which can tea you more about machine learning basics without spending a lot of money.

Have an understanding of python

Having an understanding of Python programming will take you a very long way as far as machine learning is concerned. If you do not know about Python, then don’t worry, you won’t have to join a course for understanding it. There is a lot of online study material available with regards to Python programming. You can go through tutorials and virtual classes, and gather a basic understanding of the same. Once you have ample knowledge and experience in Python as well as computer programming, machine learning will certainly become easier for you to grasp.

Deepen your understanding of statistics

A good understanding of descriptive statistics, data visualization as well as data distribution will take you a long way. This method becomes a prerequisite when evaluating the skill of a machine learning model it also helps when selecting the model configuration as well as the final model. Statistics also comes in very handy, when the presentation of the model is made in front of a stakeholder. Statistics are definitely one of the most important requirements, so browse through your old books, hone your skills by practicing before dipping your toes into the subject of machine learning.

Learn as much theory as you possibly can

We know this isn’t the fun part, but once you get through this, you will be able to understand machine learning basics a lot better. Learn the fundamentals of machines learning and go through all the theories, in fact, memorize them. Trust us when we say that they will come in handy. There is a lot of study material available on the internet, so you have no excuses, but to absorb all that knowledge and charge ahead into the world of machine learning.

Dive into target practice

As the clichéd goes, practice makes perfect and machine learning is no different. Start by learning and practicing the workflow. Try doing everything first hand, you will make mistakes, but you will learn from them. Go ahead and practice on data cleaning and collection, model building, evaluation, tuning, etc. This way you will become more intuitive with regards to the many models and try to work on your own.

Skills which will help to lay a foundation in machine learning

You need to have a deep understanding of the theories and know how to use them in practical application. Having an understanding of algorithms is what you will have to develop eventually with learning and practice. You have to give this subject ample time and invest in a more academic setting. Go through the course notes well, and practice them to hone your skills

Go through Python packages

As mentioned before, the understanding of Python programming is absolutely imperative before venturing into machine learning. You should also learn about Numpy, Pandas, and Matplotlib as well.

Deep learning in Python

If you want to dig much deeper into machine learning basics, there are few online books which you must go through.

• Theano: it a python library, which allows you to evaluate mathematical expressions efficiently.
• Caffe: speed and modularity are kept in mind as far as Caffe is concerned.

Once you go through the aforementioned steps thoroughly, you will definitely develop a fine understanding of machine learning. The use of algorithms in Python, and understanding machine learning about algorithms will become very simple for you.

The executives in our current business generation have seconds to sell their idea. Thus the presentations done with these seconds needs to be very useful. The visual elements of a presentation play a vital role in the effectiveness of it. Today’s business presentations use advanced business visualisation tools to add clarity to their complex business messages. When it comes to integrating insights or trends to graphics that can be read by professionals and novices, QlikView and Tableau are the two best choices you can find.

QlikView and Tableau

Both QlikView and Tableau provide top class data manipulation tools to its users. The business intelligence capabilities of these two make them toppers of the market. However, fundamentally QlikView and Tableau are unique enough to perform specific types of applications.

QlikView

The company named Qlik founded in 1993 created the QlikView software. According to their website, this data visualisation software serves 34,000 customers around the world.
The unique features making QlikView popular are the following:

• Quick Analysis – The high optimising nature and scalability of QlikView provides it access to a vast data set instantly. Resulting in a quicker delivery of search results.
• User-oriented Interactivity – The large set of visualisation features help you to find insights in the nick of time.
• Find Association – The association between the data sets can be easily found using QlikView. It also lets you conduct a search across all the data and filter the result to match your needs.

However, few drawbacks are observed in this software. A common issue with QlikView is that the many intricacies and facets in it lead to difficulty in learning the software. The back end which is not so user-friendly and the front end which requires occasional IT support also adds up to the drawbacks.

Tableau

Tableau was found ten years after Qlik. Tableau spotted the vulnerability of QlikView in data visualisation and analytics, and they capitalised on it. Today, Tableau serves more than 23,000 customer accounts and are growing fast.
The significant benefits of Tableau are following;

• More comfortable usage – Even if you have limited experience, using Tableau you can generate interactive business intelligence reports of good quality.
• Speed – With a few minutes of time, you can create interactive visualisations in Tableau.
• Interactive Data Visualization – The superior data visualisation capabilities of Tableau provide interactive reports and recommendations according to your need.
• Cost-effective – Along with the time and effort, this technology saves money as it let the companies avoid investing in additional IT resources.

