I am in class 9 .Can u tell me which will be good for me?
Cool dude, thanks
Nice books
Tnx
Michio Kaku to sahi khel gaye
Only the alpha's are here damn
Thanks sir your recommend books are just amazing
Thanku for making these video
Sab Hindi main Hain
Thanks a lot Bhaiya its really very knowledgeable and it helps me a lot
Bhaiya how can I get these books
Sir I am beginner please tell me which book should I read
Amazing
Hi , I'm studying intermediate 1st year and I'm a beginner, which book I need to read to understand about space science most. Plz recommend a book for me
Ma class 7 ma perta hu Kiya ma in books ko samaj paunga
Is this books fusible for 8th calss student
My dream astrophysics
NICE ,
Nagpur where?
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Data Science Masters Certification Curriculum, Visit our Website: (Use Code "")Subscribe to Edureka YouTube channel for latest video updates: so much
Can i please get the datasets used in this course
How can I get the answers to the Bayes theorem example?
Hey Hi, Your course is so helpful. Could you please share the notes or datasheets to me?
Can you please share the datasets and notebook
Thank you so much for this, leaning a lot. Please how can I get the datasheet?. Will love to.
00:00 Agenda2:44 Introduction to Data Science9:55 Data Analysis at Walmart13:20 What is Data Science?14:39 Who is a Data Scientist?16:50 Data Science Skill Set21:51 Data Science Job Roles26:58 Data Life Cycle30:25 Statistics & Probability34:31 Categories of Data34:50 Qualitative Data36:09 Quantitative Data39:11 What is Statistics?41:32 Basic Terminologies in Statistics42:50 Sampling Techniques45:31 Random Sampling46:20 Systematic Sampling46:50 Stratified Sampling47:54 Types of Statistics50:38 Descriptive Statistics55:52 Measures of Spread55:56 Range56:44 Inter Quartile Range58:58 Variance59:36 Standard Deviation1:14:25 Confusion Matrix1:19:16 Probability1:24:14 What is Probability?1:27:13 Types of Events1:27:58 Probability Distribution1:28:15 Probability Density Function1:30:02 Normal Distribution1:30:51 Standard Deviation & Curve1:31:19 Central Limit Theorem1:33:12 Types of Probablity1:33:34 Marginal Probablity1:34:06 Joint Probablity1:34:58 Conditional Probablity1:35:56 Use-Case1:39:46 Bayes Theorem1:45:44 Inferential Statistics1:56:40 Hypothesis Testing2:00:34 Basics of Machine Learning2:01:41 Need for Machine Learning2:07:03 What is Machine Learning?2:09:21 Machine Learning Definitions2:!1:48 Machine Learning Process2:18:31 Supervised Learning Algorithm2:19:54 What is Regression?2:21:23 Linear vs Logistic Regression2:33:51 Linear Regression2:25:27 Where is Linear Regression used?2:27:11 Understanding Linear Regression2:37:00 What is R-Square?2:46:35 Logistic Regression2:51:22 Logistic Regression Curve2:53:02 Logistic Regression Equation2:56:21 Logistic Regression Use-Cases2:58:23 Demo3:00:57 Implement Logistic Regression3:02:33 Import Libraries3:05:28 Analyzing Data3:11:52 Data Wrangling3:23:54 Train & Test Data3:20:44 Implement Logistic Regression3:31:04 SUV Data Analysis3:38:44 Decision Trees3:39:50 What is Classification?3:42:27 Types of Classification3:42:27 Decision Tree3:43:51 Random Forest3:45:06 Naive Bayes3:47:12 KNN3:49:02 What is Decision Tree?3:55:15 Decision Tree Terminologies3:56:51 CART Algorithm3:58:50 Entropy4:00:15 What is Entropy?4:23:52 Random Forest4:27:29 Types of Classifier4:31:17 Why Random Forest?4:39:14 