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The Soaring Importance of Apache Spark in Machine Learning: Explained Here

The Soaring Importance of Apache Spark in Machine Learning: Explained Here

Apache Spark has become an essential part of operations of big technology firms, like Yahoo, Facebook, Amazon and eBay. This is mainly owing to the lightning speed offered by Apache Spark – it is the speediest engine for big data activities. The reason behind this speed: Rather than a disk, it operates on memory (RAM). Hence, data processing in Spark is even faster than in Hadoop.

The main purpose of Apache Spark is offering an integrated platform for big data processes. It also offers robust APIs in Python, Java, R and Scala. Additionally, integration with Hadoop ecosystem is very convenient.


Why Apache Spark for ML applications?

Many machine learning processes involve heavy computation. Distributing such processes through Apache Spark is the fastest, simplest and most efficient approach. For the needs of industrial applications, a powerful engine capable of processing data in real time, performing in batch mode and in-memory processing is vital. With Apache Spark, real-time streaming, graph processing, interactive processing and batch processing are possible through a speedy and simple interface. This is why Spark is so popular in ML applications.

Apache Spark Use Cases:

Below are some noteworthy applications of Apache Spark engine across different fields:

Entertainment: In the gaming industry, Apache Spark is used to discover patterns from the firehose of real-time gaming information and come up with swift responses in no time. Jobs like targeted advertising, player retention and auto-adjustment of complexity levels can be deployed to Spark engine.

E-commerce: In the ecommerce sector, providing recommendations in tandem with fresh trends and demands is crucial. This can be achieved because real-time data is relayed to streaming clustering algorithms such as k-means, the results from which are further merged with various unstructured data sources, like customer feedback. ML algorithms with the aid of Apache Spark process the immeasurable chunk of interactions happening between users and an e-com platform, which are expressed via complex graphs.

Finance: In finance, Apache Spark is very helpful in detecting fraud or intrusion and for authentication. When used with ML, it can study business expenses of individuals and frame suggestions the bank must give to expose customers to new products and avenues. Moreover, financial problems are indentified fast and accurately.  PayPal incorporates ML techniques like neural networks to spot unethical or fraud transactions.

Healthcare: Apache Spark is used to analyze medical history of patients and determine who is prone to which ailment in future. Moreover, to bring down processing time, Spark is applied in genomic data sequencing too.

Media: Several websites use Apache Spark together with MongoDB for better video recommendations to users, which is generated from their historical data.

ML and Apache Spark:

Many enterprises have been working with Apache Spark and ML algorithms for improved results. Yahoo, for example, uses Apache Spark along with ML algorithms to collect innovative topics than can enhance user interest. If only ML is used for this purpose, over 20, 000 lines of code in C or C++ will be needed, but with Apache Spark, the programming code is snipped at 150 lines! Another example is Netflix where Apache Spark is used for real-time streaming, providing better video recommendations to users. Streaming technology is dependent on event data, and Apache Spark ML facilities greatly improve the efficiency of video recommendations.

Spark has a separate library labelled MLib for machine learning, which includes algorithms for classification, collaborative filtering, clustering, dimensionality reduction, etc. Classification is basically sorting things into relevant categories. For example in mails, classification is done on the basis of inbox, draft, sent and so on. Many websites suggest products to users depending on their past purchases – this is collaborative filtering. Other applications offered by Apache Spark Mlib are sentiment analysis and customer segmentation.


Apache Spark is a highly powerful API for machine learning applications. Its aim is wide-scale popularity of big data processing and making machine learning practical and approachable. Challenging tasks like processing massive volumes of data, both real-time and archived, are simplified through Apache Spark. Any kind of streaming and predictive analytics solution benefits hugely from its use.

If this article has piqued your interest in Apache Spark, take the next step right away and join Apache Spark training in Delhi. DexLab Analytics offers one the best Apache Spark certification in Gurgaon – experienced industry professionals train you dedicatedly, so you master this leading technology and make remarkable progress in your line of work.


