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Scalable Machine Learning on Big Data using Apache Spark
This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark.
Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer.
Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner.
gain a practical understanding of Apache Spark, and apply it to solve machine learning problems involving both small and big data -
eliminate out-of-memory errors generated by traditional machine learning frameworks when data doesn’t fit in a computer's main memory -
10 Questions to Consider Before Pursuing a Career in Data Science
The ongoing “data rush” is, therefore, attracting so many professionals with diverse backgrounds such as physics, mathematics, statistics, economics, and engineering.
This article will discuss 10 important questions that everyone interested in data science should consider before pursuing a career as a data scientist.
Their job role includes data collection, data transformation, data visualization, and analysis, building predictive models, providing recommendations on actions to implement based on data findings.
How much you make as a data scientist depends on the organization or company you are working for, your educational background, number of years of experience and your specific job role.
The point here is that you don’t need ten years to learn the basics of programming, but learning programming in a rush is certainly not helpful.
Your role as a data scientist is to draw out meaning insights from data that can be used for data-driven decisions that can improve the efficiency of your company or improve the way business is conducted, or help increase profits.
If you have a solid background in an analytical discipline such as physics, mathematics, engineering, computer science, economics, or statistics, you can basically teach yourself the basics of data science.
After establishing a strong foundation in data science concepts, you may seek an internship or participate in Kaggle competitions where you get to work on real data science projects.
Here are some: Data Science 101 — A Short Course on Medium Platform with R and Python Code Included Professional Certificate in Data Science (HarvardX, through edX) Analytics: Essential Tools and Methods (Georgia TechX, through edX) Applied Data Science with Python Specialization (the University of Michigan, through Coursera) In summary, we’ve discussed 10 important questions that everyone interested in pursuing a career in data science should consider.
- On Friday, May 29, 2020
IBM AI Certification
IBM, together with The Open Group, announced the creation of a first-of-a-kind Data Scientist certification that will give organizations of any size the ability to ...
My 5 favourite Coursera Courses for Python, Data Science and Machine Learning
These are my 5 favourite Coursera courses for learning python, data science and Machine Learning.
Artificial Intelligence Full Course | Artificial Intelligence Tutorial for Beginners | Edureka
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How to become IBM Certified Advanced Data Scientist with Coursera
IBM Watson DataScientist on Coursera, AI, Deep Learning, Keras, TensorFlow, PyTorch
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PG Diploma in Machine Learning & Artificial Intelligence program - Live Webinar
Columbia Engineering, in partnership with EMERITUS, has created the Applied Machine Learning (Online) course. The webinar is presented by Tom Dougherty, ...
John Cohn recommends Applied AI with DeepLearning course on coursera, DataScience Certificate
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Machine Learning is harder than I thought #coursera
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How To Learn Artificial Intelligence For Free From Scratch || Learn from Top Companies || AI Courses
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