What is Random Forest?4:51:26 How Random Forest Works?4:51:36 Random Forest Algorithm5:04:23 K Nearest Neighbour5:05:33 What is KNN Algorithm?5:08:50 KNN Algorithm Working5:14:55 kNN Example5:24:30 What is Naive Bayes?5:25:13 Bayes Theorem5:27:48 Bayes Theorem Proof5:29:43 Naive Bayes Working5:39:06 Types of Naive Bayes5:53:37 Support Vector Machine5:57:40 What is SVM?5:59:46 How does SVM work?6:03:00 Introduction to Non-Linear SVM6:04:48 SVM Example6:06:12 Unsupervised Learning Algorithms - KMeans6:06:18 What is Unsupervised Learning?6:06:45 Unsupervised Learning: Process Flow6:07:17 What is Clustering?6:09:15 Types of Clustering6:10:15 K-Means Clustering6:10:40 K-Means Algorithm Working6:16:17 K-Means Algorithm6:19:16 Fuzzy C-Means Clustering6:21:22 Hierarchical Clustering6:22:53 Association Clustering6:24:57 Association Rule Mining6:30:35 Apriori Algorithm6:37:45 Apriori Demo6:40:49 What is Reinforcement Learning?6:42:48 Reinforcement Learning Process6:51:10 Markov Decision Process6:54:53 Understanding Q - Learning7:13:12 Q-Learning Demo7:25:34 The Bellman Equation7:48:39 What is Deep Learning?7:52:53 Why we need Artificial Neuron?7:54:33 Perceptron Learning Algorithm7:57:57 Activation Function8:03:14 Single Layer Perceptron8:04:04 What is Tensorflow?8:07:25 Demo8:21:03 What is a Computational Graph?8:49:18 Limitations of Single Layer Perceptron8:50:08 Multi-Layer Perceptron8:51:24 What is Backpropagation?8:52:26 Backpropagation Learning Algorithm8:59:31 Multi-layer Perceptron Demo9:01:23 Data Science Interview Questions
how do I get data for which source?
Thank you edureka for explaining each concept so nicely. Would it be possible for you to share the pdf or any document of the course in order to brush up the concepts quickly
Where can i find ppt of this tutorial ?
Mam please provide this ppt
Heyy, Can commerce student do this course? If Anyone has any idea please share
Can we get whole course pdf?
Does anyone know where I can find the Jupyter Notebook code files for this youtube video ? I really enjoyed seeing the code but if i had the file I could practice a bit more on my own.
i am a commerecde student and want to learn data science skill for mt Mba will it be helpful or should I go in depth for more ?
2:44 Introduction to Data Science9:55 Data Analysis at Walmart13:20 What is Data Science?14:39 Who is a Data Scientist?16:50 Data Science Skill Set21:51 Data Science Job Roles26:58 Data Life Cycle30:25 Statistics & Probability34:31 Categories of Data34:50 Qualitative Data36:09 Quantitative Data39:11 What is Statistics?41:32 Basic Terminologies in Statistics42:50 Sampling Techniques45:31 Random Sampling46:20 Systematic Sampling46:50 Stratified Sampling47:54 Types of Statistics50:38 Descriptive Statistics55:52 Measures of Spread55:56 Range56:44 Inter Quartile Range58:58 Variance59:36 Standard Deviation1:14:25 Confusion Matrix1:19:16 Probability1:24:14 What is Probability?1:27:13 Types of Events1:27:58 Probability Distribution1:28:15 Probability Density Function1:30:02 Normal Distribution1:30:51 Standard Deviation & Curve1:31:19 Central Limit Theorem1:33:12 Types of Probablity1:33:34 Marginal Probablity1:34:06 Joint Probablity1:34:58 Conditional Probablity1:35:56 Use-Case1:39:46 Bayes Theorem1:45:44 Inferential Statistics1:56:40 Hypothesis Testing2:00:34 Basics of Machine Learning2:01:41 Need for Machine Learning2:07:03 What is Machine Learning?2:09:21 Machine Learning