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Know the 5 Best AI Trends for 2019

Know the 5 Best AI Trends for 2019

Artificial Intelligence is perhaps the greatest technological advancement the world has seen in several decades. It has the potential to completely alter the way our society functions and reshape it with new enhancements. From our communication systems to the nature of jobs, AI is likely to restructure everything.

‘Creative destruction’ has been happening since the dawn of human civilization. With any revolutionary technology, the process just speeds up significantly. AI has unleashed a robust cycle of creative destruction across all employment sectors. While this made old skills redundant, the demand and hence acquisition of superior skills have shot up.

The sweeping impact of AI can be felt from the fact that the emerging AI rivalry between USA and China is hailed as ‘The New Space Race’! Among the biggest AI trends of 2018 was China’s AI sector – it came under spotlight for producing more AI-related patents and startups compared to the US. This year, the expectations and uncertainties regarding AI both continue to rise. Below we’ve listed the best AI trends to look out for in 2019:


AI Chipsets

AI wholly relies on specialized processors working jointly with CPU. But, the downside is that even the most innovative and brilliant CPUs cannot train an AI model. The model requires additional hardware to carry out higher math calculations and sophisticated tasks such as face recognition.

In 2019, foremost chip manufacturers like Intel, ARM and NVidia will produce chips that boost the performance speed of AI-based apps. These chips will be useful in customized applications in language processing and speech recognition. And further research work will surely result in development of applications in fields of automobiles and healthcare.

Union of AI and IoT

This year will see IoT and AI unite at edge computing more than ever. Maximum number of Cloud-trained models shall be placed at the edge layer.

AI’s usefulness in IoT applications for the industrial sector is also anticipated to increase by leaps and bounds. This is because AI can offer revolutionary precision and functionality in areas like predictive maintenance and root cause analysis. Cutting edge ML models based on neural networks will be optimized along with AI.

IoT is emerging as the chief driver of AI for enterprises. Specially structured AI chips shall be embedded on majority of edge devices, which are tools that work as entry points to an entire organization or service provider core networks.

Upsurge of Automated ML

With the entry of AutoML (automated Machine Learning) algorithms, the entire machine learning subject is expected to undergo a drastic change. With the help of AutoML, developers can solve complicated problems without needing to create particular models. The main advantage of automated ML is that analysts and other professionals can concentrate on their specific problem without having to bother with the whole process and workflow.

Cognitive computing APIs as well as custom ML tools perfectly adjust to AutoML. This helps save time and energy by directly tackling the problem instead of dealing with the total workflow. Because of AutoML, users can enjoy flexibility and portability in one package.

AI and Cyber security

The use of AI in cybersecurity is going to increase by a significant measure because of the following reasons: (i) there a big gap between the availability and requirement of cybersecurity professionals, (ii) drawbacks of traditional cybersecurity and (iii) mounting threats of security violations that necessitate innovative approaches. Depending on AI doesn’t mean human experts in the field will no longer be useful. Rather, AI will make the system more advanced and empower experts to handle problems better.

As cybersecurity systems worldwide are expanding, there’s need to cautiously supervise threats. AI will make these essential processes less vulnerable and way more efficient.

Need for AI Skilled Professionals:

In 2018, it was stated that AI jobs would be the highest paying ones and big enterprises were considering AI reskilling. This trend has been carried over to 2019. But companies are facing difficulties trying to bridge the AI skills gap in their employees.

Having said that, artificial intelligence can do wonders for your career if you’re a beginner or advanced employee working with data or technology. In Delhi, you’ll find opportunities to enroll for comprehensive artificial intelligence courses. DexLab Analytics, the premier data science and AI training institute, offers advanced artificial intelligence certification in Delhi NCR. Check out the course details on their website.


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Decoding the Equation of AI, Machine Learning and Python

Decoding the Equation of AI, Machine Learning and Python

AI is an absolute delight. Not only is it considered one of the most advanced fields in the present computer science realm but also AI is a profit-spinning tool leveraged across diverse industry verticals.

