FREE Online Classes to learn Data Science, Blockchain, Big Data :

Just choose your learning path, finish the courses and put the #Badge in your LinkedIn profile to attract more recruiters!

✅The learning path:
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💡List of some the courses:
1)Introduction to Data Science
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2)Data Science Tools
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3)Data Science Methodology
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4)Statistics
https://lnkd.in/fpgJf7D
5)Predictive Modeling Fundamentals I
https://lnkd.in/f9_Y7UZ
6)Python for Data Science
https://lnkd.in/fy8E2wH
7)Data Analysis with Python
https://lnkd.in/fRQWByd
8)Data Visualization with Python
https://lnkd.in/fFu93ME
9)Machine Learning with Python
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10)Deep Learning Fundamentals
https://lnkd.in/fNvPvix
11)Deep Learning with TensorFlow
https://lnkd.in/ftfRtvQ
and many more...

📚Don't miss top 5 free essential books for Data scientists:
https://lnkd.in/gKYqpfV

#datascience #deeplearning #python #machinelearning #ai #hadoop #bigdata #scala #kubernetes #blockchain

✴️ @AI_Python_EN

Uncertainty in big data analytics: survey, opportunities, and challenges

https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0206-3

#BigData #statistics #NLP

✴️ @AI_Python_EN

The Deep Learning Toolset — An Overview

http://bit.ly/2R2eUGh

#artificialintelligence, #bigdata, #businessanalytics, #datascience, #deeplearning, #ds #datascience, #iot, #machinelearning, #neuralnetworks #AI

✴️ @AI_Python_EN

DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

Paper: http://ow.ly/Nk6Y50uAmII
#artificialinteligence #ai #ml #machinelearning #bigdata #deeplearning #technology

✴️ @AI_Python_EN

Why Should you Learn AI and Machine Learning

Why Machine Learning Fascinates Me?

Supervised Machine Learning

Do you know what is Machine Learning All About?

The Science of Machine Learning is about Learning the Models that Generalize Well Machine learning is an area of artificial intelligence and computer science This includes the development of software and algorithms that can make predictions based on data.

Data Science Enthusiasts, I have Created a Community for Us to Learn Together🗝

Interested people let me know in the Comments and I will send you the invite link to our Community🎟🗣

#reinforcementlearning #machinlearning #Datascience #ArtificialIntelligence #gans
#SupervisedMachineLearning #ML #dl #iot #bigdata

✴️ @AI_Python_EN

Don't mess with Robots! 😂😂

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Bosstown Robotics

✴️ @AI_Python_EN

11 things I learned from the Machine Learning for Coders course at fastdotai"
https://medium.com/yottabytes/11-things-i-learned-from-the-machine-learning-for-coders-course-at-fast-ai-799468b089bc?source=friends_link&sk=337416e814280d88e7bfad994cac8533
#machinelearning #datascience #ml #python #bigdata

✴️ @AI_Python_EN

I am a big fan of small data for big discovery in #DataScience:
https://mapr.com/blog/when-big-data-goes-local-small-data-gets-big-part-2/

———
But #DeepLearning needs large labeled training sets of #BigData:
https://hackernoon.com/%EF%B8%8F-big-challenge-in-deep-learning-training-data-31a88b97b282

———

Human-Machine Collaborative Annotation with #MachineLearning can help:
https://onlinelibrary.wiley.com/doi/full/10.1002/bult.2013.1720390414

✴️ @AI_Python_EN

Getting System Information in Linux using Python Script.
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http://bit.ly/2X56cZa

✴️ @AI_Python_EN

Quantile Regression Deep Reinforcement Learning

Researchers: Oliver Richter, Roger Wattenhofer
Paper: https://lnkd.in/fnwiYXi
#artificialintelligence #ai #ml #machinelearning #bigdata #deeplearning #technology #datascience

✴️ @AI_Python_EN

Anticipatory Thinking: A Metacognitive Capability
Researchers: Adam Amos-Binks, Dustin Dannenhauer
Paper: http://ow.ly/wEyC50uR9q1

#artificialintelligence #ai #ml #machinelearning #bigdata #deeplearning #technology #datascience

✴️ @AI_Python_EN

#AI/ #DataScience/ #MachineLearning/ #ML:
7 Steps for Data Preparation Using #Python

