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Tag: big data Hadoop training courses

What’s so trendy about machine learning? Why’s everyone crazy about it?

Posted on by Imarticus Learning

Machine learning has become quite the trend, you must be noticing a lot of people opting for this particular course. So today we will tell you what the fuss is all about. To put it in simple terms, machine learning is basically learning from data. It involves tweaking of parameters and adjusting data, to get the best possible inference. It takes a little bit of practice to master machine learning, but it is not rocket science, you will get there sooner all later, just make data and algorithms your very best friends.
What is machine learning?
To start off, machine learning is all about feeding data into a generic algorithm and help it build its own logic, based on the data fed to it. This way, you don’t have to write codes. The subject can be divided into two main categories; supervised learning and unsupervised learning.
If you are tired of nodding at conversations about machine learning without understanding a thing, it is time you change that by getting hold of a machine learning courses. Believe it or not, it is an amazing skill to have, which will hold a very strong place in your resume or C.V. In fact, in today’s tech-savvy era, not knowing about machine learning is going to have a negative impact on your job. If you have no idea about what is machine learning then be a sport and start from scratch, there is plenty of study material available online and offline. Try to go through the theories, understand the basics and when you are ready, do opt for a machine learning certification course.
What is the hype all about?
Truth be told, the hype around machine learning is not going to fizzle out any time soon. It is a very important subject in a number of domains, as the subject has yielded some amazing results and there you can expect even better things in the future. At its core, the subject is really simple, and it involves lots and lots of data. It is very important to have access to as much data as you can possibly derive, and having documentation of the same. The progress made in the field of machine learning within the past decade has been absolutely phenomenal. This is a brand of artificial intelligence which is heavily based on data. The algorithms, as well as the data, helps the model to make accurate decisions, with the least human intervention.
Machine learning is one subject with the help of which we can easily, also very quickly analyze and understand, complex, big data and yield accurate results from it. This can be done on a very large scale, which increases the chances of identifying profitable opportunities.
The trend of machine learning
If machine learning facts and trends are anything to go by, then some major breakthroughs are on their way. Organizations can make better decisions without relying on human intervention. By using an algorithm to build models with the help of machine learning. Any industry working with a large amount of data, can make the most of progress and work more efficiently to gain an edge over their competitors. Many people are buying the machine learning trends and are more than willing to imbibe it in their organization whilst making the best use of it.
Why is everyone going gaga over machine learning?
With a  machine learning certification, you can make yourself useful in the following fields:

  • Financial services: Banks make use of machine learning to understand investment opportunities, trading trends and identify the clients with high-risk profiles. In fact, acts of fraudulence can be pinpointed with the help of machine learning surveillance. With such cut-throat completion in the finance sector, having a machine learning certification will most certainly prove to be an asset.
  • Transportation: Transportation is one field, where analyzing data helps in making some of the key decisions. The data analysis of machines learning can help both public and private sector transportation in many different ways.
  • Healthcare: All thanks to sensors and wearable devices which can assess a patient’s health, a lot of data can be gathered. With the use of machine learning, medical experts will be able to look at the various health trends, point out hazards and even stop epidemics from spreading. This will lead to better diagnosis, treatments, and prevention as well.
  • Government: The government deals with various different kinds of data, especially in areas such as public safety and utilities. Machine learning can really help in analyzing different kinds of data and find solutions to the impending problems with regards to the civilians. It can also minimize identity theft, online frauds and much more.
  • Marketing and sale: If you wish to build your career in this field, then you must opt for a machine learning certification course. Capturing data and analyzing upcoming marketing trends, alongside planning new campaigns based on them will become easy.

A course in machine learning opens many vistas of opportunities for candidates in the various fields. It is perhaps because of this reason, people are growing crazy about this particular area of computer studies. It is not the most difficult to master and people with the non-technical background can get a hang of it as well. The bottom line is, machine learning trends are on the high, so you might as well, think of opting for a course and strengthen your position in your organization, as it is a very important skill set in today’s times.

What is Data Wrangling and Why is it Important?

Posted on by Imarticus Learning

Data has changed the digital landscape drastically in the past few decades. From analyzing and providing insights real-time to enhance one’s life, data is integral to everything we do. 

It is impossible today to live in a world where we do not encounter data. Whether it is watching recipes on YouTube to adding friends on social networking sites, data is everywhere. Due to the abundance of data, there is also an abundance of knowledge and insights which we never had before.