In the past few years, Python also seems to be garnering enough fame and popularity. Ideal for web application development, process automation, web scripting, this wonder tool is a very potent programming language in the world. But, what makes it so special?

Owing to ease of scalability, learning and adaptability of Python, this advanced interpreted programming language is the fastest growing global language. Plus, its ever-evolving libraries aid it in becoming a popular choice for projects, like mobile app, data science, web app, IoT, AI and many others.

Python, Machine Learning, AI: Their Equation

Be it startups, MNCs or government organizations, Python seem to be winning every sector. It provides a wide array of benefits without limiting itself to just one activity – its popularity lies in its ability to combine some of the most complex processes, including machine learning, artificial intelligence, data science and natural language processing.

Deep learning can be explained as a subset of a wider arena of machine learning. From the name itself you can fathom that deep learning is an advanced version of machine learning where intelligence is being harnessed by a machine generating an optimal or sub-optimal solution.

Combining Python and AI

Lesser Coding

AI is mostly about algorithms, while Python is perfect for developers who are into testing. In fact, it supports writing and execution of codes. Hence, when you fuse Python and AI, you drastically reduce the amount of coding, which is great in all respects.

Encompassing Libraries

Python is full of libraries, subject to the on-going project. For an instance, you can use Numpy if you are into scientific computation – for advanced computing, you have put your bet on SciPy – whereas, for machine learning, PyBrain is the best answer.

A Host of Resources

Entirely open source powered by a versatile community, Python provides incredible support to developers who want to learn fast and work faster. The huge community of web developers are active worldwide and willing to offer help at any stage of the development cycle.

Better Flexibility

Python is versatile. It can be used for a variety of purposes, right from OOPs approach to scripting. Also, it performs as a quintessential back-end and successfully links different data structures with one another.

Perfect for Today’s Millennial

Thanks to its flexibility and versatility, Python is widely popular amongst the millennials. You might be surprised to hear that it is fairly easier to find out Python developers than finding out Prolog or LISP programmers, especially in some countries. Encompassing libraries and great community support helps Python become the hottest programming language of the 21st century.

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Some of the most popular Python libraries for AI are:

  • AIMA
  • pyDatalog
  • SimpleAI
  • EasyAI

Want to ace problem-solving skills and accomplish project goals, Machine Learning Using Python is a sure bet. With DexLab Analytics, a recognized Python Training Center in Gurgaon, you can easily learn the fundamentals and advance sections of Python programming language and score goals of success.


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Best Machine Learning Questions to Crack the Toughest Job Interview

Best Machine Learning Questions to Crack the Toughest Job Interview

The robust growth of artificial intelligence has ignited a buzz of activities along the scientific community. Why not? AI has no many dimensions – including Machine Learning. Machine Learning is a dynamic field of IT– where, one gets access to data and learn from that data, resulting into massive breakthroughs in the field of marketing, fraud detection, healthcare, data security, etc.

Day by day, companies are recognizing the potentials of Machine Learning. This is why investment in this notable field is spiking up as much as the demand for skilled professionals. Machine Learning jobs are found topping the list of emerging jobs displayed on LinkedIn – the median salary of a ML professional is $106,225, which pretty much suffices for a well-paying career option.

Importantly, we’ve picked out 5 best interview questions about Machine Learning that’ll optimize your chances of getting hired. Known to all, though ML skill is in high demand, grabbing a job in this booming field of technology is no mean feat. Employers seek particular knowledge and expertise in this field to get you hired. Our 5 best interview questions will help you expand your knowledge base on ML and hone your skills ahead of time.

You can also check out our Machine Learning training course – it comprises of industry-standard course material, real life use cases and encompassing curriculum.

What is Machine Learning?

While you define the exact meaning of the term, make sure you convey your good grip over the nuanced concepts of machine learning, and its real life applications. Put simply, you must show the interviewers how well versed you are in AI and machine learning skills.

What is the difference between deductive and inductive Machine Learning?

Deductive ML begins with a conclusion, and then proceeds towards making deductions about that conclusion. Inductive ML starts from examples and ends with drawing conclusions.

