Knowledge Hut

MAY 9, 2024

Skills Required Skills necessary for AI engineers are programming languages, statistics, deep learning, natural language processing, and problem-solving with communication skills. Average Annual Salary of Machine Learning Engineer A machine learning engineer can earn over $132,910 on average per year.

Data Science 52

Data Science Data Mining Data Architect Programming Language 52

Knowledge Hut

JANUARY 16, 2024

Needless to say, the applications of machine learning go far beyond recommendation engines and into many other fields. Some of the applications with promising career prospects include healthcare, finance, insurance, pharmaceuticals, farming, retail, oil and gas, and more. Who Is a Machine Learning Engineer?

Machine Learning 98

Machine Learning Engineering Programming Language Algorithm 98

ProjectPro

JANUARY 20, 2022

These statistics show that it's a perfect time to pursue a career in machine learning and artificial intelligence. Get Closer To Your Dream of Becoming a Data Scientist with 150+ Solved End-to-End ML Projects How much does an ML Engineer earn? Deep learning and computer vision-related careers may demand higher degrees.

Machine Learning 52

Machine Learning Deep Learning Algorithm Programming Language 52

ProjectPro

DECEMBER 22, 2016

Prepare for Your Next Big Data Job Interview with Kafka Interview Questions and Answers Robert Half Technology survey of 1400 CIO’s revealed that 53% of the companies were actively collecting data but they lacked sufficient skilled data analysts to access the data and extract insights.

Data Mining 52

Data Mining Data Cleanse Datasets Data Analysis 52

ProjectPro

FEBRUARY 24, 2016

If you do a quick google search for “data science jobs” or “data scientist jobs”, you will come across data scientist job opportunities in every industry. There is a need for data scientists across all industries. R is the language of choice for doing data analysis.

Hadoop 40

Hadoop Data Science Computer Science R (Programming) 40

ProjectPro

AUGUST 21, 2015

IBM has a nice, simple explanation for the four critical features of big data: a) Volume –Scale of data b) Velocity –Analysis of streaming data c) Variety – Different forms of data d) Veracity –Uncertainty of data Here is an explanatory video on the four V’s of Big Data 3.

Hadoop 40

Hadoop Big Data Metadata Java 40