However, if the data is outdated or irrelevant, it serves no purpose.  This means that there is a real need today for data wrangling. Data wrangling is the art of providing the right information to business analysts to make the right decision on time. It aids organisations by sorting through data and access them for further processing and analytics.

Apart from this data wrangling also involves removing unnecessary data, organising them in a consumable fashion.
Data wrangling also provides organisations with the right information in a short span of time to access the right information thereby helping make strategic decisions for the business. It also helps business perform all these tasks at a reduced cost and more efficiently with minimal human intervention.

Here are the top reasons why data wrangling should be everyone’s priority

Credibility of data
When large amounts of data are processed for interpretation chances are all of it is not relevant or outdated. Although data wrangling is a tedious process, conducting it will ensure that the data secured is not outdated or irrelevant.  Therefore, data wrangling provides credibility to data analytics courses. It picks the right data required in order to provide the necessary solutions to a problem. 

Build trust amongst stakeholders
When valuable information is extracted and presented to the stakeholders involved it build trust. Data should not only be presented in a simple format, but it also must add value to the circumstances. This means that any data that is extracted must be able to benefit the organisation or individual one way or another. This can be achieved through data wrangling, making it an important activity to carry out in an organisation.

Aid Machine Learning
Machines of today have the ability to create, process and understand data to arrive at plausible solutions thereby aiding a company’s overall growth and success. In order to optimise the vast volumes of data obtained from various sources, data wrangling becomes an important task.

It is not possible for a machine to scale and learn from new information if the data itself is corrupt or unnecessary.  Data which is historic in nature which allows the machine to learn and adapt can only be procured through data wrangling. If the quality of data that is fed into an AI is useless, the results which it will produce will also be irrelevant.

Conclusion
Data wrangling is extremely relevant today due to the large amounts of data that gets proceeded every day.  We will not be able to do thorough analytics if we do not have a strong infrastructure of data storage and hence companies are investing heavily in data wrangling tools.

Popular Tools to Analyze Data

Posted on by Imarticus Learning

Big Data is now an inevitable part of how many companies operate. While we all leave our footprint on the internet, companies ranging from IT to manufacturing firms are reaping the benefits of data analytics.

Knowing how to extract the information and trends, you require from the vast pool of data is imperative. Data analytics courses lets companies leverage this information for creating new plans, products, trends, offers, and more.

There are many tools that can be used effectively for analyzing data. Each of these tools has their own benefits and strengths. Once you are familiar with the capabilities of these tools, you will be able to employ the right tool for the right analysis. Tools for data analysis can be categorized into three main types.

  • Open Source Tools

KNIME

KNIME Analytics Platform is one of the most popular choices available to data scientists. It lets you model and manipulates data with more than 1000 modules, ready-to-run examples, a comprehensive set of integrated tools, and a large collection of advanced algorithms.

RapidMine

This tool is similar to KNIME in that it is a visual program. This tool has a unified environment making it easy to run through the entire gamut of the analytical workflow. You can use this tool for everything from data prep to machine learning to model validation to deployment.

  • Tools for Data Visualizations

Datawrapper

This is an effective tool used by news rooms around the world to create easy understand graphics and interactive charts. During elections, for example, newsrooms will plug in data collected by various resources and journalists on the ground to create charts that the layman can use.

The data can be populated according to race, ethnicity, age, gender, qualification, and more in order to understand the trend of the elections. Politicians in turn can use the same data to understand where they have popularity and with whom their ideologies resonate.

Google Fusion Tables

This is an amped up version of Spreadsheets backed by the powerful mapping tools of Google. You can use preexisting tables and combine two or more tables to create a visualization for both sets of data. You can choose to map, graph, chart the data which can then be shared or embedded into any page. This tool is great for collaboration as all the data organisation is saved on Google Drive.

  • Sentiment Tools

SAS Sentiment Analysis

Going back to the elections example, sentiment techniques can be used to assess sentiments in real time. The SAS tool extracts and interprets sentiments in real time or over a time period that you can specify. The tool features natural language processing and statistical modelling. The language processing is rule-based, and so you can choose the specific trend or emerging topic. This tool can be used to find the current feeling a population has towards a particular electoral candidate. This can be further developed to reflect the sentiments based on age, employment, gender, and sexual orientation.

Opinion Crawl

This is a great data analytics tool for all data scientists. It allows you to get sentiment analysis based on topic. This could be a person, a real-time event, a company, a product, or more. This tool provides the data in the form of a pie chart representing the real-time sentiment of the topic along with related images, a few headlines, and, most importantly, key semantic concepts related to the topic according to the public.