How to choose an algorithm for a particular classification problem?

The answer here is subject to the degree of accuracy and the size of the training set. For a tiny training set, low variance/high bias classifier will work, and vice versa.

Name some methods of reducing dimensionality

Integrate features with feature engineering, eliminating collinear features, or use algorithmic dimensionality reduction – these procedures can definitely reduce dimensionality.

What makes classification and regression differ?

For definite answers, classification is far better a tool. It predicts class or group membership. On the other hand, regression entails prediction of a response.

What does a Kernel SVM mean?

Kernel SVM is the short form of Kernel Support Vector Machine. Kernel methods are basically a specific class of algorithms used for patter analysis and amongst them the most popular one is the Kernel SVM.

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What do you mean by a recommendation system?

Recommendation system is a common feature for those who have worked on Spotify or shopped at Amazon. It’s an information filtering system that forecasts what a user wants to hear or see, structured on the choice patterns given by the user.

No second thoughts, these interview questions will set you on the right track to crack an interview – but, if you want to gain a deeper understanding on Machine Learning or AI, obtain Machine Learning training Gurgaon from the experts at DexLab Analytics.

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How Machine Learning and AI is Influencing Logistics, Supply Chain & Transportation Management

How Machine Learning and AI is Influencing Logistics, Supply Chain & Transportation Management

More than 65% of top transportation professionals agree that logistics and supply chain management is in the midst of a revolution – a period of incremental transformation. And, the most potent drivers of change are none other than machine learning and artificial intelligence.

Top notch companies are already found leveraging the tools of artificial intelligence and machine learning for fine-tuning its superior strategies, including warehouse location scouting and enhancing real-time decision-making. Though these advanced technologies nurture large chunks of data, the logistic industry has for long been hoarding piles of data. Today, the difference lies in the gargantuan volume of data, as well as the existence of powerful algorithms to inspect, evaluate and trigger the process of understanding and its respective action.

Below, we will understand how AI streamlines logistics and transportation functionalities, influencing profitability and client satisfaction. Day by day, more companies are fusing Artificial Intelligence with Internet of Things to administer logistics, inventory and suppliers, backed by a certain amount of precision and acumen. Let’s delve deeper!

Predictive Maintenance

AI-powered Sensors monitor operational conditions of machines; thus can detect discrepancies even before the scheduled machine servicing based on manufacturer’s recommendation. Then they alert the technicians prior to any potential equipment failure or service disorientation. Thanks to real-time wear and tear!

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Shipping Efficiency

Powerful algorithms are constantly used to tackle last minute developments, including picking the best alternate port in case the main port is non-operational or something like that, planning beforehand if the main carrier cancels a booking and even gauging times-of arrival.

Machine Learning capabilities are also put to use for estimating the influence of extreme weather conditions on shipping schedules. Location specific weather forecasts are integral to calculate potential delays in shipments.

Warehouse Management

Machine learning has the ability to determine inventory and dictate patterns. It ascertains the items which are selling and are to be restocked on a priority basis, and items which need sound remarketing strategy.

Voice recognition is a key tool that uses AI to ensure efficiency and accuracy through successful Warehouse Management System – a robotic voice coming out of a headset says which item to pick and from where, enabling a fast process of warehousing and dispatching of goods.

Once, the worker founds the item, he/she reads out the number labeled on them, which the system then tallies with its own processed data list through speech recognition and then confirms the picked item for the next step.  The more the system is put to use, the more trained it gets. Over time, the system learns the workers’ tone and speech patterns, resulting into better efficiency and faster work process.


 A majority of shipping companies are competing with each other to have the most robust and efficient delivery service, because delivery is the final leg of a logistic journey. And it’s vitally important – predictive analytics is used to constantly maneuver driver routes, and plan and re-plan delivery schedules.