Definitions2:11:48 Machine Learning Process2:18:31 Supervised Learning Algorithm2:19:54 What is Regression?2:21:23 Linear vs Logistic Regression2:33:51 Linear Regression2:25:27 Where is Linear Regression used?2:27:11 Understanding Linear Regression2:37:00 What is R-Square?2:46:35 Logistic Regression2:51:22 Logistic Regression Curve2:53:02 Logistic Regression Equation2:56:21 Logistic Regression Use-Cases2:58:23 Demo3:00:57 Implement Logistic Regression3:02:33 Import Libraries3:05:28 Analyzing Data3:11:52 Data Wrangling3:23:54 Train & Test Data3:20:44 Implement Logistic Regression3:31:04 SUV Data Analysis3:38:44 Decision Trees3:39:50 What is Classification?3:42:27 Types of Classification3:42:27 Decision Tree3:43:51 Random Forest3:45:06 Naive Bayes3:47:12 KNN3:49:02 What is Decision Tree?3:55:15 Decision Tree Terminologies3:56:51 CART Algorithm3:58:50 Entropy4:00:15 What is Entropy?4:23:52 Random Forest4:27:29 Types of Classifier4:31:17 Why Random Forest?4:39:14 What is Random Forest?4:51:26 How Random Forest Works?4:51:36 Random Forest Algorithm5:04:23 K Nearest Neighbour5:05:33 What is KNN Algorithm?5:08:50 KNN Algorithm Working5:14:55 kNN Example5:24:30 What is Naive Bayes?5:25:13 Bayes Theorem5:27:48 Bayes Theorem Proof5:29:43 Naive Bayes Working5:39:06 Types of Naive Bayes5:53:37 Support Vector Machine5:57:40 What is SVM?5:59:46 How does SVM work?6:03:00 Introduction to Non-Linear SVM6:04:48 SVM Example6:06:12 Unsupervised Learning Algorithms - KMeans6:06:18 What is Unsupervised Learning?6:06:45 Unsupervised Learning: Process Flow6:07:17 What is Clustering?6:09:15 Types of Clustering6:10:15 K-Means Clustering6:10:40 K-Means Algorithm Working6:16:17 K-Means Algorithm6:19:16 Fuzzy C-Means Clustering6:21:22 Hierarchical Clustering6:22:53 Association Clustering6:24:57 Association Rule Mining6:30:35 Apriori Algorithm6:37:45 Apriori Demo6:40:49 What is Reinforcement Learning?6:42:48 Reinforcement Learning Process6:51:10 Markov Decision Process6:54:53 Understanding Q - Learning7:13:12 Q-Learning Demo7:25:34 The Bellman Equation7:48:39 What is Deep Learning?7:52:53 Why we need Artificial Neuron?7:54:33 Perceptron Learning Algorithm7:57:57 Activation Function8:03:14 Single Layer Perceptron8:04:04 What is Tensorflow?8:07:25 Demo8:21:03 What is a Computational Graph?8:49:18 Limitations of Single Layer Perceptron8:50:08 Multi-Layer Perceptron8:51:24 What is Backpropagation?8:52:26 Backpropagation Learning Algorithm8:59:31 Multi-layer Perceptron Demo9:01:23 Data Science Interview Questions
Thanks for such a rich content on DataScience... Can you please share datasets with me used in video tutorial!!
Great course. Well done!
Thank you Edureka for this wonderful course. From where can we get the datasets used in this course? It would be great if you can share
this is so awesome! Because once "full", we can build an AI to search for repeated inputs or such, and eventuallyt put events like "whoever can input something successfully into the library, gains scientific attention and a review". And this is a bridge for society and maths as a possibility for anyone to add to the field :O
4:55
What if! Computers can use mathematics to write their own algorithm? It would be first real artificial intelligence. Now we are feeding knowledge to the computer like a new born baby. But soon there will be a time when computer can feed itself. May be then time machine will be available to humans.
