Online Learning With Amazon
Amazon is now offering these free courses on its online learning platform.
If you get access to any of these courses before the 9th of December, you will have free access to those courses purchased until April 2023.
If you find any of these courses interesting, you can check out other courses for free on their platform before Dec 9.
1) The Elements of Data Science | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:
2) Data Analytics Fundamentals | Data Analytics (BigData) Online Course | AWS Training & Certification
🔗 Course Link:
3) Math for Machine Learning | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:
4) Machine Learning for Business Challenges | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:
5) Linear and Logistic Regression | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:
6) Machine Learning for Leaders | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:
7) Data Science Capstone: Real World ML Decisions | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link
8) Computer Vision with GluonCV | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link
#data_science #datascience #Amazon #data_analysis #machine_learning
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉 Join @datascience_bds for more cool data science materials.
*This channel belongs to the @bigdataspecialist group
Amazon is now offering these free courses on its online learning platform.
If you get access to any of these courses before the 9th of December, you will have free access to those courses purchased until April 2023.
If you find any of these courses interesting, you can check out other courses for free on their platform before Dec 9.
1) The Elements of Data Science | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:
2) Data Analytics Fundamentals | Data Analytics (BigData) Online Course | AWS Training & Certification
🔗 Course Link:
3) Math for Machine Learning | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:
4) Machine Learning for Business Challenges | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:
5) Linear and Logistic Regression | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:
6) Machine Learning for Leaders | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link:
7) Data Science Capstone: Real World ML Decisions | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link
8) Computer Vision with GluonCV | Machine Learning Online Course | AWS Training & Certification
🔗 Course Link
#data_science #datascience #Amazon #data_analysis #machine_learning
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉 Join @datascience_bds for more cool data science materials.
*This channel belongs to the @bigdataspecialist group
500+ AI, Machine learning, Deep learning ,Computer vision, NLP Projects with code
Creator: ashishpatel26
Stars ⭐️: 10.7K
Forked By: 3.2K
GitHub Repo: Link
#data_science #deep_learning #nlp #data_analysis #machine_learning #computer_vision #ai #neural_networks
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉 Join @datascience_bds for more cool data science materials.
*This channel belongs to the @bigdataspecialist group
Creator: ashishpatel26
Stars ⭐️: 10.7K
Forked By: 3.2K
GitHub Repo: Link
#data_science #deep_learning #nlp #data_analysis #machine_learning #computer_vision #ai #neural_networks
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉 Join @datascience_bds for more cool data science materials.
*This channel belongs to the @bigdataspecialist group
GitHub
GitHub - ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code: 500 AI Machine learning Deep…
500 AI Machine learning Deep learning Computer vision NLP Projects with code - ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
One of the most frequent questions I got is how to start with data science and machine learning as a complete beginner, and what skills do you need to have. Do you need to know programming, do you need to know math etc.
Below is my answer I wrote on my discord server, few years ago. It's still relevant and hopefully helpful.
Here are some things you should be familiar with to start your journey as data scientist:
Statistics
You need to have some statistical knowledge, like theory of probability, bayes theorem, probability distributions (uniform, normal/gaussian, logarithmic, exponential, chi-square distribution etc), you should know some basics like what is mean, median and mode. You should understand hypothesis testing and statistical significance as well. If mentioned terms are not familiar to you try researching about them. I shared 4 books of statistics for data science here at discord, they might be useful.
Programming
Generally you are going to need some programming background, which languages have you used before?
Most of people use python, it's great for preparing data as well as using some ML packages for creating machine learning models. What is great about Python is that it's very beginner friendly. R programming language is another option for data science/machine learning. Java and Scala offers nice libraries for data science as well. I personally use Java at my work.
Most important libraries
In case Python is your first choice (and it probably is if you are beginner) then you should check pandas - the biggest library for data manipulation and data analysis, numpy - library for multidimensional arrays and matrices, there are many libraries for machine learning as Keras (Deep learning), Scikit-learn, PyTorch, TensorFlow. Some libraries for data visualization are also important - biggest is matplotlib but there are also Seaborn, Plotly, ggplot, Bokeh...
When it comes to java i use deeplearning4j, ApacheSpark, Apache Hadoop, and bunch of NLP (Natural Processing Libraries) which are not so important now if you are total beginner. We will get you there eventually.
Where to start?
If this sounds like too much for you don't worry, that is just an overview of situation in the field. You don't have to know all those libraries, some basics of Pandas, Numpy and maybe Scikit-learn for beginning is enough.
