ダウンロード "Data Science Full Course - Learn Data Science in 10 Hours | Data Science For Beginners | Edureka"

🔥 Data Science Course (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/masters-program/data-scientist-certification This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms. Below are the topics covered in this Data Science for Beginners course: 00:00 Data Science Full Course 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 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 5:24:30 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 🔴 Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://www.youtube.com/user/edurekaIN?sub_confirmation=1 Edureka Data Science Training & Certifications 🔵 Data Science Training using Python: https://www.edureka.co/data-science-python-certification-course 🔵 Python Programming Training: https://www.edureka.co/python-programming-certification-training 🔵 Python Masters Program: https://www.edureka.co/masters-program/python-developer-training 🔵 Machine Learning Course using Python: https://www.edureka.co/data-science-python-certification-course 🔵 Data Scientist Masters Program: https://www.edureka.co/masters-program/data-scientist-certification 🔵 Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll free).