What makes Hadoop so Powerful and how to Learn it?

Posted on by Imarticus Learning

Why Hadoop?

With today’s powerful hardware, distribution capabilities, visualization tools, containerization concepts, cloud storage and computing capabilities, huge amounts of raw data can be stored, processed, analyzed, and converted into information, used for decision making, historical analysis and for future trend prediction.

Understanding Big data and converting into knowledge is the most powerful thing any entity can possess today. To achieve this, Hadoop is currently the most used data management platform. The main benefits of Hadoop are:

  1. Highly scalable
  2. Cost-effective
  3. Fault-tolerant
  4. Easy to process
  5. Open Source
  1. What is Hadoop?

Hadoop is a Highly distributed file system (HDFS), maintained by Apache Software Foundation. It is a software to store raw data, process it by leveraging the distributed computing capability and to manipulate and filter it for further analysis.

Several frameworks and machine learning libraries like python and Operate on the processed data to analyze and make predictions out of it. It is a horizontally scalable, largely distributed, clustered, highly available, and reliable framework to store and process unstructured data.

Hadoop consists of the file storage system (HDFS), a parallel batch processing engine Map Reduce and a resource management layer, YARN as standalone components. Open source software like Pig, Flume, Drill, Storm,Spark, Tez, Hive, Kafka, HBase, Mahoot, Zepplin etc. can be integrated on top of the Hadoop ecosystem to achieve the intended purpose.

How to Learn Hadoop?

With interest in Big Data growing day by day, learning it can help propel your career in development. There are several Big data Hadoop training courses and resources available online which can be used to master Hadoop theoretically.

However, mastery requires years of experience, practice, availability of large hardware resources and exposure to differently dimension ed software projects. Below area few ways to speed up learning Big Data.

  1. Join a course: There are several Big Data and Hadoop training courses available from a developer, architect, and administrator perspective. Hadoop customization like MapR, Horton Works, Cloud era etc. offer their own certifications.
  2. Learning marketplaces: Virtual classrooms and courses are available in Course Era, Udemy, Audacity etc. They are created by the best minds in the Big Data profession and are available at a nominal price.
  3. Start your own POC: Start practice with a single node cluster on a downloaded VM. Example: Cloud Era.com quick start.
  4. Books and Tutorials on the Hadoop ecosystem: Hadoop.apache.org, Data Science for Business, edurekha,digital vidya, are a few examples apart from the gazillion online tutorials and videos.
  5. Join the community: Joining the big data community, taking part in discussions and contributing back is a surefire way to increase your expertise in big data.

Points to remember why Learning Hadoop:

Below are the things to keep in mind while working on large open source Big Data projects like Hadoop:

  1. It can be overwhelming and frustrating: There will always be someone wiser and more adept than you are.Compete only with yourself.
  2. Software changes: The ecosystem keeps shifting to keep up with new technology and market needs. Keeping abreast is a continuous process.
  3. Always Optimize: Keep finding ways to increase the performance, maturity, reliability, scalability, and usability of your product. Try making it domain agnostic.
  4. Have Fun: Enjoy what you are doing, and the rest will come automatically!

All the Best on your foray into the digital jungle!

How Companies Use Machine Learning

Posted on by Imarticus Learning

How Companies Use Machine Learning

Machine Learning and data processing has changed drastically the way things work inenterprises and even our daily lives. Digital technology has been able to enablemachines with ML software and algorithms to process intelligently and unsupervised the large volumes of data generated. The advent of the internet and such limitless uninterrupted data processing has generated many an error-free gainful insight.

Businesses can now transform to the high-efficiency mode where profits increase by creative use of employee time in using the insights and forecasts provided by machine learning, data analytics, big data processing, and accurate predictive analysis.

What are companies using ML in?

Learning and Scanning data images, text and voice: Repetitive tasks and tasks that are labour-intensive are now a one-step zero-error machine process. Digitizing data has scored in the following areas.

  • Data entry, documentation and report generation: The way data is processed, the volumes of data available, used and predictive analysis of data analytics have impacted lives and businesses to upgrade and upskill for better efficiency and profits.
  • Image Interpretations: Complex insights are possible with accurate predictive insights which have huge ramifications in the film, media, health, banking, insurance sectors and more.
  • Previewing videos: Data previewing in video form can help to process in speeds far higher than humans could ever think of. They can also match the videos to preferences of people, match advertisements to these, edit and curate video footage in fractions of a second! The advertising, marketing, media, film, and video industry has been transformed forever. The revenues generated with accompanied efficiency and speed has led to collaborations of machines and humans in a positive manner.