First thing i have ever read about machine learning which is very important for data science is this medium article:
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
It's subtitle is: The world’s easiest introduction to Machine Learning and it's not far form truth. After i read this i understood machine learning as well as data science much better.
Tip: medium allows you to read 3 articles for free per month, but if you open them in incognito mode you have unlimited access to all articles for free smile
After finishing this try researching about other ML concepts like: Types of ML algorithms, classification and regression problems, overfitting/underfitting, model evaluation techniques and measures etc.
I would definitely recommend Andrew Ng's courses on coursera, some of them are available on yt as well.
Once you understand basic concepts, you can dive deeper in data science. Learn about datasets, how to prepare data, how to handle missing values, how to perform feature engineering etc. and try to solve some real world data science problems. I shared 500+ interesting data science projects with source code in post above. I also shared a data science live course by UC Berkeley, Fall 2022. Go check that as well.
Phew 😅 , that was lots of text. I got really tired writing it. But since i get 10-20 of these questions every day, mostly on Instagram and WhatsApp, it's better to have all written in one place. I hope i helped, good luck with your data science journey!
#data_science #datascience #Berkeley
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉 Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Below is my answer I wrote on my discord server, few years ago. It's still relevant and hopefully helpful.
Here are some things you should be familiar with to start your journey as data scientist:
Statistics
You need to have some statistical knowledge, like theory of probability, bayes theorem, probability distributions (uniform, normal/gaussian, logarithmic, exponential, chi-square distribution etc), you should know some basics like what is mean, median and mode. You should understand hypothesis testing and statistical significance as well. If mentioned terms are not familiar to you try researching about them. I shared 4 books of statistics for data science here at discord, they might be useful.
Programming
Generally you are going to need some programming background, which languages have you used before?
Most of people use python, it's great for preparing data as well as using some ML packages for creating machine learning models. What is great about Python is that it's very beginner friendly. R programming language is another option for data science/machine learning. Java and Scala offers nice libraries for data science as well. I personally use Java at my work.
Most important libraries
In case Python is your first choice (and it probably is if you are beginner) then you should check pandas - the biggest library for data manipulation and data analysis, numpy - library for multidimensional arrays and matrices, there are many libraries for machine learning as Keras (Deep learning), Scikit-learn, PyTorch, TensorFlow. Some libraries for data visualization are also important - biggest is matplotlib but there are also Seaborn, Plotly, ggplot, Bokeh...
When it comes to java i use deeplearning4j, ApacheSpark, Apache Hadoop, and bunch of NLP (Natural Processing Libraries) which are not so important now if you are total beginner. We will get you there eventually.
Where to start?
If this sounds like too much for you don't worry, that is just an overview of situation in the field. You don't have to know all those libraries, some basics of Pandas, Numpy and maybe Scikit-learn for beginning is enough.
First thing i have ever read about machine learning which is very important for data science is this medium article:
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
It's subtitle is: The world’s easiest introduction to Machine Learning and it's not far form truth. After i read this i understood machine learning as well as data science much better.
Tip: medium allows you to read 3 articles for free per month, but if you open them in incognito mode you have unlimited access to all articles for free smile
After finishing this try researching about other ML concepts like: Types of ML algorithms, classification and regression problems, overfitting/underfitting, model evaluation techniques and measures etc.
I would definitely recommend Andrew Ng's courses on coursera, some of them are available on yt as well.
Once you understand basic concepts, you can dive deeper in data science. Learn about datasets, how to prepare data, how to handle missing values, how to perform feature engineering etc. and try to solve some real world data science problems. I shared 500+ interesting data science projects with source code in post above. I also shared a data science live course by UC Berkeley, Fall 2022. Go check that as well.
Phew 😅 , that was lots of text. I got really tired writing it. But since i get 10-20 of these questions every day, mostly on Instagram and WhatsApp, it's better to have all written in one place. I hope i helped, good luck with your data science journey!
#data_science #datascience #Berkeley
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉 Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Medium
Machine Learning is Fun!
The world’s easiest introduction to Machine Learning
DIMENSIONALITY REDUCTION
Have you heard of Dimensionality Reduction👀?
If this is your first time😃, then get your seats closer🙂.
It means trimming down data to remove unwanted features👌.
Did this make any sense🤷♀️? If it didn't then you must know that whenever you have a very large dataset, It can help you capture the majority of your dataset's information within a few number of features.
Here's one method😃 of Dimensionality Reduction you must know.
It's the Principal Component Analysis (PCA)😎. It gives us the ability to plot multivariate data🤯 in 2 dimensions and works perfectly☺️ in identifying the axis of greatest variance in our dataset.
