| Формат | Размер | Скачать |
|---|
| Название | : | |
| Продолжительность | : | |
| Пользователь | : | id 910305469717 |
| Дата публикации | : | ript nonce= |
| Просмотры | : | 9ABloU8c3tFZfQAekw |
| Понравилось | : | 1,306 |
| Не понравилось | : | 22 |
 | 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: bit.ly/3sw3tJj (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") Комментарий от : edureka! |
 | its great but could you provide the pdf of this Комментарий от : Atreya Raghuvanshi |
 | its a very great course. Could I please request you to provide me the slides used in the video? Комментарий от : parnika kansal |
 | Thank You so much 👌🏻👍🏻
Edureka !! This was amazing Комментарий от : Oishik Sinha |
 | Best video lectures available right now. Can you plz tell how can i get the slides of the series?? Комментарий от : Yashasvi Mahajan |
 | Amazing work, very professional. Can I please get these data sets used? Комментарий от : hamid awan |
 | Awesome Комментарий от : Rajesh Natarajan |
 | Do you provide certificate as well? by taking test on this? Комментарий от : Anuradha Tarpe |
 | Can we please get the dataset files you guys used so that we can follow along ? Комментарий от : TheAbdou27 |
 | Yasss thankyou I find this video no ads Комментарий от : Sahlul Furqon |
 | Very informative and helpful video , could you please tell me where i can obtain the jupyter notebooks used in the videos form Комментарий от : BASHARRAT HARIS |
 | Excellent excellent excellent excellent excellent!! Keep excelling Комментарий от : Anil Roy Dubey |
 | Could you plz send this course slides at gmail: waqasirfanasad@gmail.com. Комментарий от : PERSONALITY GROWTH |
| Could you plz share your presentation of this cousre thnks 😊 email : waqasirfanasad@gmail.com Комментарий от : PERSONALITY GROWTH |
 | Great work, shall you send me the presented material odd or ppt please my email is ahmedfahmyaee@yahoo.com Комментарий от : Ahmed Fahmy |
 | 00:00 Agenda
2:44 Introduction to Data Science 9:55 Data Analysis at Walmart 13:20 What is Data Science? 14:39 Who is a Data Scientist? 16:50 Data Science Skill Set 21:51 Data Science Job Roles 26:58 Data Life Cycle 30:25 Statistics & Probability 34:31 Categories of Data 34:50 Qualitative Data 36:09 Quantitative Data 39:11 What is Statistics? 41:32 Basic Terminologies in Statistics 42:50 Sampling Techniques 45:31 Random Sampling 46:20 Systematic Sampling 46:50 Stratified Sampling 47:54 Types of Statistics 50:38 Descriptive Statistics 55:52 Measures of Spread 55:56 Range 56:44 Inter Quartile Range 58:58 Variance 59:36 Standard Deviation 1:14:25 Confusion Matrix 1:19:16 Probability 1:24:14 What is Probability? 1:27:13 Types of Events 1:27:58 Probability Distribution 1:28:15 Probability Density Function 1:30:02 Normal Distribution 1:30:51 Standard Deviation & Curve 1:31:19 Central Limit Theorem 1:33:12 Types of Probablity 1:33:34 Marginal Probablity 1:34:06 Joint Probablity 1:34:58 Conditional Probablity 1:35:56 Use-Case 1:39:46 Bayes Theorem 1:45:44 Inferential Statistics 1:56:40 Hypothesis Testing 2:00:34 Basics of Machine Learning 2:01:41 Need for Machine Learning 2:07:03 What is Machine Learning? 2:09:21 Machine Learning Definitions 2:!1:48 Machine Learning Process 2:18:31 Supervised Learning Algorithm 2:19:54 What is Regression? 2:21:23 Linear vs Logistic Regression 2:33:51 Linear Regression 2:25:27 Where is Linear Regression used? 2:27:11 Understanding Linear Regression 2:37:00 What is R-Square? 