Data Science Didactic Total Course in Online with Scratch Examples



About Data Science Full Course:

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.

Data Science Didactic Total Course in Online with Scratch Examples



About Course Timings:



  • 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:!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



Copyrights: Edureka

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