Machine Learning Jobs For Freshers

Machine Learning Jobs For Freshers

Machine Learning Fresher Jobs :

Computer Science Fundamentals and Programming:


Having a Computer Science background is very important to have a rewarding career in Machine Learning. Engineers looking for Machine Learning Jobs in India should have in-depth knowledge of data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.), and computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.). One must be able to apply, implement, adapt or address them when programming.

Machine Learning Algorithms and Libraries:


Candidates looking forward to Machine Learning jobs in India should be well acquainted with standard implementations of Machine Learning algorithms, most of which are widely available through libraries/packages/APIs .one should also be aware of the relative advantages and disadvantages of different approaches.



Probability and Statistics:


If you are looking for a career in Machine Learning, you should possess strong knowledge of formal characterization of probability and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.). Also, knowledge of analysis methods (ANOVA, hypothesis testing) is necessary for building and validating models from observed data.

Software Engineering and System Design:


Having a strong base in software engineering and system design is required for a promising career in Machine Learning and data science. It also adds on to your Machine Learning skills. You should be able to build appropriate interfaces for your component. Having a good knowledge of Software engineering best practices (including requirements analysis, system design, modularity, version control, testing, documentation, etc.) are invaluable for productivity, collaboration, quality, and maintainability.


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Machine Learning Job Roles :


Machine Learning jobs in India and abroad include Machine Learning Engineer, Data Architect, Data Scientist, Data Mining Specialists, Cloud Architects, and Cyber Security Analysts, and many more. Let us take a sneak peek into some of the ML job roles in demand.  Machine Learning Jobs for freshers may include the job of a data analyst or data scientist.

Machine Learning Engineer:


A Machine Learning Engineer creates amazing algorithms to help decipher meaningful patterns from humongous amounts of data. ML Engineers should focus on Python, Java, Scala, C++, and JavaScript. He must be capable of building highly-scalable distributed systems and work in teams that focus on personalization. Machine Learning engineers have to design and implement Machine Learning applications/algorithms such as clustering, anomaly detection, classification, or prediction to address business challenges.

Data Analytics Course by Digital Vidya

Data Engineer/Data Architect:


Data Engineers are responsible for the organization’s big data ecosystem. With a strong foundation in programming, they must be familiar with Hadoop, MapReduce, Hive, MySQL, Cassandra, MongoDB, NoSQL, SQL, Data streaming, and programming. In addition, they must also be proficient in R, Python, Ruby, C++, Perl, Java, SAS, SPSS, and Matlab. Data infrastructure engineers develop, construct, test, and maintain highly scalable data management systems. Data Engineers also develop custom analytics applications and software components. Data engineers collect and store data, do real-time or batch processing, and serve it for analysis to data scientists via an API.

Data Scientist:


One of the most in-demand professionals today, Data Scientists are experts in R, SAS, Python, SQL, MatLab, Hive, Pig, and Spark. They are proficient in Big Data technologies and analytical tools. They use coding to sift through large amounts of unstructured data to derive insights and help design future strategies. Data scientists clean, manage and structure big data from disparate sources.

Data Analyst:


Most organizations expect Data Analysts to be familiar with data retrieval and storing systems, data visualization and data warehousing using ETL tools, Hadoop-based analytics, and business intelligence concepts. These persistent and passionate data miners usually have a strong background in math, statistics, Machine Learning, and programming. Core responsibilities of a Data Analyst include designing and deploying algorithms, culling information and recognizing risk, extrapolating data using advanced computer modeling, triaging code problems, and pruning data.

Machine Learning Jobs Salary :


The Indian Data Analytics Industry, with its current worth of $2 billion is expected to witness a whopping eight-fold growth and to be worth $16 billion by 2025, according to NASSCOM. Furthermore, the study indicates that the median salary of analytics professionals is growing year on year. The average salary of Data Science professionals across all skill sets and experience levels was ₹ 12.7L in 2017, an 8% increase since 2016, on a much larger base of professionals. The salary for Machine Learning Jobs for Freshers may start at ₹ 8L and may go up to ₹10-15 L.

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Machine Learning Jobs_salary :


According to the latest industry estimates, an AI professional with an experience of 2-4 years can earn annually ₹15-₹20 lakh ($20.6-27.5K). Experienced professionals with 4-8 years can expect up to ₹20-₹50 lakh ($20.6-68.8K). The salaries may vary from company to company. Top notch companies like Amazon India, Google, and Flipkart are ready something between 8-12 L per annum.  Most AI and Machine Learning are concentrated in IT, FinTech and e-commerce, as they rely on analytics, business intelligence and cloud for industrial automation.