2:46:35 Logistic Regression 2:51:22 Logistic Regression Curve 2:53:02 Logistic Regression Equation 2:56:21 Logistic Regression Use-Cases 2:58:23 Demo 3:00:57 Implement Logistic Regression 3:02:33 Import Libraries 3:05:28 Analyzing Data 3:11:52 Data Wrangling 3:23:54 Train & Test Data 3:20:44 Implement Logistic Regression 3:31:04 SUV Data Analysis 3:38:44 Decision Trees 3:39:50 What is Classification? 3:42:27 Types of Classification 3:42:27 Decision Tree 3:43:51 Random Forest 3:45:06 Naive Bayes 3:47:12 KNN 3:49:02 What is Decision Tree? 3:55:15 Decision Tree Terminologies 3:56:51 CART Algorithm 3:58:50 Entropy 4:00:15 What is Entropy? 4:23:52 Random Forest 4:27:29 Types of Classifier 4:31:17 Why Random Forest? 4:39:14 What is Random Forest? 4:51:26 How Random Forest Works? 4:51:36 Random Forest Algorithm 5:04:23 K Nearest Neighbour 5:05:33 What is KNN Algorithm? 5:08:50 KNN Algorithm Working 5:14:55 kNN Example 5:24:30 What is Naive Bayes? 5:25:13 Bayes Theorem 5:27:48 Bayes Theorem Proof 5:29:43 Naive Bayes Working 5:39:06 Types of Naive Bayes 5:53:37 Support Vector Machine 5:57:40 What is SVM? 5:59:46 How does SVM work? 6:03:00 Introduction to Non-Linear SVM 6:04:48 SVM Example 6:06:12 Unsupervised Learning Algorithms - KMeans 6:06:18 What is Unsupervised Learning? 6:06:45 Unsupervised Learning: Process Flow 6:07:17 What is Clustering? 6:09:15 Types of Clustering 6:10:15 K-Means Clustering 6:10:40 K-Means Algorithm Working 6:16:17 K-Means Algorithm 6:19:16 Fuzzy C-Means Clustering 6:21:22 Hierarchical Clustering 6:22:53 Association Clustering 6:24:57 Association Rule Mining 6:30:35 Apriori Algorithm 6:37:45 Apriori Demo 6:40:49 What is Reinforcement Learning? 6:42:48 Reinforcement Learning Process 6:51:10 Markov Decision Process 6:54:53 Understanding Q - Learning 7:13:12 Q-Learning Demo 7:25:34 The Bellman Equation 7:48:39 What is Deep Learning? 7:52:53 Why we need Artificial Neuron? 7:54:33 Perceptron Learning Algorithm 7:57:57 Activation Function 8:03:14 Single Layer Perceptron 8:04:04 What is Tensorflow? 8:07:25 Demo 8:21:03 What is a Computational Graph? 8:49:18 Limitations of Single Layer Perceptron 8:50:08 Multi-Layer Perceptron 8:51:24 What is Backpropagation? 8:52:26 Backpropagation Learning Algorithm 8:59:31 Multi-layer Perceptron Demo 9:01:23 Data Science Interview Questions Комментарий от : Data |
 | is this enough?? Комментарий от : supriya singh |
 | Hi I am not related to any of the branches neither have degree or certification of any kind still I find it interesting and want to learn it as these are the skills of future Can anyone please suggest if I have persued BA Maths then what should I do Комментарий от : Satyendra |
 | Thank you so much..... Комментарий от : Deep's Learning Studio |
 | Is this course is sufficient for begginers Комментарий от : Shaik saqafee Talaha |
 | am civil engineer|| can i learned?? for data science engineer.?? Комментарий от : rameez khan |
 | could you please clear me the dataset at 1:11:00 of video, from where did you get 6 instances true & 8 instances false in the humidity dataset. I am getting 9 yes and 5 no. Please help me out. Комментарий от : Anish Apostate |
 | Thanks for sharing this information Edureka. Really Helpful Комментарий от : Croma Campus |
 | 👍👍👍👍👍👍👍 Комментарий от : Brainstorming & sharing |
 | Hello edureka team, I really loved your efforts to make such helpful content, now I want to know is that all a data scientist needs to know or this was just for beginners? Thanks alot anyway 😊 Комментарий от : Zawar Allahbuxbhatti |
 | Can I apply for job after completing this course ? Комментарий от : suchit patil |
 | You are best I'm not Комментарий от : Jai Singh |
 | you are providing course in hindi or english Комментарий от : Rakesh Yadav |
