Machine Learning Full Course – Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka

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This Edureka Machine Studying Full Course video will show you how to perceive and be taught Machine Studying Algorithms intimately. This Machine Studying Tutorial is good for each inexperienced persons in addition to professionals who need to grasp Machine Studying Algorithms. Beneath are the matters coated on this Machine Studying Tutorial for Learners video:
00:00 Introduction
2:47 What’s Machine Studying?
4:08 AI vs ML vs Deep Studying
5:43 How does Machine Studying works?
6:18 Varieties of Machine Studying
6:43 Supervised Studying
8:38 Supervised Studying Examples
11:49 Unsupervised Studying
13:54 Unsupervised Studying Examples
16:09 Reinforcement Studying
18:39 Reinforcement Studying Examples
19:34 AI vs Machine Studying vs Deep Studying
22:09 Examples of AI
23:39 Examples of Machine Studying
25:04 What’s Deep Studying?
25:54 Instance of Deep Studying
27:29 Machine Studying vs Deep Studying
33:49 Jupyter Pocket book Tutorial
34:49 Set up
50:24 Machine Studying Tutorial
51:04 Classification Algorithm
51:39 Anomaly Detection Algorithm
52:14 Clustering Algorithm
53:34 Regression Algorithm
54:14 Demo: Iris Dataset
1:12:11 Stats & Chance for Machine Studying
1:16:16 Classes of Information
1:16:36 Qualitative Information
1:17:51 Quantitative Information
1:20:55 What’s Statistics?
1:23:25 Statistics Terminologies
1:24:30 Sampling Methods
1:27:15 Random Sampling
1:28:05 Systematic Sampling
1:28:35 Stratified Sampling
1:29:35 Varieties of Statistics
1:32:21 Descriptive Statistics
1:37:36 Measures of Unfold
1:44:01 Info Acquire & Entropy
1:56:08 Confusion Matrix
2:00:53 Chance
2:03:19 Chance Terminologies
2:04:55 Varieties of Occasions
2:05:35 Chance of Distribution
2:10:45 Varieties of Chance
2:11:10 Marginal Chance
2:11:40 Joint Chance
2:12:35 Conditional Chance
2:13:30 Use-Case
2:17:25 Bayes Theorem
2:23:40 Inferential Statistics
2:24:00 Level Estimation
2:26:50 Interval Estimate
2:30:10 Margin of Error
2:34:20 Speculation Testing
2:41:25 Supervised Studying Algorithms
2:42:40 Regression
2:44:05 Linear vs Logistic Regression
2:49:55 Understanding Linear Regression Algorithm
3:11:10 Logistic Regression Curve
3:18:34 Titanic Information Evaluation
3:58:39 Choice Tree
3:58:59 what’s Classification?
4:01:24 Varieties of Classification
4:08:35 Choice Tree
4:14:20 Choice Tree Terminologies
4:18:05 Entropy
4:44:05 Credit score Danger Detection Use-case
4:51:45 Random Forest
5:00:40 Random Forest Use-Circumstances
5:04:29 Random Forest Algorithm
5:16:44 KNN Algorithm
5:20:09 KNN Algorithm Working
5:27:24 KNN Demo
5:35:05 Naive Bayes
5:40:55 Naive Bayes Working
5:44:25Industrial Use of Naive Bayes
5:50:25 Varieties of Naive Bayes
5:51:25 Steps concerned in Naive Bayes
5:52:05 PIMA Diabetic Check Use Case
6:04:55 Help Vector Machine
6:10:20 Non-Linear SVM
6:12:05 SVM Use-case
6:13:30 okay Means Clustering & Affiliation Rule Mining
6:16:33 Varieties of Clustering
6:17:34 Okay-Means Clustering
6:17:59 Okay-Means Working
6:21:54 Execs & Cons of Okay-Means Clustering
6:23:44 Okay-Means Demo
6:28:44 Hierarchical Clustering
6:31:14 Affiliation Rule Mining
6:34:04 Apriori Algorithm
6:39:19 Apriori Algorithm Demo
6:43:29 Reinforcement Studying
6:46:39 Reinforcement Studying: Counter-Strike Instance
6:53:59 Markov’s Choice Course of
6:58:04 Q-Studying
7:02:39 The Bellman Equation
7:12:14 Transitioning to Q-Studying
7:17:29 Implementing Q-Studying
7:23:33 Machine Studying Initiatives
7:38:53 Who’s a ML Engineer?
7:39:28 ML Engineer Job Traits
7:40:43 ML Engineer Wage Traits
7:42:33 ML Engineer Abilities
7:44:08 ML Engineer Job Description
7:45:53 ML Engineer Resume
7:54:48 Machine Studying Interview Questions

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