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Machine Learning Tutorial
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Naive Bayes in Machine Learning
    • Dataset Labelling
    • DataSet Example
    • Deep Learning Techniques
    • Dataset ZFS
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • What is Kernel in Machine Learning
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Machine Learning Certifications
    • Machine Learning Python vs R
    • Optimization for Machine Learning
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Support Vector Machine in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • Dataset for Linear Regression
    • Decision tree limitations
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Apriori Algorithm in Machine Learning
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • Hierarchical Clustering Agglomerative
    • What is a Greedy Algorithm?
    • What is Genetic Algorithm?
    • Random Forest Algorithm
    • Nearest Neighbors Algorithm
    • Weak Law of Large Numbers
    • Ray Tracing Algorithm
    • SVM Algorithm
    • Naive Bayes Algorithm
    • Neural Network Algorithms
    • Boosting Algorithm
    • XGBoost Algorithm
    • Pattern Searching
    • Loss Functions in Machine Learning
    • Decision Tree in Machine Learning
    • Hyperparameter Machine Learning
    • Unsupervised Machine Learning
    • K- Means Clustering Algorithm
    • KNN Algorithm
    • Monty Hall Problem
  • Supervised
    • What is Supervised Learning
    • Supervised Machine Learning
    • Supervised Machine Learning Algorithms
    • Perceptron Learning Algorithm
    • Simple Linear Regression
    • Polynomial Regression
    • Multivariate Regression
    • Regression in Machine Learning
    • Hierarchical Clustering Analysis
    • Linear Regression Analysis
    • Support Vector Regression
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning Architecture
    • Automation Anywhere Architecture
    • Machine Learning C++ Library
    • Machine Learning Frameworks
    • Data Preprocessing in Machine Learning
    • Data Science Machine Learning
    • Classification of Neural Network
    • Neural Network Machine Learning
    • What is Convolutional Neural Network?
    • Single Layer Neural Network
    • Kernel Methods
    • Forward and Backward Chaining
    • Forward Chaining
    • Backward Chaining
  • Deep Learning
    • What Is Deep learning
    • Overviews Deep Learning
    • Application of Deep Learning
    • Careers in Deep Learnings
    • Deep Learning Frameworks
    • Deep Learning Model
    • Deep Learning Algorithms
    • Deep Learning Technique
    • Deep Learning Networks
    • Deep Learning Libraries
    • Deep Learning Toolbox
    • Types of Neural Networks
    • Convolutional Neural Networks
    • Create Decision Tree
    • Deep Learning for NLP
    • Caffe Deep Learning
    • Deep Learning with TensorFlow
  • RPA
    • What is RPA
    • What is Robotics?
    • Benefits of RPA
    • RPA Applications
    • Types of Robots
    • RPA Tools
    • Line Follower Robot
    • What is Blue Prism?
    • RPA vs BPM
  • Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

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Careers in Machine Learning

By Priya PedamkarPriya Pedamkar

Careers in Machine Learning

Introduction to Careers in Machine Learning

Machine learning is the field of AI that provides the ability to the system to learn on its own without any human intervention at higher accuracy, due to which it is highly required in the area of Information Technology Industry and the developers working in these technologies are assigned the role of Machine Learning Engineer. Initially, it is followed by the Architect level position whose work is to design the prototype for the applications that needs to be developed, starting salary of the machine learning engineer as per the American website is 100,000 dollars annually.

Education Required for Machine Learning

Machine Learning needs a lot of basic computers science concepts and one should be strong in computer science concepts such as Mathematical, Data Structures, and Algorithms subjects like computations, statistics, etc. Strong knowledge of basic mathematics is also recommended. Machine Learning is the core component of Artificial Intelligence where one needs to show much interest and enthusiasm in learning these concepts.

  • Machine Learning is evolving quite rapidly and gradually nowadays. A lot of technology professionals are required in the coming years in the area of Machine Learning.
  • Machine Learning includes technology, mathematics, statistics, business knowledge, and many technical and logical skills to excel in this area. Data analysis is one of the main elements of the Machine Learning area where this area mainly depends on data in which the machine learns on its own.
  • This requires a lot of valuable data to be processed before a machine is learning itself. A Data Analyst can easily transform his/her career in Machine Learning. Python is the most used programming language in the area of Machine Learning. This is also included in most of the academic programs as well in most of the universities.