In this method, we take old sets of variables and convert them into a newer set. The new sets created are called principal components⭐️. There is a trade-off between reducing the number of variables while maintaining the accuracy of your model👍🏼.
The next time you have problems working with very large datasets 🤯, you could try Dimensionality Reduction😉
Have you heard of Dimensionality Reduction👀?
If this is your first time😃, then get your seats closer🙂.
It means trimming down data to remove unwanted features👌.
Did this make any sense🤷♀️? If it didn't then you must know that whenever you have a very large dataset, It can help you capture the majority of your dataset's information within a few number of features.
Here's one method😃 of Dimensionality Reduction you must know.
It's the Principal Component Analysis (PCA)😎. It gives us the ability to plot multivariate data🤯 in 2 dimensions and works perfectly☺️ in identifying the axis of greatest variance in our dataset.
In this method, we take old sets of variables and convert them into a newer set. The new sets created are called principal components⭐️. There is a trade-off between reducing the number of variables while maintaining the accuracy of your model👍🏼.
The next time you have problems working with very large datasets 🤯, you could try Dimensionality Reduction😉
Graphic SQL reference for data manipulation
Source: https://raw.githubusercontent.com/kelsfarmer/SQL/master/SQL%20Data%20Manipulation%20Language%20Cheat%20Sheet.png
Source: https://raw.githubusercontent.com/kelsfarmer/SQL/master/SQL%20Data%20Manipulation%20Language%20Cheat%20Sheet.png
Often the hardest part of solving a machine learning problem can be finding the right estimator for the job.
Different estimators are better suited for different types of data and different problems.
The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.
Source: Scikit-learn
Different estimators are better suited for different types of data and different problems.
The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.
Source: Scikit-learn
This media is not supported in your browser
VIEW IN TELEGRAM
DIVE INTO DEEP LEARNING ||d2l.ai
Here's an Interactive deep learning book with code, math, and discussions.
Implemented with PyTorch, NumPy/MXNet, and TensorFlow.
Book Link : https://d2l.ai/
GitHub Repo: https://github.com/d2l-ai/d2l-en
Stars: 15.7K
Forks:3.4K
#deep_learning #pyTorch #numPy #MXNet #TensorFlow #neural_networks
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Here's an Interactive deep learning book with code, math, and discussions.
Implemented with PyTorch, NumPy/MXNet, and TensorFlow.
Book Link : https://d2l.ai/
GitHub Repo: https://github.com/d2l-ai/d2l-en
Stars: 15.7K
Forks:3.4K
#deep_learning #pyTorch #numPy #MXNet #TensorFlow #neural_networks
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Hey folks, this week's round of our programming quiz league is about data science. Here is the quiz link:
http://www.tg-me.com/QuizBot?start=H4Ow9sU8
Feel free to answer on those 8 short questions and let me know about your placement on final score.
Also to those who celebrate today I wish Merry Christmas 🎄🥳😊
http://www.tg-me.com/QuizBot?start=H4Ow9sU8
Feel free to answer on those 8 short questions and let me know about your placement on final score.
Also to those who celebrate today I wish Merry Christmas 🎄🥳😊
Quiz Directory
Data Science Quiz
8 questions
NOC: Reinforcement Learning, IIT Madras
🆓 Free Online Course
💻 65 Lecture Videos
⏰ 12 Modules
🏃♂️ Self paced
Teacher 👨🏫 : Dr. B. Ravindran
🔗 https://nptel.ac.in/courses/106106143
#Reinforcement_Learning #IIT
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
🆓 Free Online Course
💻 65 Lecture Videos
⏰ 12 Modules
🏃♂️ Self paced
Teacher 👨🏫 : Dr. B. Ravindran
🔗 https://nptel.ac.in/courses/106106143
#Reinforcement_Learning #IIT
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
Applications of Deep Neural Networks
Washington University in St. Louis
https://sites.wustl.edu/jeffheaton/t81-558/
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Washington University in St. Louis
https://sites.wustl.edu/jeffheaton/t81-558/
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
sites.wustl.edu
T81-558:Applications of Deep Neural Networks | Jeff Heaton | Washington University in St. Louis
Course Description Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques a...