Uncovering and forecasting insights: ML has truly transformed the way we function with computers and ML replacing routine, repetitive tasks. Notably, the following sectors have improved tremendously.

Monitoring Markets: Mining of big data can result in time-saving and provides lead time in relevant and urgent monitoring of opportunities. News channels, competing in the business world, taking corrective actions and strategising have become a matter of nanoseconds with ML.

  • Root cause analysis: This technique used in production lines can predict and forecast failures of tasks, identify the root cause of the issues, suggest strategy changes required and generate alerts in these conditions.
  • Predictive maintenance: This tool is most effective in its forecasting abilities and ensures there will be no downtime in functioning.
  •  
  • Predictive modelling: ML has enabled matching customer profiles and preferences to products available and browsing history-making auto-suggestions a routine affair. The huge potential of generating through advertisements matched to such preferences can generate more efficiency and high revenues.

With the advent and use of ML in everything you do, there is an urgent need for collaborators who can tweak software, create new applications, use the predictive and forecasting alerts and insights gainfully to improve profits, efficiency and save time, effort and costs. It is still early days and the right time to upgrade and re-skill with machine learning courses that will enable smart and creative use of machine learning benefits mentioned above.

Big data Hadoop training courses are also required to help ML understand and use the mind-boggling quantities of data that is now usable. Without the will to effectively use data and the training needed to adapt you will be left far behind. The situation today is adapt, or stay behind! 

What Are The Advantages Of Business Competition With Deep Learning?

Posted on by Imarticus Learning

We all produce data. Enterprises have their own data. But as the adage goes, the wise learn from the past. Today, machines, robots, and software are smart. Machine Learning has in the past decade transformed software to help machines learn unsupervised from data. Deep Learning is the subset which helps ML learn from data that is unstructured. Humans are limited by data.
ML process huge quantities of data and learns patterns and can thus give you recommendations on Facebook based on your browsing history and use or suggest interesting videos on YouTube on your smartphone. It is now time to use ML intelligently in your business enterprise or career and stay abreast with the latest upgrades or be left behind.

Advantages in Business:

Primarily three benefits accrue with Deep Learning.

  1. Time and Cost benefits: Most employees do the same repetitive job day in and day out. Neural networks have given artificial intelligence the brains to use data, learn both supervised and unsupervised from it and use it to perform such repetitive tasks. In terms of time saved the employees are now free to use their time on creative tasks. Money in hiring more employees to handle large data generated is saved. ML never sleeps or takes a holiday. With the potential to offer so much saving of time and money it is well worth the investment of ML.
  2. Quality scores with accurate results: Human emotions bias our results and output. ML, on the other hand, is error-free learning with no emotional bias. In processing data and for repeating tasks in a production line such errors can be costly. ML also needs no food, sleep or breaks. With highly accurate results that can be preset and traverses data with multi-variables and time constraints cutting across all departments and sources of data ML has the ability to improve quality and efficiency. The obvious outcomes are better organisations, speedy deliveries, accurate results, and high efficiency.
  3. Growth in jobs: ML needs humans to program them and to use the insights provided by them in newer applications. This definitely means more humans are required with an understanding of ML. But such human intervention and supervision need an in-depth knowledge of ML, data analytics, deep learning, and artificial intelligence.

    What Should You Do About It?

    If you are an employer, then it is time your employees were re-trained and learn how to use ML to the advantage of the enterprise creatively. Encourage employees to upgrade and up-skill with machine learning courses. This will offer them better prospects and pay packets because of increased efficiency.

    Why Do Machine Learning Courses In India?

    India today is an emerging hub of innovation with huge potential in terms of trained manpower and resources in training, expertise in software programming and high demand for good quality workers and software knowledge. Large enterprises need smaller businesses to get their tasks done and this, in turn, means job generation. If you have the right knowledge and the skill to use it your career will know no bounds. That’s why should know what is so trendy about Machine Learning courses is a step in the right direction for you!
    Growing your employees means business growth which becomes super-efficient and organised, offering better returns and results. Make your move today. It is a win-win situation for both the workers and the business.