 | I want the data sets which you have used in this video, can you send me, please? Комментарий от : Rishav Pandit |
 | Please send me the datasets my email naiyarans@gmail.com Комментарий от : Alam Ansari |
 | thank you how I get document Комментарий от : Kassahu alebel |
 | Thank you very much for the course edureka. please can I get the slides used through my mail? my email id is aravind294a@gmail.com. thanks Комментарий от : Aruna Duvvuri |
 | Nice explanation , very easy to understand to the beginners also #thank you for providing absolutely free in youtube..#very helpful for the researchers Комментарий от : Malleswari malli |
 | Ans probability 11/17 Комментарий от : Alina Mirza |
 | Hi Edureka, can you pls share the dataset used in your course? Where can I send you my id? Thanks a lot! Комментарий от : Alan Ajanovic |
 | Learning Data science with python is good or without python?? Комментарий от : VINDARA |
 | 00:00 Agenda
2:44 Introduction to Data Science 9:55 Data Analysis at Walmart 13:20 What is Data Science? 14:39 Who is a Data Scientist? 16:50 Data Science Skill Set 21:51 Data Science Job Roles 26:58 Data Life Cycle 30:25 Statistics & Probability 34:31 Categories of Data 34:50 Qualitative Data 36:09 Quantitative Data 39:11 What is Statistics? 41:32 Basic Terminologies in Statistics 42:50 Sampling Techniques 45:31 Random Sampling 46:20 Systematic Sampling 46:50 Stratified Sampling 47:54 Types of Statistics 50:38 Descriptive Statistics 55:52 Measures of Spread 55:56 Range 56:44 Inter Quartile Range 58:58 Variance 59:36 Standard Deviation 1:14:25 Confusion Matrix 1:19:16 Probability 1:24:14 What is Probability? 1:27:13 Types of Events 1:27:58 Probability Distribution 1:28:15 Probability Density Function 1:30:02 Normal Distribution 1:30:51 Standard Deviation & Curve 1:31:19 Central Limit Theorem 1:33:12 Types of Probablity 1:33:34 Marginal Probablity 1:34:06 Joint Probablity 1:34:58 Conditional Probablity 1:35:56 Use-Case 1:39:46 Bayes Theorem 1:45:44 Inferential Statistics 1:56:40 Hypothesis Testing 2:00:34 Basics of Machine Learning 2:01:41 Need for Machine Learning 2:07:03 What is Machine Learning? 2:09:21 Machine Learning Definitions 2:!1:48 Machine Learning Process 2:18:31 Supervised Learning Algorithm 2:19:54 What is Regression? 2:21:23 Linear vs Logistic Regression 2:33:51 Linear Regression 2:25:27 Where is Linear Regression used? 2:27:11 Understanding Linear Regression 2:37:00 What is R-Square? 2:46:35 Logistic Regression 2:51:22 Logistic Regression Curve 2:53:02 Logistic Regression Equation 2:56:21 Logistic Regression Use-Cases 2:58:23 Demo 3:00:57 Implement Logistic Regression 3:02:33 Import Libraries 3:05:28 Analyzing Data 3:11:52 Data Wrangling 3:23:54 Train & Test Data 3:20:44 Implement Logistic Regression 3:31:04 SUV Data Analysis 3:38:44 Decision Trees 3:39:50 What is Classification? 3:42:27 Types of Classification 3:42:27 Decision Tree 3:43:51 Random Forest 3:45:06 Naive Bayes 3:47:12 KNN 3:49:02 What is Decision Tree? 3:55:15 Decision Tree Terminologies 3:56:51 CART Algorithm 3:58:50 Entropy 4:00:15 What is Entropy? 4:23:52 Random Forest 4:27:29 Types of Classifier 4:31:17 Why Random Forest? 4:39:14 What is Random Forest? 4:51:26 How Random Forest Works? 4:51:36 Random Forest Algorithm 5:04:23 K Nearest Neighbour 5:05:33 What is KNN Algorithm? 5:08:50 KNN Algorithm Working 5:14:55 kNN Example 5:24:30 What is Naive Bayes? 5:25:13 Bayes Theorem 5:27:48 Bayes Theorem Proof 5:29:43 Naive Bayes Working 5:39:06 Types of Naive Bayes 5:53:37 Support Vector Machine 5:57:40 What is SVM? 5:59:46 How does SVM work? 6:03:00 Introduction to Non-Linear SVM 6:04:48 SVM Example 6:06:12 Unsupervised Learning Algorithms - KMeans 6:06:18 What is Unsupervised Learning? 