Free Data Mining Courses
NOC:Data Mining, IIT Kharagpur
🎬 44 video lesson
⏰ 8 Modules
Taught by: Prof. Pabitra Mitra
Source: NPTEL
🔗 COURSE LINK
Data Mining for Beginners | Data Mining Full course | Learn Data Mining in 10 Hours | Great Learning
🎬 17 video lesson
Duration ⏰ : 10 hours worth of material
🏃♂️ Self paced
Source: Great Learning
🔗 COURSE LINK
NOC:Business analytics and data mining Modeling using R, IIT Roorkee
🎬 56 video lesson
⏰ 12 Modules
🔗 COURSE LINK
NOC:Business Analytics & Data Mining Modeling Using R Part II, IIT Roorkee
🎬 20 video lesson
⏰ 4 Modules
🔗 COURSE LINK
Taught by: Dr. Gaurav Dixit
Source: NPTEL
Data Mining with Weka MOOC
✅ Free Online Course
🧱 5 modules
🎬 Video Lectures
🏃♂️ Self paced
Source: University of Waikato
Taught by: Ian H. Witten
🔗 Course Link
WEKA - Data Mining with Open Source Machine Learning Tool
Rating⭐️: 4.2 out 5
Students 👨🎓 : 12,485`
Duration ⏰ : 3hr 30min of on-demand video
Teacher 👨🏫: DATAhill Solutions Srinivas Reddy
🔗 COURSE LINK
Data Mining Crash Course
🎬 6 video lesson
Duration ⏰: 1-2 hours worth of material
🏃♂️ Self paced
Source: Data Science Dojo
🔗 Course Link
Clustering in Data mining | K means Clustering Algorithm | Hierarchical Clustering | Great Learning
🎬 86 video lesson
Duration ⏰: 3-4 hours worth of material
🏃♂️ Self paced
Source: Great Learning
🔗 Course Link
Mining Online Data Across Social Networks
⏰ Free Online Course
🎬 30 video lesson
Duration ⏰: 1-2 hours worth of material
🏃♂️ Self paced
Source: Class Central
🔗 Course Link
DATA MINING (DM)
⏰ Free Online Course
🏃♂️ Self paced
Source: YouTube
🔗 Course Link
#Data_Mining
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
NOC:Data Mining, IIT Kharagpur
🎬 44 video lesson
⏰ 8 Modules
Taught by: Prof. Pabitra Mitra
Source: NPTEL
🔗 COURSE LINK
Data Mining for Beginners | Data Mining Full course | Learn Data Mining in 10 Hours | Great Learning
🎬 17 video lesson
Duration ⏰ : 10 hours worth of material
🏃♂️ Self paced
Source: Great Learning
🔗 COURSE LINK
NOC:Business analytics and data mining Modeling using R, IIT Roorkee
🎬 56 video lesson
⏰ 12 Modules
🔗 COURSE LINK
NOC:Business Analytics & Data Mining Modeling Using R Part II, IIT Roorkee
🎬 20 video lesson
⏰ 4 Modules
🔗 COURSE LINK
Taught by: Dr. Gaurav Dixit
Source: NPTEL
Data Mining with Weka MOOC
✅ Free Online Course
🧱 5 modules
🎬 Video Lectures
🏃♂️ Self paced
Source: University of Waikato
Taught by: Ian H. Witten
🔗 Course Link
WEKA - Data Mining with Open Source Machine Learning Tool
Rating⭐️: 4.2 out 5
Students 👨🎓 : 12,485`
Duration ⏰ : 3hr 30min of on-demand video
Teacher 👨🏫: DATAhill Solutions Srinivas Reddy
🔗 COURSE LINK
Data Mining Crash Course
🎬 6 video lesson
Duration ⏰: 1-2 hours worth of material
🏃♂️ Self paced
Source: Data Science Dojo
🔗 Course Link
Clustering in Data mining | K means Clustering Algorithm | Hierarchical Clustering | Great Learning
🎬 86 video lesson
Duration ⏰: 3-4 hours worth of material
🏃♂️ Self paced
Source: Great Learning
🔗 Course Link
Mining Online Data Across Social Networks
⏰ Free Online Course
🎬 30 video lesson
Duration ⏰: 1-2 hours worth of material
🏃♂️ Self paced
Source: Class Central
🔗 Course Link
DATA MINING (DM)
⏰ Free Online Course
🏃♂️ Self paced
Source: YouTube
🔗 Course Link
#Data_Mining
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
Free Big Data Courses
Complete Big Data
🎬 13 video lesson
Duration ⏰: 2-3 hours worth of material
🏃♂️ Self paced
Source: Class Central
🔗 COURSE LINK
Big Data 101 (Login Required)
⏳Modules: 6
Duration ⏰: 3 hours worth of material
🏃♂️ Self paced
Source: IBM via Cognitive Class
🔗 COURSE LINK
Introduction to Big Data - an overview of the 10 V's