One major drawback of Tableau is said to be the need for ETL (Extract, Transform and Load) to pull the required data out of your system. Also, the number of integrative data sources are less than of QlikView.
Selecting one from this two excellent software is entirely dependant on your type of needs. If you are required of excellent data visualisation with user-friendly drag and drop format, Tableau will be the best choice. QlikView is your best option if your organisation is large and vast sets of data are being managed.

Most people are very confused when they are trying to find out the meaning and rules of machine learning program algorithms while other IT professionals think machine learning program algorithms are basically set of the effective rules and protocols that are followed in machine learning.  Now it has to be noted that there is no fixed algorithm for machine learning. In simple terms, there is no fixed theorem known as no free lunch, as every algorithm is different for the different problem.
For example, one cannot say that the neural network performs better than decision trees or the other way around. Since everything is linked with each other like for instance the structure and size of a database are linked with both network and decision trees. Thus, for getting the favorable outcome, one should try out variable tests to select the winner. One should not directly jump to any conclusion. This article will enlighten the reader about some of the algorithm that has to be considered in machine learning. Some of them are:

Big Principle

This is the principle which states that machine learning can be defined in the terms of the target function. This means that input variables are a target function of the output variable.  Considering x as the input variable and y as the output variable then the target function would be Y=F(X). Thus it can be said that with the help of a big principle in machine learning it would help the system in making future predictions. The most common form for future prediction is through mapping a target function, meaning with the help of the equation Y=F(X) predictions of Y could be made keeping the variable x in consideration.  This type of mapping in general terms is known as predictive analysis or predictive modelling.

Linear Regression

Linear regression tends to form the foundation of machine learning. While big principle deals with only two variable x and y to make predictions, linear regression makes use of many different algorithms from various fields to drive a coefficient (B).  One of simplest example of this type of format can be expressed as y=B0+B1*X. Now it has to be noted that there is no fixed method to study linear regression. Some researchers say that linear regression can be learned by analyzing linear algebra solution while others say they can be easily learned by studying the optimization of gradient descent.

Logistic regression

Logistic regression is another algorithm to study machine learning. In logistic regression, the main goals are to find the worth of the coefficients by studying the weights on each input variable. Now this logistic regression is very different from linear regression as the as in linear regression the prediction is completely based on logistic function.

Linear discriminant analysis

This discrimination is based on two classes. Now if the problem requires discrimination of more than two classes then the linear classification technique is followed. For linear discrimination analysis, two things are calculated, first the variance of all classes and second the mean value of each class.

Classification of regression trees

Classification of regression trees is one of the modern ways of machine learning. For this model, a binary tree is considered where is each node is the representation of the split variable and the input variable.

Naïve Bayes

This is also a modern-day algorithm model in this case two probabilities are calculated. The first is calculated on the basis of each class and the second probability is calculated considering the value of x. once the calculation is done and the final outcome is given on the basis of Bayes theorem.

K Nearest Neighbors

In this algorithm, the prediction is given after calculating the entire set of similar instances (k) and then the output is provided for the same. Now with the help of Euclidean distance, a number is derived which basically shows the distance between the two input variables.

Learning Vector Quantization

With the help of learning vector Quantization, IT professionals can choose a variety of training instances. In learning vector quantization there is a presence of coded vectors. The IT professionals choose these codes randomly to determine the number of iterations in the training set.

Support vector machines

This algorithm takes hyperplane in consideration meaning a hyperplane is used to differentiate variables. The variables are separated on the basis on 1 and 0. With the use of this algorithm, IT professionals are able to find the coefficient of variables which separated by a hyperplane

Bagging and random forest

Machine learning training is one of the best ways to learn about bagging and random forest since machine learning training teaches the user about the benefits of using this algorithm. This algorithm uses the bagging method for calculating a data from a sample.

Many people ask if it’s possible to increase their earnings by moving into data analytics. The answer, clear and concise, is “YES”! But, there are some requirements one needs to meet for that.
Data analytics is today what computer was when it first came around in the 1960’s and 70’s. Network, as a newfound tool, attracted the attention of engineers and scientists across all fields with the new use of the machine being discovered almost every day due to its ability to fast and error-free calculation which later updated to storing of information and even receiving and transmitting them very fast. This led to widespread applications of the computer during that time which has only increased since then. Data analytics is in a similar situation today.
Also Article : Career Opportunity in Data Analytics