Machine Learning Jobs Salary in India :

      
According to Ravi Kaklasaria, CEO& Co-Founder, SpringPeople, a corporate training and certification provider, India has over 50,000 open data analytics jobs on the market and expects the numbers to grow up to 100,000 in 2018. BN Thammaiah, Managing Director, Kelly Services India expects a 60% increase in demand for AI and Machine Learning specialists in 2018. The increase in the demand for AI and ML specialists will be from 20,000 to 32,000 in 2018 even as the IT sector will continue to hire for newer skills, he added further.

As per Analytics India Magazine ‘Salary Study – 2018 that explores a range of current and emerging compensation trends in the Analytics & Data Science space across India, observes that nearly 40% analytics professionals earn an annual salary upwards of ₹10L. In 2018, 37.6% of Analytics professionals in India received a salary of fewer than 6 Lakhs, compared to 39% in 2017 (39%) and 42% in 2016.

Mid-level analytics professionals are now progressing towards a higher salary bracket of ₹ 15 to 25L by having proper machine learning requirements. While the number of Analytics professionals commanding salaries less than ₹ 10 Lakhs has gone down; the number of Analytics professionals earning more than ₹ 15L has increased from 17% in 2016 to 21% in 2017 to 22.3% in 2018.

Mumbai leads in terms of highest compensation packages for Machine Learning jobs in India, at almost 13.3L per annum. Machine Learning jobs in Bangalore follow close quarters, with 12.5L and NCR at 11.8L. Data professionals looking for Machine Learning jobs in Pune can expect something around ₹ 10.6L. Salary packages for Machine Learning jobs in Hyderabad are comparatively, at ₹ 10.2L per annum.

The Future of Machine Learning :


The future of Machine Learning looks promising. There is an urgent need for professionals who are trained in Deep Learning and AI jobs and matches machine learning requirements. If you want to be one of those professionals, prepare yourself by getting certified and industry-ready because the sooner you get your training started, the sooner you will be working in this exciting and rapidly changing field.

You might be a programmer, a mathematics graduate, or simply a bachelor of Computer Applications. Students with a master’s degree in Economics or Social Science can also be an ML professional. Take up a Data Science or Data Analytics course, to learn Data Science and Machine Learning skills and not only prepare you for the Machine Learning job, but also gives a proper overview of all the machine learning requirements.

data-analytics-certification-course :


You may also enroll in Machine Learning Course for more lucrative career options in Data Science.  Industry-relevant curriculums, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya.

For a rewarding career in Machine Learning, one must stay up to date with any up and coming changes in the machine learning requirements. This also means staying abreast of the latest developments for tools (changelog, conferences, etc.), theory and algorithms (research papers, blogs, conference videos, etc.).

Online communities are great places to know about these changes. Also, read a lot: read articles on Google Map-Reduce, Google File System, Google Big Table, and The Unreasonable Effectiveness of Data. You will get plenty of free Machine Learning books online. Practice problems, coding competitions, and hackathons are a great way to hone your machine learning skills

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We Should Know About Types Of Machine Learning

We Should Know About Types Of Machine Learning


types of machine learning

In This Article we will discuss more about Types Of Machine Learning.

Types Of Machine Learning :



As we know there is three types of Machine Learning.

  1. Supervised
  2. Unsupervised
  3. Reinforcement

(1) Supervised :

- In Supervised Learning machine learns under guidance as a teacher guides the student.

- Machines learns from provided data to them and explicitly telling them this is the input and this is how the output must look.

- Here, the system is trained using past data(which includes input and output), and is able to take decisions or make predictions, when new data is encountered.

- In example of teacher and student teacher is training data and student is machine.



(2) Unsupervised :

- The system is able to recognize patterns, similarities and anomalies, taking into consideration only the input data.

- Unsupervised means without any supervision or without anybody's direction.

- Here the data is not labeled and there is no guide.

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- Machine has to figured out data set given and find out hidden patterns.

- In short machine has to make predictions.

- We can say Unsupervised learning is our daily activities on which we take decision own self.


(3) Reinforcement :

- Decisions are made by the system on the basis of the reward/ punishment it received for the last action it performed

- Reinforcement Learning means Machine takes decision once and gives output.if it's right then OK.

- Otherwise it gives feedback after that it data Re-train again.

- This process occurs till the output not comes right.