6:06:45 Unsupervised Learning: Process Flow 6:07:17 What is Clustering? 6:09:15 Types of Clustering 6:10:15 K-Means Clustering 6:10:40 K-Means Algorithm Working 6:16:17 K-Means Algorithm 6:19:16 Fuzzy C-Means Clustering 6:21:22 Hierarchical Clustering 6:22:53 Association Clustering 6:24:57 Association Rule Mining 6:30:35 Apriori Algorithm 6:37:45 Apriori Demo 6:40:49 What is Reinforcement Learning? 6:42:48 Reinforcement Learning Process 6:51:10 Markov Decision Process 6:54:53 Understanding Q - Learning 7:13:12 Q-Learning Demo 7:25:34 The Bellman Equation 7:48:39 What is Deep Learning? 7:52:53 Why we need Artificial Neuron? 7:54:33 Perceptron Learning Algorithm 7:57:57 Activation Function 8:03:14 Single Layer Perceptron 8:04:04 What is Tensorflow? 8:07:25 Demo 8:21:03 What is a Computational Graph? 8:49:18 Limitations of Single Layer Perceptron 8:50:08 Multi-Layer Perceptron 8:51:24 What is Backpropagation? 8:52:26 Backpropagation Learning Algorithm 8:59:31 Multi-layer Perceptron Demo 9:01:23 Data Science Interview Questions Комментарий от : TechCurious |
 | Great work...thank you very much Комментарий от : Samindi Godakanda |
 | It's really useful course ..thanks for providing it's for free Комментарий от : Thota Chandrika |
 | Humanity needed Edureka! ❤ Комментарий от : syed saba |
 | I think this course should be helpful for my academic education & carrier build up. ThAnK YoU😍 Note:I am a student at Department of Statistics BsC(Hons) Комментарий от : Sujaul Suvon |
 | I love her teaching style...I'm gonna finish this in 2 days, it's so good and easy to learn...wasted 6 months in college learning this and they taught in much detail in 10 hours... it's amazing. Комментарий от : SARTHAK JAISWAL |
 | can i get all the dataset Комментарий от : Ankit Nayal |
 | Thanks edureka Комментарий от : Pothuluru Veerareddy |
 | Bro if I learn this 10 hrs course, will I get a job in data science fielf Комментарий от : HRP Productions |
 | If I complete this 10 hour course and then want to get the certificate, is it possible? Комментарий от : Sharath Premnath |
 | can science student learn data science i dont have minimum computer knowledge also Комментарий от : Gouse chinnu |
 | Hi can you send me solution of examole which in the bayes theorem? Комментарий от : Nazrin Karimova |
 | Thank you very much for this great videos. Комментарий от : thasneema umer |
 | Sir,please share the decision tree algorithm.. Комментарий от : Akanksha_50 |
 | is it mandatory to have math in 12th or college level to be a data scientist? Комментарий от : Aditi Gupta |
 | Thanks a lot Edurika , I am in 9th for now and I am looking at data science as a career option . I have finished 10 minutes of the video and the topic really interested me , I will be finishing the video in 2 weeks and hopefully I get a fair idea of how Data science works . Again , thanks a lot 😄 Комментарий от : Mohit Kotta |
 | tq edureka it is very useful to all Комментарий от : Pandu Devarasetti |
 | SUPER EDUREKA Комментарий от : ARUN RAJ |
 | Wow, this is great Can I please get the datasets used in this course Комментарий от : Emmanuel Tettey Lawer |
 | Helpful or useful Amazing video thanku you so much edureka! team Комментарий от : mr srk entertainment |
 | Simply woww... Комментарий от : Puneet Tiwari |
 | Hello. Awesome video! Could you please share datasets with me? Thank you. Комментарий от : Mersiha Ćeranić |
 | U ppl are doing amazing job...keep it up... Комментарий от : Chitra Kalpesh Vasvani |