Rating⭐️: 4.4 out 5
Students 👨🎓 :15,630
Duration ⏰ : 40min of on-demand video
Teacher 👨🏫: Taimur Zahid
🔗 COURSE LINK
MIT RES.LL-005 Mathematics of Big Data and Machine Learning, IAP 2020
🎬 20 video lesson
Duration ⏰: 14 hours worth of material
🏃♂️ Self paced
Source: MIT open courseware
🔗 Course Link
NOC:Big Data Computing, IIT Patna
🎬 35 video lesson
⏰ 8 Modules
Taught by: Dr. Rajiv Misra
Source: NPTEL
🔗 COURSE LINK
Algorithms for Big Data (COMPSCI 229r)
🎬 25 video lesson
Duration ⏰: 34 hours worth of material
🏃♂️ Self paced
Source: Harvard University
🔗 Course Link
NOC:Algorithms for Big Data, IIT Madras
🎬 48 video lesson
⏰ 8 Modules
Taught by: Prof. John Augustine
Source: NPTEL
🔗 COURSE LINK
Big Data Hadoop Tutorial for Beginners
🎬 17 video lesson
Duration ⏰: 4-5 hours worth of material
🏃♂️ Self paced
Source: Great Learning
🔗 Course Link
Big Data Analytics Full Course In 10 Hours | Big Data Hadoop Tutorial
🎬 5 video lesson
Duration ⏰: 10 hours worth of material
🏃♂️ Self paced
Source: Great Learning
🔗 Course Link
Big Data Analytics
⏰ Free Online Course
🎬 70 video lesson
Duration ⏰: 19 hours worth of material
🏃♂️ Self paced
Source: caltech via youtube
🔗 Course Link
Stanford Seminar - Big Data is (at least) Four Different Problems
⏰ Free Online Course
🎬 27 video lesson
Duration ⏰: 1-2 hours worth of material
🏃♂️ Self paced
Source: Stanford Online via YouTube
🔗 Course Link
#Big_Data
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
Complete Big Data
🎬 13 video lesson
Duration ⏰: 2-3 hours worth of material
🏃♂️ Self paced
Source: Class Central
🔗 COURSE LINK
Big Data 101 (Login Required)
⏳Modules: 6
Duration ⏰: 3 hours worth of material
🏃♂️ Self paced
Source: IBM via Cognitive Class
🔗 COURSE LINK
Introduction to Big Data - an overview of the 10 V's
Rating⭐️: 4.4 out 5
Students 👨🎓 :15,630
Duration ⏰ : 40min of on-demand video
Teacher 👨🏫: Taimur Zahid
🔗 COURSE LINK
MIT RES.LL-005 Mathematics of Big Data and Machine Learning, IAP 2020
🎬 20 video lesson
Duration ⏰: 14 hours worth of material
🏃♂️ Self paced
Source: MIT open courseware
🔗 Course Link
NOC:Big Data Computing, IIT Patna
🎬 35 video lesson
⏰ 8 Modules
Taught by: Dr. Rajiv Misra
Source: NPTEL
🔗 COURSE LINK
Algorithms for Big Data (COMPSCI 229r)
🎬 25 video lesson
Duration ⏰: 34 hours worth of material
🏃♂️ Self paced
Source: Harvard University
🔗 Course Link
NOC:Algorithms for Big Data, IIT Madras
🎬 48 video lesson
⏰ 8 Modules
Taught by: Prof. John Augustine
Source: NPTEL
🔗 COURSE LINK
Big Data Hadoop Tutorial for Beginners
🎬 17 video lesson
Duration ⏰: 4-5 hours worth of material
🏃♂️ Self paced
Source: Great Learning
🔗 Course Link
Big Data Analytics Full Course In 10 Hours | Big Data Hadoop Tutorial
🎬 5 video lesson
Duration ⏰: 10 hours worth of material
🏃♂️ Self paced
Source: Great Learning
🔗 Course Link
Big Data Analytics
⏰ Free Online Course
🎬 70 video lesson
Duration ⏰: 19 hours worth of material
🏃♂️ Self paced
Source: caltech via youtube
🔗 Course Link
Stanford Seminar - Big Data is (at least) Four Different Problems
⏰ Free Online Course
🎬 27 video lesson
Duration ⏰: 1-2 hours worth of material
🏃♂️ Self paced
Source: Stanford Online via YouTube
🔗 Course Link
#Big_Data
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
Data Science and Machine Learning [PDF]
Mathematical and Statistical Methods
Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman
8th May 2022
533 pages
🔗 Read online
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Mathematical and Statistical Methods
Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman
8th May 2022
533 pages
🔗 Read online
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group