Household Electricity Consumption – Machine Learning Algorithm

Posted on by Imarticus Learning

Power supply, generation, and its billing generate a huge amount of data. ML actually makes it possible to learn from this data and use an algorithm to accurately predict future occurrences like volumes of load and its demand, snag identification, efficiency and power loss reduction, problems and logistics involved in metering and billing and everything in between from power generation to its billing and beyond.
Machine learning courses in India could teach you how to understand ML and data analytics, so you aid ML to perform at its best in predicting outcomes. The Algorithm in ML for household electricity consumption works on data drawn from smart meters, solar panels, and data regarding the usage of electricity at different times of the day.
This huge data comprises the multi-variable time-series, and the algorithm can successfully predict future consumption. In real terms, the ML algorithm can predict such information as to help make the power generation and supply system more efficient.
Obviously, there are many steps involved in helping the machine take data in its raw multivariate form and enabling it to arrive at the future consumption prediction. This is where Machine learning courses come in handy. You can learn the techniques of ML involving predictive strategies like the direct methods and the recursive ones.
A good idea is to also incorporate learning of Big Data Hadoop training courses that can help one understand strategies, working of ML and data analytics. The logic of the process of algorithm development would be developing

  • The framework development for evaluation of non- linear, linear, and ML ensemble algorithms.
  • Evaluation of ML as it uses the strategy of forecasting the time-series both by the direct daily method and the recursive method.

Again such processes involve

  1. Describing the problem.
  2. Preparing and loading the data set.
  3. Evaluating the model.
  4. Recursive forecasting.
  5. Multi-Step direct forecasting.

Through highly accurate predictions ML helps the algorithm to plan future power generation, reduce transmission losses, tweak the metering, billing and collection systems and so much more. Once you master such algorithms, ML and data analytics, the scope of applying ML to various and everyday issues on a real-time basis, open the wide world of opportunity and good remuneration to you.
Yes, ML and data analytics use Python framework which has immense scope for progress basically because it can predict the outcomes of simple and complex tasks, single and multi-variate tasks, and even makes single and complex predictions by learning from the data, filling in the missing values, creating new values and so on. And to learn an ML course is essential. Start today and soon you will be able to master such tasks quite easily.

Reference:
https://machinelearningmastery.com/multi-step-time-series-forecasting-with-machine-learning-models-for-household-electricity-consumption/

Developing ML Models in Multivariate, Multi-Step Forecasting of Air Pollution Time-Series

Posted on by Imarticus Learning

 

Machine Learning Courses in India

The ML algorithms can be applied forecast weather and air pollution for the subsequent 3-days. This is challenging because of the need to accurately predict across multivariate input with noisy dependencies that are complex and multi-step, multi-time input data while forecasting and performing the same prediction across many sites.
‘Air Quality Prediction’ or the Global Hackathon EMC dataset provides weather conditions across various sites and needs accurate predictions of measurement of air-quality to provide a 3-day weather forecast.

The Need for Machine Learning

The primary benefits of Machine learning courses are that with them you can learn to operate the tools from a Python open source library and gain expertise in

  • Providing for missing values, transforming the time-series data and successfully create models that are worked by the trained and supervised-learning algorithms.
  • Evaluate and develop both linear and nonlinear algorithms to handle the multivariate, multi-step multi-time series forecast.

The Need for Data Analytics

A real-time problem when working with this dataset is that of missing values and multiple variables drawn from many physical sites. This means integrating and helping the ML algorithm predict and forecast accurately. You will need data analytical skills to achieve this.
The Big Data Hadoop training courses can provide you with skills and learning in

  • Imputing values that are missing, helping algorithms with supervised learning by transforming the input data time-series and creating requisite number of models using the data and the algorithm.
  • How to evaluate and develop suites of nonlinear and linear algorithms for multiple-stepped forecasting of a time series.

The Entire Process

Developing this algorithm and making it successfully predict with accuracy the weather forecast over the next 72 hours in an environment that has multiple variables, multiple data sets, some missing data, lots of ways to develop the code on the Python platform has nine parts.
Namely,

  • Description of the problem.
  • Evaluation of models.
  • ML Model creation.
  • Data preparation using ML.
  • Creating a Test Harness for model evaluation.
  • Linear Algorithms evaluation.
  • Nonlinear Algorithms evaluation.
  • Lag Size tuning.