A problem is that the title of this video is based on a lie.I don't believe anybody can know what "the biggest breakthoughs in mathematics" are.It would be more correct to title this: "Some of 2020's Biggest Breakthroughs in Math and Computer Science".
Is it just me, or is Kevin buzzard just bald Capaldi?
The universe is a computer, but the universe is not a simulation. It's digital, but nobody made it.
Frvvcvccccv RF o filme do 5-6-7 u
UNITED STATE'S GOVERNMENT MARINE'S MILITARY AIR FORCES U.S.S MY FAMILY IS UNITED STATE'S GOVERNMENT MILITARY U.S.S SINCE 1940'S I AM A WHITE WOMAN TALL 5'9 130LBS LONG RED/BLOND HAIR MY IQ IS EXACTLY HIGH TO SAVE THE PLANET EARTH
#2 was so triggering to the sapiophile in me.
"Mathematics has the completely false reputation of yielding infallible conclusions. Its infallibility is nothing but identity. Two times two is not four, but it is just two times two, and that is what we call four for short. But four is nothing new at all. And thus it goes on and on in its conclusions, except that in the higher formulas the identity fades out of sight." (Johann Wolfgang Von Goethe)
To transcend and translate the world we must abandon theory; things like Mathematics and physics, etc.
Sorry, guys, that doesn't sound like "science" at all. Obviously those guys never wrote a single working piece of software in their lives. Infinite loops do happen; they are always just the results of errors made by the programmer. And, it is always possible to prevent infinite loops by just introducing a variable that would count the number of times the code within the loop is "run", and comparing that variable with a particular limit, be it a million or a billion, etc. The program in such case may stop with a message "too many iterations" (or smth. like that), but that way the "infinity" never happens.
1:08 has a purple v
Pretty much got this vid shoved in my face every refresh
There is a verse in Holy Quran Chapter 59 Al-Hashr (The Exile) verse 23 with most number of God's names (Attributes) i.e. 8 names.He is Allah, other than whom there is no deity, 1 the Sovereign,2 the Pure, 3 the Perfection, 4 the Grantor of Security, 5 the Overseer, 6 the Exalted in Might, 7 the Compeller, 8 the Superior. Exalted is Allah above whatever they associate with Him.On the above verse we have lot of prime number facts as listed below:1) 59 is a prime number2) 23 is a prime number3) This verse has 19 words in Arabic 19 is another prime number.4) 8th prime number is 195) Concatenate 59, 23 we get 5923 a prime number6) Sum of the digits 5+9+2+3 = 19 a prime number7) Add 8 to 23 we get 31 a prime number.8. Add 8 to 59 we get 67 another prime number.9) Concatenate 8 with 23 we get 823 a prime number.10) Concatenate 8 with 59 we get 859 a prime number.11) Add 59, 23 and 19 we get 101 another prime number.12) Add 58, 23, 19 and 8 we get 109 another prime number.13) Sum of first 8 numbers is 8*9/2=36. See this matches with (surah no-verse no) 59-23=36.