Career Path

Machine Learning Professionals are highly required in the area of the Information Technology Industry across the world especially in the USA. Machine Learning reduces a lot of human efforts easily by reducing pain and errors. Most of the companies are starting automation and those also need the Machine learning technology to be implemented across their business units to increase their performance and efficiency while reducing the costs.

  • The career path initially starts as a Machine Learning Engineer, who will be developing applications that perform some common tasks done by human beings and this will be used for repeated things that will perform without any errors and produces effective results.
  • A Machine Learning Engineer role will be followed by the Architect level position. The next level of a career path in the Architect level will be of some role to design and develop the prototypes for the applications to be developed.
  • Even a software engineer with some years of experience can switch their careers in the Machine Learning area. A Python Developer or a data scientist can also easily switch careers in Machine Learning.
  • Persons even without any experience in software engineering can also start their careers in Machine Learning if they have some string knowledge in computer science, mathematics, statistics, etc.

Job Positions or Application Areas

In the area of Machine Learning, there are different roles available in the information technology industry to pursue the career are such as Machine Learning Engineer, Senior Machine Learning Engineer, Lead Machine Learning Engineer, Machine Learning Engineer Front Office and Back office, Principal Engineer – Machine Learning, Machine Learning Software Engineer, Data Scientist, Senior Data Scientist, Data Scientist IT, Senior Data Scientist IT, etc. The Machine Learning Engineer possesses some strong core knowledge of Computer Science concepts, a solid Mathematics background with Statistics as well.

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Salary

The average salary pay of the Machine Learning Engineer in the United States is $100,956 per year as per the top American website that provides salary and compensation information about different companies Payscale.com. Moreover, this position has candidates with no more than 10 years of experience in the industry.

The national average salary for Machine Learning as mentioned in another top salary information website Glassdoor.com is $120,931 in the United States.

The most famous website Indeed.com also mentions that the average salary for careers in the Machine Learning area is $135,246 per year.

The average median salaries for the different SharePoint career paths are as below:

  • Data Scientist (US$69000 – US$133000)
  • Senior Data Scientist (US$98000 – US$160000)
  • Machine Learning Engineer (US$77000 – US$155000)
  • Data Scientist in IT (US$69000 – US$129000)
  • Senior Data Scientist IT (US$92000 – US$164000)

A Machine Learning Engineer earns a median salary of around USD 112,622 in the United States.

Career Outlook in Machine Learning

There are multiple and different career paths in the area of Machine Learning and also the average salaries are also big figures in the Machine Learning career path. This suggests that the future for one who wants to enter into the area of Machine Learning will be bright and exciting. There will be huge numbers of requirements for the persons with skills set in the area of Machine Learning in the coming future.

  • There are also multiple career paths to move on after entering into the Machine Learning Engineer area like Artificial Intelligence, Data Science and Data Analytics, etc.
  • An IT professional with some good communication skills and a strong technical skillset with a solid mathematics or statistics background can reach some top heights in their careers like Senior Architects or Senior Subject Matter Experts in the career of Machine Learning or Artificial Intelligence.
  • The requirements for the job positions in the area of Machine Learning Engineer in the United States are increasing daily in large numbers. Because of the day-to-day routine activities or tasks in the large customer based companies, the job handling responsibilities need to be very accurate and error-free for successful business deliverers to the customers.
  • Machine Learning Software applications or products are a great need for businesses to maintain the customers’ content data secure, Machine Learning Engineer is one of the best technological advancements available in the market to provide some high complexity business solutions.

Recommended Articles

This has been a guide to Careers in Machine Learning. Here we have discussed the introduction, education, career path, job positions along with salary and career outlook. You may also look at the following article to learn more –

  1. Careers in SharePoint
  2. Develop Your Career Using Kaizen
  3. Career Advice for College Students
  4. Web Development Professional
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