The sudden interest in Data Analytics

Data analytics jobs have emerged as a significant field of attention in recent years due to the advancement of computational and mathematical models by companies such as Google, Amazon, Microsoft, etc. Add into it the near-universal connectivity and access provided by technologies such as GPS, cloud and smartphones in modern time, and we get a giant network of interconnected devices continually generating data which can be used by the organisations for their benefits. The analysis and of this data and concluding from it falls under data analyst responsibilities which is in significant demand right now as companies are willing to offer much pay package and amenities to data analysts.
However, people often tend to forget that the salary offered to them by companies comes from the profits earned by it from the end user side. So, unless a working professional can add a sort of value to the company, he/she cannot expect an excellent salary even from the data analyst job.
Some of the factors required from a person for value creation are –

• Mindset
• Knowledge
• Skills

A data analyst career needs an analytic mindset just like a career in computers required a computing mindset earlier. All of the students and professors of data analytics must ponder over the points mentioned below –

1. All individuals related to the field of data analytics must be able to make sense out of a sense of numbers which seem unconnected at first glance. They must possess an eye for patterns and the ability to connect those patterns with the information available to them.
2. The knowledge of analytics has evolved a lot over the years in the form of optimisation models, machine learning, statistical models, pattern recognition techniques, artificial intelligence, etc. Such a dynamic evolution of the field demands the professionals to be at their toes always. Any person looking to thrive must undergo proper data analytics training to survive the competition in the area.
3. Just as the advent of the computer prompted professionals to learn programming and coding in the 60’s, data analytics requires the learning of analytical tools and languages such as Java, R, C++, etc. An analytics certification goes a long way in establishing the qualification of the person in the field.

So, the people who ask whether it is possible to double their salary by turning into data analytics must ask themselves whether they possess the mindset required for it or not first. It’s true that a data analyst salary is excellent compared to another field in today’s time, but one needs to do a lot of hard work and must possess the ability to impart value to his/her organisation to achieve that. Getting a top rank in analytics doesn’t guarantee success. Instead one must possess the ability to apply the knowledge for the company to make it.
Related Article : Data Analytics Popular Algorithms Explained

New To Data Science? Start With These 10 Python Libraries

Data Analysis has become the forefront of every organisation. Companies combine Big Data and cutting-edge data analytics to arrive at actionable insights that benefit business performances.
We know that Data Science has been dubbed as one of the sexiest jobs of the 21st century. If you’ve always wanted to learn Data Science, then R and Python are your bread and butter. To get started, here are the top ten Python Libraries you should sink your teeth into:

• NumPy

NumPy stands out as a beginner-friendly Python library. It features sophisticated broadcasting functions, powerful multidimensional array objects, and matrices. It doesn’t use loops and lets you transfer data to external libraries that are written in C, C++ or Fortran Code.

• SciPy

SciPy is NumPy’s best friend and relies on its speedy N-Dimensional array manipulation. SciPy offers users various numerical routines such as numerical integration and optimisation. SciPy, when coupled with NumPy, is used to solve multiple tasks related to integral calculus, linear algebra, probability theory, and others. The latest editions of SciPy involve significant build improvements and bundle the new BLAS and LAPACK functions.

• Pandas

Pandas is a Python Library that lets you translate complex operations with data in just a few commands. It includes built-in features like grouping, time-series functionality, filtering, and lets you combine data sets. Its numerous bug fixes and API improvements make it a must-use library for Data Science enthusiasts. Additionally, Pandas lets you perform custom operations.

• Matplotlib

Matplotlib is a low-level Python library used for data visualisation in interactive environments and hardcopy formats. It lets you create graphs, histograms, pie charts, scatterplots, and more. There’s a colourblind-friendly colour cycle feature, and the latest versions include support different GUI backends on operating systems and lets you export graphics/images in various formats like PDF, SVG, GIF, JPG, BMP, etc. The legends and graph axes are automatically aligned, and when you use it with the iPython Notebook, it becomes your visualisation playground, literally.

• Scikit-Learn

Scikit-Learn lets you quickly implement various Machine Learning Algorithms on your datasets. It gives you apply algorithms on tasks related to logistic regression, classification, clustering, etc. It’s a popular module that’s built on top of the SciPy library and is perfect for beginner and advanced Data Scientists.

• Theano

Theano is a Python library explicitly used for mathematical computations. It lets you optimise and evaluate mathematical expressions to your liking and uses multi-dimensional arrays for blazing fast calculations. It also works as a core computational component in libraries like the PyLearn 2.

• Statsmodels

Statsmodels lets you statistically explore data and includes various classes and functions that help you estimate statistical models. Its ‘estimator’ brings a list of ‘result statistics’ that let you test your analyses against existing statistical packages which are released under an open-source license.