Holy Quran Chapter 60 Pattern (60-13-114-6236). This is only possible because of 13 verses of chapter 60.Total Chapters of Holy Quran (114) Calculation:Chapter No 60 + No of verses in chapter 60, 13 + 13th Prime Number 41 = 60+13+41 = 114Total Verses of Holy Quran (6236) Calculation:Total Number of Verses in Holy Quran (6236) = 7699 (Sum of the first 60 prime numbers)- 1390 (Sum of the non prime numbers under 60)- 73 (60 + 13 verses of 60th chapter)Holy Quran Chapter 96 Al-Alaq (19 Verses) (The Clinging Thing) PatternTotal Verses of Holy Quran (6236) Calculation:Total Number of Verses in Holy Quran (6236) = 6535 Sum of the first 96 composite Numbers- 96 (Chapter Number and it has 19 Verses)- 30 (19th Composite Number)- 67 (19th Prime Number)- 98 (19th Chapter Mary Verses)- 8 (8th Prime Number 19)Holy Quran Chapter 19 Mary (98 Verses) PatternTotal Verses of Holy Quran (6236) Calculation:Total Number of Verses in Holy Quran (6236) = 6794 (Sum of the first 98 composite Numbers)- 521 (98th Prime Number)- 67 (19th Prime Number)+ 30 (19th Composite Number)Holy Quran Chapter 55 Ar-Rahman (The Beneficent) (78 Verses with 31 repeated) PatternTotal Number of Verses in Holy Quran (6236) = 6338 (Sum of the first 55 Prime Numbers)- 55 (Chapter Number)- 78 (Total Verses)+ 31 (Repeated Verses)Holy Quran Chapter 18 Al-Kahf (110 Verses) PatternTotal Number of Verses in Holy Quran (6236) = 6105 (Sum of the first 100 Numbers)+ 171 (Sum of the first 18 numbers)+ 28 (18th Composite Number)+ 10 (10th Composite Number 18)- 78 (Sum of first 12 Numbers (12 Rukus in this Chapter))
Just cut the string and unwind it. No more knot.
The police have aversion for truth. Maybe next year kid.
Do you want to talk/learn about God and Jesus? God and Jesus both love you and can help you with whatever you may be going through!
Quantum Entangled Twisted Tubules:When we draw a sine wave on a blackboard, we are representing spatial curvature. Does a photon transfer spatial curvature from one location to another? Wrap a piece of wire around a pencil and it can produce a 3D coil of wire, much like a spring. When viewed from the side it can look like a two-dimensional sine wave. You could coil the wire with either a right-hand twist, or with a left-hand twist. Could Planck's Constant be proportional to the twist cycles. A photon with a higher frequency has more energy. (More spatial curvature). What if gluons are actually made up of these twisted tubes which become entangled with other tubes to produce quarks. (In the same way twisted electrical extension cords can become entangled.) Therefore, the gluons are actually a part of the quarks. Mesons are made up of two entangled tubes (Quarks/Gluons), while protons and neutrons would be made up of three entangled tubes. (Quarks/Gluons) The "Color Force" would be related to the XYZ coordinates (orientation) of entanglement. "Asymptotic Freedom", and "flux tubes" make sense based on this concept. Neutrinos would be made up of a twisted torus (like a twisted donut) within this model. Gravity is a result of a very small curvature imbalance within atoms. (This is why the force of gravity is so small.) Instead of attempting to explain matter as "particles", this concept attempts to explain matter more in the manner of our current understanding of the space-time curvature of gravity. If an electron has qualities of both a particle and a wave, it cannot be either one. It must be something else. Therefore, a "particle" is actually a structure which stores spatial curvature. Can an electron-positron pair (which are made up of opposite directions of twist) annihilate each other by unwinding into each other producing Gamma Ray photons.Does an electron travel through space like a threaded nut traveling down a threaded rod, with each twist cycle proportional to Plancks Constant? Does it wind up on one end, while unwinding on the other end? Is this related to the Higgs field? Does this help explain the strange spin of many subatomic particles? Does the 720 degree rotation of a 1/2 spin particle require at least one extra dimension?Alpha decay occurs when the two protons and two neutrons (which are bound together by entangled tubes), become un-entangled from the rest of the nucleons. Beta decay occurs when the tube of a down quark/gluon in a neutron becomes overtwisted and breaks producing a twisted torus (neutrino) and an up quark, and the ejected electron. The phenomenon of Supercoiling involving twist and writhe cycles may reveal how overtwisted quarks can produce these new particles. The conversion of twists into writhes, and vice-versa, is an interesting process.Gamma photons are produced when a tube unwinds producing electromagnetic waves.