DHL invests on semi-autonomous vehicles that drive independently without human intervention carrying deliverables to people across urban communities. Another company, Starship Technologies, founded by the co-founders of Skype employs six-wheeled robots across London packed with hi-tech cameras and GPS. The robots are stuffed with cutting edge technology, but are controlled by humans so that they can take charge as and when required minimizing any negative outcomes.

Overall, artificial intelligence and machine learning has started augmenting human role for efficient logistics and transportation management. With all the recent developments in the technology sphere, it’s only a matter of time until AI becomes a necessary management part of supply chain.

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And of course all this excites us to the core! If you are excited too, then please check out our brand new Machine Learning Using Python training courses. We combine theoretical knowledge merged with practical expertise to ensure students get nothing but the best!

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How VC Firms Are Using Machine Learning to Make Robust Investment Decisions

How VC Firms Are Using Machine Learning to Make Robust Investment Decisions

Venture capital companies find it hard to pool in interesting investment options – the task is laborious and travel-intensive. But, thanks to machine learning and predictive analytics – they have now started to transform the entire procedure of how an investor builds up a portfolio altogether.

Considering the power of AI’s utility in determining the most fabulous startup investments, InReach Ventures co-founder Roberto Bonanzinga has decided to invest $7 million on respective software that deploys machine learning to identify significant European startups to invest capital into. Following its footsteps, several other VC firms have started doing this, already just to thrive in.


Rightfully so, AI is an incredible tool that is capable enough to filter out all the unnecessary noise and pull up VCs with potential candidates for sound investment. This makes it easier for entrepreneurs to hit the optimal level of funding and appeal to strong VCs.

AI: An Investment Ally

According to a Social Science Research Network Study, there lies an inherent risk with investing on newbie entrepreneurs, and just only 18% tastes success on their feats. Brand new business owners are ambiguous, they need some scrutiny before investment – for that, AI framework is armed with the required tools and information – it can internalize data to easily derive at conclusions and fasten a success rate to a company on the basis of past industry performance, revenue growth, profit ratios and market size.

As a result, entrepreneurs can tweak their pitches and alter company profiles to better tally with AI, and this how they can start:

Get Deeper

Who doesn’t dream of owning a company that’s a market leader?! However, raising such adequate amount of capital becomes the real challenge. The challenge intensifies when budding entrepreneurs need to attract funds.

For such minority-fronted startups, Alice, a formidable AI platform uses data to decide which businesses are worth funding. Entrepreneurs should implement AI platforms, like Alice to take a deeper look into the key metrics to get a larger picture how their startups are staking up to their tailing rivals who received funding and how well they are functioning.

Tracking Investor Trends Helps

Age-old methods of tracking investment trends are things from the past, because AI and machine learning is changing the entire ball-game. A Berlin-based VC firm Fly Venture plans to target European startups in the seed stage and pre-Series A startups and finally closed its first fund at $41 million. It aims to use machine learning to generate deal flow. This type of technology helps entrepreneurs meet the right investors at right time. After keeping a close eye on the market, it’s about time to utilize the AI-sought information to make sure your company is line with what investors are seeking in a veritable startup partner. This will bear more fruits and less frustration.

Never stop evolving

The best thing about AI is that it never stops improving. Constantly, machine learning is on the move – it analyzes information 24/7 so that entrepreneurs gain access to non-stop updates to tweak their businesses, while pitching for investors.

In a nutshell, to have better insights and cleaner access to data, entrepreneurs need to harness the relentless power of AI. The technology isn’t eating away our jobs, instead its bringing a new change in the data-inspired environment. And if you are already working with it, you’ll understand how it’s reshaping and guiding venture capital to startups that AI finds worthwhile.

To grasp emerging trends, newer solutions, robust techniques and real-life case studies, take up Machine Learning Using Python courses from DexLab Analytics. Their Machine Learning Training Gurgaon simply gives an out of the world experience, thus need to be tried on.


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Chief Data Officer Is the Next “Commander” To Join the Digital Kingdom and Here’s Why

Chief Data Officer Is the Next “Commander” To Join the Digital Kingdom and Here’s Why

An over-empowering digital transformation is here and it is wreaking havoc in the C-Suite. CDOs have started taking a front line in managing and pushing new tech like AI and machine learning to alter business landscapes forever.