 | Awosome job edureka team Комментарий от : Prabu Venkatesan |
 | THankyouu for this course Комментарий от : Anubha Gupta |
 | 18:20 Комментарий от : Brandon cheng |
 | Datasets ? Комментарий от : saryuable |
 | Love u edureka! Really u just not made us understood concept but coding also. ❣️🙏 Комментарий от : Dishant Kumbhar |
 | may God bless you people . Комментарий от : Ghulam Mujtaba Adil |
| Guys this is really an awesome tutorial, you won't find out on another channel. Like if u r watching this video Thank you Edureka for making us understand complex thing in simplest manner. Комментарий от : Dishant Kumbhar |
 | 00:00 Agenda
2:44 Introduction to Data Science 9:55 Data Analysis at Walmart 13:20 What is Data Science? 14:39 Who is a Data Scientist? 16:50 Data Science Skill Set 21:51 Data Science Job Roles 26:58 Data Life Cycle 30:25 Statistics & Probability 34:31 Categories of Data 34:50 Qualitative Data 36:09 Quantitative Data 39:11 What is Statistics? 41:32 Basic Terminologies in Statistics 42:50 Sampling Techniques 45:31 Random Sampling 46:20 Systematic Sampling 46:50 Stratified Sampling 47:54 Types of Statistics 50:38 Descriptive Statistics 55:52 Measures of Spread 55:56 Range 56:44 Inter Quartile Range 58:58 Variance 59:36 Standard Deviation 1:14:25 Confusion Matrix 1:19:16 Probability 1:24:14 What is Probability? 1:27:13 Types of Events 1:27:58 Probability Distribution 1:28:15 Probability Density Function 1:30:02 Normal Distribution 1:30:51 Standard Deviation & Curve 1:31:19 Central Limit Theorem 1:33:12 Types of Probability 1:33:34 Marginal Probability 1:34:06 Joint Probability 1:34:58 Conditional Probability 1:35:56 Use-Case 1:39:46 Bayes Theorem 1:45:44 Inferential Statistics 1:56:40 Hypothesis Testing 2:00:34 Basics of Machine Learning 2:01:41 Need for Machine Learning 2:07:03 What is Machine Learning? 2:09:21 Machine Learning Definitions 2:11:48 Machine Learning Process 2:18:31 Supervised Learning Algorithm 2:19:54 What is Regression? 2:21:23 Linear vs Logistic Regression 2:33:51 Linear Regression 2:25:27 Where is Linear Regression used? 2:27:11 Understanding Linear Regression 2:37:00 What is R-Square? 2:46:35 Logistic Regression 2:51:22 Logistic Regression Curve 2:53:02 Logistic Regression Equation 2:56:21 Logistic Regression Use-Cases 2:58:23 Demo 3:00:57 Implement Logistic Regression 3:02:33 Import Libraries 3:05:28 Analyzing Data 3:11:52 Data Wrangling 3:23:54 Train & Test Data 3:20:44 Implement Logistic Regression 3:31:04 SUV Data Analysis 3:38:44 Decision Trees 3:39:50 What is Classification? 3:42:27 Types of Classification 3:42:27 Decision Tree 3:43:51 Random Forest 3:45:06 Naive Bayes 3:47:12 KNN 3:49:02 What is a Decision Tree? 3:55:15 Decision Tree Terminologies 3:56:51 CART Algorithm 3:58:50 Entropy 4:00:15 What is Entropy? 4:23:52 Random Forest 4:27:29 Types of Classifier 4:31:17 Why Random Forest? 4:39:14 What is Random Forest? 4:51:26 How Random Forest Works? 4:51:36 Random Forest Algorithm 5:04:23 K Nearest Neighbour 5:05:33 What is KNN Algorithm? 5:08:50 KNN Algorithm Working 5:14:55 kNN Example 5:24:30 What is Naive Bayes? 5:25:13 Bayes Theorem 5:27:48 Bayes Theorem Proof 5:29:43 Naive Bayes Working 5:39:06 Types of Naive Bayes 5:53:37 Support Vector Machine 5:57:40 What is SVM? 5:59:46 How does SVM work? 6:03:00 Introduction to Non-Linear SVM 6:04:48 SVM Example 6:06:12 Unsupervised Learning Algorithms - KMeans 6:06:18 What is Unsupervised Learning? 6:06:45 Unsupervised Learning: Process Flow 6:07:17 What is Clustering? 