Link => https://bit.ly/PyDataPrep

#datamining #statistics #bigdata #artificialintelligence

✴️ @AI_Python_EN

Data in the Life: Authorship Attribution in Lennon-McCartney Songs", was just published in the first issue of the HARVARD DATA SCIENCE REVIEW, the inaugural publication of harvard datascience published by the mit press. Combining features of a premier research journal, a leading educational publication, and a popular magazine, HDSR leverages digital technologies and data visualizations to facilitate author-reader interactions globally. Besides our article, the first issue features articles on topics ranging from machine learning models for predicting drug approvals to artificial intelligence. Read it now:
https://bit.ly/2Kuze2q.
#datascience #bigdata #machinelearing #statistics #AI

✴️ @AI_Python_EN

Artificial Intelligence: the global landscape of ethics guidelines

Researchers: Anna Jobin, Marcello Ienca, Effy Vayena
Paper: http://ow.ly/mDA430p2R0q

#artificialintelligence #ai #ml #machinelearning #bigdata #deeplearning #technology #datascience

✴️ @AI_Python_EN

A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019). This is a lightweight graph convolutional neural network for the fast calculation of approximate graph similarity at scale. Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound.

https://lnkd.in/gA5tfuC

#datamining #machinelearning #deeplearning #datascience #bigdata

✴️ @AI_Python_EN

Pytorch Implementation of Deep Flow.

#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #TensorFlow #CloudComputing #DataScientist #Linux

https://arxiv.org/pdf/1905.02884.pdf

✴️ @AI_Python_EN

Understanding the Backpropagation Algorithm.

#BigData #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #TensorFlow #CloudComputing #Algorithms

http://bit.ly/2ASKwqx

❇️ @AI_Python_EN

What's the purpose of statistics?

"Do you think the purpose of existence is to pass out of existence is the purpose of existence?" - Ray Manzarek

The former Doors organist poses some fundamental questions to which definitive answers remain elusive. Happily, the purpose of statistics is easier to fathom since humans are its creator. Put simply, it is to enhance decision making.

These decisions could be those made by scientists, businesspeople, politicians and other government officials, by medical and legal professionals, or even by religious authorities. In informal ways, ordinary folks also use statistics to help make better decisions.

How does it do this?

One way is by providing basic information, such as how many, how much and how often. Stat in statistics is derived from the word state, as in nation state and, as it emerged as a formal discipline, describing nations quantitatively (e.g., population size, number of citizens working in manufacturing) became a fundamental purpose. Frequencies, means, medians and standard deviations are now familiar to anyone.

Often we must rely on samples to make inferences about our population of interest. From a consumer survey, for example, we might estimate mean annual household expenditures on snack foods. This is known as inferential statistics, and confidence intervals will be familiar to anyone who has taken an introductory course in statistics. So will methods such as t-tests and chi-squared tests which can be used to make population inferences about groups (e.g., are males more likely than females to eat pretzels?).

Another way statistics helps us make decisions is by exploring relationships among variables through the use of cross tabulations, correlations and data visualizations. Exploratory data analysis (EDA) can also take on more complex forms and draw upon methods such as principal components analysis, regression and cluster analysis. EDA is often used to develop hypotheses which will be assessed more rigorously in subsequent research.

These hypotheses are often causal in nature, for example, why some people avoid snacks. Randomized experiments are generally considered the best approach in causal analysis but are not always possible or appropriate; see Why experiment? for some more thoughts on this subject. Hypotheses can be further developed and refined, not simply tested through Null Hypothesis Significance Testing, though this has been traditionally frowned upon since we are using the same data for multiple purposes.

Many statisticians are actively involved in designing research, not merely using secondary data. This is a large subject but briefly summarized in Preaching About Primary Research.

Making classifications, predictions and forecasts is another traditional role of statistics. In a data science context, the first two are often called predictive analytics and employ methods such as random forests and standard (OLS) regression. Forecasting sales for the next year is a different matter and normally requires the use of time-series analysis. There is also unsupervised learning, which aims to find previously unknown patterns in unlabeled data. Using K-means clustering to partition consumer survey respondents into segments based on their attitudes is an example of this.

Quality control, operations research, what-if simulations and risk assessment are other areas where statistics play a key role. There are many others, as this page illustrates.

The fuzzy buzzy term analytics is frequently used interchangeably with statistics, an offense to which I also plead guilty.

"The best thing about being a statistician is that you get to play in everyone's backyard." - John Tukey

#ai #artificialintelligence #ml #statistics #bigdata #machinelearning
#datascience

❇️ @AI_Python_EN