As a matter of fact, this promising job title has existed for years, even decades – mostly in the financial market. But now when data is being generated at record high speeds, the job role of the CDO is emerging out bigger and better. No more a single person or a general crew is enough to tackle such challenging data issues – to fulfill complicated data management tasks, management is now looking up to specialized data experts.

Gartner predicts that 90% of multinational organizations will appoint a CDO by 2019. Though the first generation CDOs were only concerned about data governance and management, of late, they have been shifting focus on how to best implement data as the best strategic asset in organizations to trigger optimum results.


Take a look down to know how CDOs can add value to your organization, while streamlining data and developing strategies:

Be competitive, be ahead of the curve

The best way to ace is by taking over your competitors. In corporate jargon, it means to understand your competitor’s strategies better and arm yourself in the way. Also, it calls up to know your customers better, including the things they like to purchase and know ways you can fulfill their needs. Glean all of these observations with the flattering tool of IoT and machine learning, including social media and supply chain.


Share information through Data silos

Think how would you feel if you are unable to share information within your department? It can be exasperating. But in reality, it happens. Employees working in the same company, even in the same team forget to share information – data is treated as a commodity that is traded for. That’s why, chief data officers break down data silos in an organization to make sure everyone within the framework get access to data to boost decision-making.

CDOs infuse life into data

All analysts are not good with data. No matter how much they pore themselves over into pie charts and bar diagrams, they just can’t nail it. Machine learning using Python and other related technologies has made things easier – now CDOs can infer trends and draw meaningful insights necessary for a better company future. And mind it these analyses eventually saves hours of production time, millions of losses and much more.


There’s nothing better than cleaner, fresh data

Unkempt data is no data at all. In fact, data comes handy only when it is clean. Today, with the influx of so many data, organizations falter to keep pace with so much data extravagance data starts becoming dirty or of little use. This results in – every report run is full of flaws, estimates are wrong and lists compiles are inaccurate. As a savior in troubled situations, CDOs help in churning out crystal clear, consistent data by taking care of all the business processes, and making sure that they are properly maintained by the users.

CDOs are the meat and potatoes of C-Suite team

Not only they understand the intricacies of the subject matter, CDOs undoubtedly makes better use of your data, and looks forward to ways to use them in more meaningful manners. They are not here to hoard the data, but to share it extensively among the people working in the organization to produce fascinating results all around.

Now that you know how important CDOs are, enroll for a reputable business analytics online certification by DexLab Analytics. Business analytics certification is the key to good times, go get one for yourself today!


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How Machine Learning Training Course and AI Made Lives Easier

How Machine Learning Training Course and AI Made Lives Easier

Technological superiority, the rise of the machines and an eventual apocalypse are often highlighted in sci-fi Hollywood movies. The unfavorable impacts of machine learning and excessive dependence on artificial intelligence have always been the hot topic for several Hollywood blockbusters, since years. And people who watch such movies develop a perception that more the technical advancement, higher is the chances that it will ignite a war against humans.

However, in reality, away from the world of Hollywood and motion pictures, Machine Learning and Artificial Intelligence is creating a sensation! If we look past the hype of Hollywood movies, we will understand that the Rise of Machines is certainly not the end of the world or the harbinger of apocalypse but a window of opportunity to achieve technical convenience.

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Though individual are reaping benefits from AI, but it is the business world that is deriving most of its benefits. You will find AI everywhere- from gaming parlors to the humongous amount of data piled in workstation computers. Extensive research is being carried out in this field and scientists and tech gurus are spending huge amount of time in making this improved technology reach the masses. Also, Google and Facebook have placed their high hopes on AI and have also started implementing it in their products and services. Soon, we will see how easily Machine Learning and AI will stream from one product to another.

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Who Are The Best Users of Machine Learning?