• Plotly

Plotly lets you create complex visualisations, maps, financial charts and various graphical presentations that meet publication quality online. It works with interactive web applications and bundles features such as ternary plots, 3D charts, contour graphics, etc. Crosstalk integration, “multiple linked views” and animation generation make it one of the hottest visualisation tools in Data Science.

• Bokeh

Bokeh lets you create scalable and interactive visualisations using JavaScript widgets. It includes a small zoom tool, customizable tooltip field enhancements, linking plots, and many versatile (but interactive) styling and graphing features.

• Gensim

Gensim is a free Python library used for building scalable semantic statistics. Its retrieves structurally similar documents and speedily implements Machine Learning algorithms for useful statistical analysis. Perfect for topic modelling with large data-sets and is used popularly in text mining projects.
Conclusion
Use these libraries to kickstart your ML projects and avoid writing algorithms from scratch. They save time, are ideal for beginners and advanced Data Scientists, and are highly recommended in the Data Science community worldwide.

It’s no new fact that the present generation of information thrives on data. If you think regarding digital bits, every company targeting any specific field has tons of data to manage. And eventually, it seems human hands are limited to process all of them. Thus Machine Learning comes to our rescue and as the name might suggest- we teach the machines how to do their stuff and get the work rolling out of them. To define Machine Learning, one may say that it is a core part of the developing technology called Artificial Intelligence (AI), in which you can program the devices to work by themselves on the input data without the need of an explicit programmer.
Also Read : How to Start a Career in Machine Learning?
The advancement in this sector can enhance the growth of a nation. Machine learning programs are evolving on a reasonable scale in India, and several companies are welcoming AI engineers. For any person interested in the field of science and robotics, Machine Learning courses are an exciting option with lots of scope for growth. There are several methods which are taught in the course mainly involving in-depth analysis of mathematical discourses, computer programming and formation of networks and algorithms. While Python Machine Learning is considered to be the most preferred language for coding- Java, C++, R and other words are also convenient options for those who are well versed in it.
Where can you explicitly see the outcomes of Machine Learning? Take for instance Google and Amazon. That’s where you look at Machine Learning on a smaller scale to enhance your web surfing into a personalised one. It’s, however, a developing field and industries are working on it on a massive scale to create such AI programs which can quickly change your daily lifestyle. The technology of Internet of Things (IoT) and Cloud Computing and its likes, all submerge into the growing implementation of Machine Learning to enhance objects and gadgets into being “Smart” for themselves. The potential massiveness of such a concept is endless from the current standpoint.
Managing data can be crucial in interdisciplinary fields like education and thus, Machine Learning comes in very handy in such areas. The current pedagogy of classrooms is an evolving one where the teacher is necessarily required who is thoroughly learnt in the subject. Smart classrooms have been developed into expanding the database of resources. But real enhancement in the school of fifty individuals can come only by following a method where every child benefits and is given resources according to their needs. That’s where you need to learn digital systems which can record every individual’s performance and provide an accurately customized report of their specific needs.
Having talked about classrooms, one may analyze other such institutions where a large number of data needs to be managed. Take a law court for instance which sees multiple cases each day of varying degree of importance.
In the current scenario, a lot of handworks is needed to collect and categorize data. Machine learning can be boons at this point to several lawyers who won’t need overspending time on an essential collection of data. Even manual labor in industrial sites which is often risky for workers can be replaced by automatically functioning machines which can finish the task faster and more efficiently.
Health sectors can utilize a digital system to self-diagnose patients and cross-referencing of symptoms. Your daily life at home can become very easy with automatically working ACs, refrigerators, washing machines and any switch operated device. With the current rate of development, one can easily say that Machine Learning is positively here to evolve the world.
Related Articles:

• What is The Easiest Way To Learn Machine Learning?
• What are The Skills You Need to Become a Machine Learning Engineer?
• 10 Machine Learning Use Cases You Should Know About

Infographics: Who Am I? – Data Scientist

Everyone wants to become a data scientist, after all, it is considered and labelled as the ‘sexiest job of the 21^st century!’ There is a lot of mystic in the tile ‘Data Scientist’. The data scientist is considered as a wizard, with mystical skills, which when applied can get great insights from the existing data, which holds the key to the future success. But as much as we would like to believe otherwise, it is the way the data is interpreted, analysed and methods of data science applied by the data scientist that gets us excellent results.
In this infographics let’s understand “Who is Data Scientist?”