Benefits of ML, in this case, are handling features that are irrelevant, the ability to support between variables noise and noisy features, and the ability to support inter-variable relationships. ML forecasting provides both recursive and direct forecasts.
Benefits of data analytics relevant here are in preparing data, feature engineering, lag-tuning the meteorological variables, creating models across many sites, and tuning the algorithm itself.
Enrol in the most suitable course that will help you learn how to develop the algorithm for air pollution forecasting.
Reference:
https://machinelearningmastery.com/how-to-develop-machine-learning-models-for-multivariate-multi-step-air-pollution-time-series-forecasting/

Build Your Own AI Applications in a Neural Network

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Today Big Data, Deep Learning, and Data Analytics are widely applied to build neural networks in almost all data-intensive industries. Machine learning courses in India offers such learning as short-term courses, MOOCs, online classrooms, regular classrooms, and even one-on-one courses. Choices are aplenty with materials, tutorials and options for training being readily available thanks to high-speed data and visualization made possible by the internet.
The study on jobs in Data Sciences says that core skills in Python are preferred by recruiters and is requisite for jobs in data analytics. The challenge lies in formulating a plan to study Python and the need of a specialist to help understand the technical and practical aspects of this cutting edge technology.

Why do a Specialization Course for Beginners?

Not all are blessed with being able to learn, update knowledge and be practically adept with the Python platform. It requires a comprehensive knowledge of machine learning, understanding of data handling, visualization techniques, AI deep learning, statistical modelling and being able to use your expertise on real-time practical examples of data sets from various industries.
Machine learning courses and case studies on Python platform are conducted in flexible learn-at-your-own-pace sessions in modes like instructor-led classroom sessions at select locations, virtual online classes led by certified trainers or even video sessions with mentoring at pre-determined convenient times.
One can do separate modules or certificate Big data Hadoop training courses with Python to understand data science analytics and then opt for modules using AI for deep learning with Python or opt for a dual specialization by doing the beginners course and courses covering AI and Deep Learning with Python. The areas of Deep Learning and AI both require prior knowledge of Deep Learning, Machine Learning, and data analytics with Python.
An example of one such course is the AnalytixLabs starter classes in Gurugram and Bangalore as a speedy boot-camp followed by a package of two courses in AI Deep Learning with Python and the Data Science with Python. The prerequisites are knowledge of at least one OOPs language and familiarity with Python. Their 36 classes, 250-hour course offers dual specialisations, and 110 hours of live training using multiple libraries in Python.
Just ensure you choose the right course to allow your career prospects to advance and allows further learning in Python-associated specialised subjects.

Facts on Machine Learning and Statistics

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All machine learning courses in India need proficiency in statistics. However ML is not only statistics but definitely draws inspiration from analysis of statistics. This is so because data is their common factor. An ML-engineer though must and should have proficiency in statistics, while an ML-expert needs to only have sufficient knowledge of basic statistical techniques and data management. Let’s look into why this is so.

Overlaps of Machine Learning and Statistics

Machine learning courses of today borrow concepts like data analysis and statistical modelling to arrive at predictive models for ML. Machine Learning is a branch of computer science while statistics deals with the analysis of statistics in pure mathematics. However, they are interdependent mathematical applications both dealing with the analysis of data, data models, and problem-solving.
It goes without saying that statistics is the older sibling and yet today even statisticians use ML to achieve its end results with Big Data and for Predictive Analysis. Similarly, ML draws on statistical analysis though its aim is entirely different. That’s why Big Data Hadoop training courses also need knowledge of statistics and database management.
Mostly the overlap and confusion occur because both use algorithms and data to predict the end results. However, it is incorrect to equate the two, which are separate advanced fields, in two different branches. They are at best complementary interdependent fields which can aid each other much like siblings often do. Two separate individuals, completely different, in one environment, and with individual destinations. Sure they walk the same path at times!

Clearing the Confusion

Statistics uses a model with defined parameters fitting the data tested through classification and regression techniques to account for clustering and density estimation, to provide the best inference. ML works with networks, graphs and bar charts learning from general data through assigned weights using unsupervised learning techniques to give an accurate prediction of outcomes.
Looking very closely into the two one will notice that ML has no set rules, equations, parameters, variables or assumptions. It learns from the data input and provides a predictive outcome. In statistics, you get an inference unique to a small data set with fixed variables and based on strict regression and classification techniques of mathematical equations. Though older, statistics is pure math. ML is a carefree youngster, which uses and learns from past data, has no limit to data used or variables present and works with algorithms that govern data to give an accurate predictive outcome.
An ML Engineer and Statistician may have areas where their jobs overlap. They share a common path through the use of modelling and data and then branch out to their own destinations. Truly they are complementary in nature bring out the best in the other and helping each other achieve individual end results.