Machine learning cannot be implemented by every SaaS. Then who can be the active users of machine learning? As stated by a spokesperson of a reputable AI company, the implementation of Machine Learning is suitable for companies that have massive amounts of historical data stored. To train a puppy, you need a handful of treats, similarly to tackle an algorithm you need a vast amount of human corrected error-free data.

Secondly, to get the taste of success the companies, who are thinking of implementing AI, need a proper business case. You need a proper plan before you start operating. Always question yourself, whether your machine learning algorithm will be able to reduce your costs, while offering better value. If yes, then it is a green signal for you!

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The Math Behind Machine Learning: How it Works

The Math Behind Machine Learning: How it Works

It is evident that in the last few months, we have had several people showcase their enthusiasm about venturing into the world of data science using Machine Learning techniques. They are keen on probing the statistical regularities and building impeccable data-driven products. but we have made an observation that some may actually lack the necessary mathematical knowledge and intuition to get the framework for achieving results with data. And this is why we have decided to discuss this lacking through our blog.

In the recent times, there has been a noticeable upsurge in the availability of several easy-to-use machine and deep learning packages such as Weka, Tensorflow, scikit learn etc. But you must understand that machine learning as a field is one that has both statistical concepts, probabilistic concepts, computer science and algorithmic concepts to arise from learning intuitively from available data and also is about determining the patterns and hidden insights, which can be used to build intelligent applications. While still having the immense possibilities of Machine Learning and Deep Learning which is a thorough mathematical understanding of many of these techniques which is necessary for a good grasp of the internal workings of algorithms to achieve a good result.

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Why we must think about the math?

To explain why it is necessary to behind the scenes into the mathematical details of Machine Learning, we have put own a few important points:

  1. To choose the right algorithm which will include giving considerations, to accuracy, to the right training time, complexity of model, number of parameters and the number of features.
  2. To choose parameter settings and to validate the strategies
  3. To indentify the under-fitting and over-fitting by understanding the bias-variance trade off.
  4. For acquiring ample confidence about the interval and uncertainty

 The level of math one will need:

The primary question when one tries to understand an interdisciplinary field such as Machine Learning, is the amount of math needed and the level of math needed to understand these techniques.

The answer to this question is not as simple as it may seem and is multidimensional which, depends upon the level and interest of the individual. Research conducted in these mathematical formulations and theoretical advancements for Machine Learning is an ongoing process and a few researchers are already working on few more advanced techniques. However, we will state the least amount of math that is a must have skill for being a successful Machine learning Engineer/ Scientist is the importance of each and every mathematical concept.

Linear algebra:

This is the math skill to have for the 21st century. One must be well-versed with the topics of Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigendecomposition of a matrix, LU Decomposition, QR Decomposition/Factorization, Symmetric Matrices, Orthogonalization & Orthonormalization, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces as these norms are absolutely necessary for the understanding and the optimization methods for machine learning. The best thing about linear algebra is that there are a lot of online resources.

Probability theory and statistics:

Machine learning and statistics are not too different a field. And in reality some people have actually defined Machine Learning as “doing statistics on a Mac”. A few fundamentals that are a must have for machine learning are – Combinatorics, Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions, Maximum Likelihood Estimation (MLE), Prior and Posterior, Maximum a Posteriori Estimation (MAP) and Sampling Methods.

Multivariate calculus:

Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution are some of the necessary topics necessary for understanding ML.

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Algorithms and Complex Optimizations:

In order to realize the computational efficiency and scalability of our Machine Learning Algorithm and for exploiting the sparsity in the dataset, this concept is necessary. One must have knowledge of data structures such as Binary Trees, Hashing, Heap, Stack etc, and Dynamic Programming, Randomized & Sublinear Algorithm, Graphs, Gradient/Stochastic Descents and Primal-Dual methods.

A few other mathematical skills that are often necessary for understanding ML are the following Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits), Information Theory (Entropy, Information Gain), Function Spaces and Manifolds.

Machine learning training in Gurgaon from experts with in-depth instruction on math skills is offered at DexLab Analytics. Check out our Machine learning certification brochure for the same at the website. 



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