6:09:15 Types of Clustering 6:10:15 K-Means Clustering 6:10:40 K-Means Algorithm Working 6:16:17 K-Means Algorithm 6:19:16 Fuzzy C-Means Clustering 6:21:22 Hierarchical Clustering 6:22:53 Association Clustering 6:24:57 Association Rule Mining 6:30:35 Apriori Algorithm 6:37:45 Apriori Demo 6:40:49 What is Reinforcement Learning? 6:42:48 Reinforcement Learning Process 6:51:10 Markov Decision Process 6:54:53 Understanding Q - Learning 7:13:12 Q-Learning Demo 7:25:34 The Bellman Equation 7:48:39 What is Deep Learning? 7:52:53 Why we need Artificial Neuron? 7:54:33 Perceptron Learning Algorithm 7:57:57 Activation Function 8:03:14 Single Layer Perceptron 8:04:04 What is Tensorflow? 8:07:25 Demo 8:21:03 What is a Computational Graph? 8:49:18 Limitations of Single Layer Perceptron 8:50:08 Multi-Layer Perceptron 8:51:24 What is Backpropagation? 8:52:26 Backpropagation Learning Algorithm 8:59:31 Multi-layer Perceptron Demo 9:01:23 Data Science Interview Questions Комментарий от : VISHWANATH T S |
 | Edureka is great 👍👏😊 Much love to you guys.. Комментарий от : Aryan menon 16 |
 | love edureka, Комментарий от : Kumaran bala |
 | Nice course! Greetings from UK Комментарий от : mbagraduate application |
 | Thank you, it helps me lot Комментарий от : Harsh Ranjan |
 | thank you so much Комментарий от : shubh verma |
 | Thank you very much for your video tutorials, its very help full to me Комментарий от : Khayima Arnisti |
 | Thank You so much Комментарий от : andrzej21111 |
 | I am 11 years old ....... I am studying it from age 6 Комментарий от : Karthika Ravindran |
 | Excellent video!! Is it possible upload the datasets used in this folder?? Maybe like a drive folder Комментарий от : Shreenidhi R |
 | where are the datasets? Комментарий от : Drishal Ballaney |
 | Can I get the datasets used in this course?? Комментарий от : maheswari mahi |
 | This is what we wanted.. Thank u Edureka for providing us with such a good video for free😊😊 Комментарий от : Rishabh Kumar |
 | Where I can get code and datasets? Комментарий от : Vignesh Pai |
 | Thank you such much for providing precious stuff. Комментарий от : Anything for Fun |
 | It's very helpful video ... Комментарий от : om shah #prayagraj |
 | Amazing content, loved it! Комментарий от : akhil shaganti |
 | what is the difference between ur course on youtube and paid ones on your website? Комментарий от : Garry |
 | Just Completed the 10hrs theory. Комментарий от : Debojyoti Mandal |
 | It's a great beginners course. Loved it, thank you for getting me started off. Комментарий от : Dee |
 | Thank you for this wonderful course 💗 Комментарий от : Naaz |
 | 1:00:00 Комментарий от : All About Studies |
 | Thanks for the information and knowledge❤ Комментарий от : Rupesh Sunuwar |
 | Im just 12 years old and excited about data science Комментарий от : Mail Rama |
 | Great thanks for sharing this lecture. Комментарий от : Ravi Ranjan |
 | Thabkyou so much Edureka ❤ Комментарий от : Malik Muhammad Ali |
 | Thanks edureka for this video 💗💗 Комментарий от : Panchanan Sahoo |
 | Please we need this course in hindi Комментарий от : Gohar Ali |
 | Awesome video. Is it possible to have the data used? Комментарий от : Daniel Mwaura |
 | Biggest fan and i just watch edureka videos only Комментарий от : yash shukla |
 | Edureka team gets 5 star room in heaven Thanks for the amazing video I love this video a lot Комментарий от : Giri Vardhana Kumar |
 | Thank you so much edureka! for this video. This is so beneficial for me as a beginner. 😊😊 Комментарий от : Utkarsha Nishad |
 | thankyou edureka for being in youtube and giving us the best courses free without any cost love you edureka thanks keep it up Комментарий от : Saksham Sharma |
 | thanks Комментарий от : Hashem Alattas |