- so, basically this type learn from feedback and past experiences.


In short terms :


1) Supervised :  
  • Labeled Data
  • Direct Feedback
  • Predict Outcome / Future


2) Unsupervised :


  • No Labels
  • No Feedback
  • Find Hidden Structure In Data





3) Reinforcement :

  • Decision Process
  • Reward System
  • Learn Series Of Actions


There are 4 main parts of machine learning.


  • Machine Learning
  1. Supervised :
  • Classification
  • Regression
  1. Unsupervised :
  • Clustering
  • Association

Supervised  


1) Classification 


The data needs to be divided into a number of different categories based on training using past data.

An example of a classification problem, would be how we are able to sort emails are spam or otherwise using previously received emails that have been already identified. 

A famous algorithm that can be used to solve classification problems, is the Naive Bayes theorem New Mail Classification Not Not spam Senator on spam Regression Learns Spam Spam Enables the machine to be trained to classify observations into some class

2) Regression 


 We predict a value for an input based on previously received information. 

Although this sounds similar to classification considering that they both use past data to make predictions, their similarity ends there of regression, 

you're trying to In the case estimate a value and not just a class of an observation Now let's consider weather prediction.

The likelihood of it raining today can be calculated by taking weather factors like temperature, humidity a measurement of other pressure, wind-speed, wind-direction and then seeing how they correlate to rains in the past. 

If the measurements taken today are strongly correlated to days when it rained then the likelihood of it raining is high today The linear regression algorithm is one that's commonly used to solve this problem.

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Unsupervised 


1) Clustering


This uses a method where we assign a set of observations into subsets. These subsets are known as clusters. 

The observations inside these clusters are similar to one another, based on some parameter or other Hence, all the data is divided into clusters.

An example of when clustering is used, when a telecom provider wants to set up a network in a region by setting up towers there,

They use the clustering algorithm, taking into consideration areas that would provide optimum connectivity to all users and the maximum range a cell tower would have, to divide the entire region into clusters. 

K Means is a prominently used method to cluster data in k-clusters based on some similarity measures. 

2) Association


In an association problem, we identify patterns of associations between different variables or items. 

Its concepts are applied to e-commerce websites, where they're able to suggest other items for you to buy.

It is based on the prior purchases that you've done Previous shopping history Suggests Clustering Association Learns Flip-kart, amazon Identifies patterns of association between different variables and items

I hope this article is help to you for learn types of machine learning.

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Things You Should Know About Python Before Learning Python :


Python is an interpreted, high-level, general-purpose programming language. Developed by Guido van Rossum and first released in 1991,

The Reasons  For Why Writing Software Applications in Python


1) Readable and Maintainable Code
2) Multiple Programming Paradigms
3) Compatible with Major Platforms and Systems
4) Robust Standard Library
5) Many Open Source Frameworks and Tools
6) Simplify Complex Software Development
7) Adopt Test Driven Development

The Python Applications :


1) Web and Internet Development
2) Applications of Python Programming in Desktop GUI
3)  Science and Numeric Applications
4) Software Development Application
5) Python Applications in Education
6) Python Applications in Business
7) Database Access
8) Network Programming
9) Games and 3D Graphics
10) Console-based Applications
11) Audio – or Video- based Applications
12) Applications for Images
13) Enterprise Applications
14) 3D CAD Applications
15) Computer Vision
16) Machine Learning
17) Robotics
18) Web Scraping
19) Scripting
20) Artificial Intelligence
21) Data Analysis


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Top 7 Best Programming Languages For Machine Learning!!

Top 7 Best Programming Languages For Machine Learning!!


In this article we discuss about 7 best programming language used for Machine Learning.

  1. Python
  2. Java
  3. R
  4. C++
  5. Lisp
  6. Matlab
  7. Prolog
(1) Python :

- Python described as simple and easy to learn.
- It has the simple syntax which makes easier to work in complex systems.
- In Python we can use python and other programming languages for reach our goal.
- In Python we can choose between scripting and OOPs.
- Python has a rich library system.
   it includes pre-written code that allows user to perform different actions.it gives some base level codes. so, programmer don't need to write code every time.
  • Pandas
  • Jupyter 
  • TensorFlow
  • Scikit-learn
  • Keras

(2) Java :

- Java provides many benefits like : Easy to use
                                                        - Simplified work
                                                        - Easy to work
                                                        - Easy debugging
                                                        - Package services
                                                        - Good user interaction etc..



- Libraries of Machine Learning for java,
  • Weka
  • Deeplearning4j
  • MOA
  • MALLET
  • ELKI

(3) R :


- R allows rapid prototyping and working with data sets.


- There is advantage of advanced data analysis packages that cover the pre-modeling,modeling,post-modeling stages.

(4) C++ :


- C++ creates more compact and faster runtime code.
- In C++ major compilers tend to do specific optimisation and platform specific.
- C++ code is low-level language which means it is easy for computer to read and harder for humans.



- But developers prefer python over C++ because it has more libraries and more easier for developing.


(5) Lisp :


- Lisp allows the construction of executable code at Run-time.
- Lisp has macros that works at the syntax tree level.
- The default data structure for Lisp is linked list.


(6) Matlab :

- Machine Learning is basically maths based programming.
- Matlab gives facility to express maths directl.
- Matlab provides the appropriate tools.
- Matlab provides function and capabilities the developer need.



- Matlab complete tasks easily than other custom programming.
- Matlab runs your program faster and also try bigger problems

(7)  Prolog :

- Prolog is declarative programming language.
- The program logic is expressed in terms of relationship, represented as facts and rules.




What Is Machine Learning

What Is Machine Learning??





We Discuss in this article about :

 What Is Machine Learning ?
 How Does Machine Learning Work ?
 Types Of Machine Learning 
 Applications Of Machine Learning.

What Is Machine Learning?


- So, as we know humans are learn from their past experiences and machines are follow the instructions.
- If machine learn from human how to learn from past experiences Then it is Machine Learning.
- Machine Learning works from on the data development of computer program that can be access data and use it to automatically learn and improve from experiences.

Definition by Tom Mitchell (1998) :

Machine learning is the study of algorithms that
  • Improve their performance P
  • At some task T
  • With experience E.
A well defined learning task is given by <P,T,E>. 

Examples Of Machine Learning In Day To Day Life :

  1. Alexa
  2. Image Recognition
  3. Speech Recognition
  4. Medical Diagnosis etc...

How Does Machine Learning Work ?


Machine Learning uses algorithm to mimic human learnings in machines. It is a subset of Artificial Intelligence.
In Machine Learning There is no need to write programs, Computer automatically generates the program.

Work : 

- We  create a model with trained machine learning algorithm.
- When we gives input to the machine it checks the machine learning algorithm then it creates a prediction and gives output.
- If Output is right then prediction is right and all OK.
- If Output is not right then from the feedback create a further machine learning algorithm and checks till the output not comes right.
- so, this how Machine Learning learns from the mistakes.



Difference between Traditional Programming and Machine Learning :



For more information we have to know about Types Of Machine learning.


Types Of Machine Learning



In Machine Learning machines are used the algorithms and perform on the basis of algorithm and stored input data in the system.

Types :


There Is three part of machine learning :

1) Supervised :
- Develop predictive model based on input and output data.  
- Makes Machine learn explicitly.
- Direct Feedback given.
- Predicts outcome.



2) Unsupervised :
- Group and interpret data based only on input data.
- Does not predict.
- Machine Understands the data.
- Evaluation is indirect.


3) Reinforcement : 
- It learns from the mistake.
- An approach of AI.
- Machine learn how to act in certain environment.

We will discuss in brief about types of machine learning in next article. this is an overview of types for understanding. 


Applications Of Machine Learning 



Social Media : Sentiment Analysis, Filtering spam etc..

Genetics : Exposure, Latent defect, haritable pathology etc..

Financial Services : Algorithm trading, Portfolio Management, Fraud detection etc.. 

Healthcare : Drug Discovery, Disease Diagnosis, Robotic Surgery etc..

Virtual Assistant : Intelligent Agents, Natural Language, Processing etc..

eCommerce : Customer Support, Product Recommendation, Advertising etc..

Transport : Safety, Monitoring, Air traffic control etc.. 


In Addition Answers For  Some Questions Such As :


What Is Machine Learning ?


1) You Can also say that Machine Learning is an extensively algorithm driven study which makes software capable of learning on the basis of experience and improve performance for task.

2) Machine Learning Stands fr learning defined as the acquisition of knowledge or skills through experience, study or by being taught.

3) Machine Learning is the intersection between theoretically sound computer science and practically noisy data.
Essentially, it's about machines making sense out of data in much the same way that we humans do.

4) It is about improving the machine's performance on a specified task with it's experience.


In this article we learn about Machine Learning in short, in next article we discussed in deep about supervised,unsupervised and reinforcement learning with their example.