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This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Tutorial for Beginners video:

00:00 Introduction

2:47 What is Machine Learning?

4:08 AI vs ML vs Deep Learning

5:43 How does Machine Learning works?

6:18 Types of Machine Learning

6:43 Supervised Learning

8:38 Supervised Learning Examples

11:49 Unsupervised Learning

13:54 Unsupervised Learning Examples

16:09 Reinforcement Learning

18:39 Reinforcement Learning Examples

19:34 AI vs Machine Learning vs Deep Learning

22:09 Examples of AI

23:39 Examples of Machine Learning

25:04 What is Deep Learning?

25:54 Example of Deep Learning

27:29 Machine Learning vs Deep Learning

33:49 Jupyter Notebook Tutorial

34:49 Installation

50:24 Machine Learning 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 & Probability for Machine Learning

1:16:16 Categories of Data

1:16:36 Qualitative Data

1:17:51 Quantitative Data

1:20:55 What is Statistics?

1:23:25 Statistics Terminologies

1:24:30 Sampling Techniques

1:27:15 Random Sampling

1:28:05 Systematic Sampling

1:28:35 Stratified Sampling

1:29:35 Types of Statistics

1:32:21 Descriptive Statistics

1:37:36 Measures of Spread

1:44:01 Information Gain & Entropy

1:56:08 Confusion Matrix

2:00:53 Probability

2:03:19 Probability Terminologies

2:04:55 Types of Events

2:05:35 Probability of Distribution

2:10:45 Types of Probability

2:11:10 Marginal Probability

2:11:40 Joint Probability

2:12:35 Conditional Probability

2:13:30 Use-Case

2:17:25 Bayes Theorem

2:23:40 Inferential Statistics

2:24:00 Point Estimation

2:26:50 Interval Estimate

2:30:10 Margin of Error

2:34:20 Hypothesis Testing

2:41:25 Supervised Learning 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 Data Analysis

3:58:39 Decision Tree

3:58:59 what is Classification?

4:01:24 Types of Classification

4:08:35 Decision Tree

4:14:20 Decision Tree Terminologies

4:18:05 Entropy

4:44:05 Credit Risk Detection Use-case

4:51:45 Random Forest

5:00:40 Random Forest Use-Cases

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 Types of Naive Bayes

5:51:25 Steps involved in Naive Bayes

5:52:05 PIMA Diabetic Test Use Case

6:04:55 Support Vector Machine

6:10:20 Non-Linear SVM

6:12:05 SVM Use-case

6:13:30 k Means Clustering & Association Rule Mining

6:16:33 Types of Clustering

6:17:34 K-Means Clustering

6:17:59 K-Means Working

6:21:54 Pros & Cons of K-Means Clustering

6:23:44 K-Means Demo

6:28:44 Hierarchical Clustering

6:31:14 Association Rule Mining

6:34:04 Apriori Algorithm

6:39:19 Apriori Algorithm Demo

6:43:29 Reinforcement Learning

6:46:39 Reinforcement Learning: Counter-Strike Example

6:53:59 Markov’s Decision Process

6:58:04 Q-Learning

7:02:39 The Bellman Equation

7:12:14 Transitioning to Q-Learning

7:17:29 Implementing Q-Learning

7:23:33 Machine Learning Projects

7:38:53 Who is a ML Engineer?

7:39:28 ML Engineer Job Trends

7:40:43 ML Engineer Salary Trends

7:42:33 ML Engineer Skills

7:44:08 ML Engineer Job Description

7:45:53 ML Engineer Resume

7:54:48 Machine Learning Interview Questions

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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Machine Learning & AI Masters Course Curriculum, Visit our Website: http://bit.ly/2QixjBC Here is the video timeline: 2:47 What is Machine Learning?

4:08 AI vs ML vs Deep Learning

5:43 How does Machine Learning works?

6:18 Types of Machine Learning

6:43 Supervised Learning

8:38 Supervised Learning Examples

11:49 Unsupervised Learning

13:54 Unsupervised Learning Examples

16:09 Reinforcement Learning

18:39 Reinforcement Learning Examples

19:34 AI vs Machine Learning vs Deep Learning

22:09 Examples of AI

23:39 Examples of Machine Learning

25:04 What is Deep Learning?

25:54 Example of Deep Learning

27:29 Machine Learning vs Deep Learning

33:49 Jupyter Notebook Tutorial

34:49 Installation

50:24 Machine Learning 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 & Probability for Machine Learning

1:16:16 Categories of Data

1:16:36 Qualitative Data

1:17:51 Quantitative Data

1:20:55 What is Statistics?

1:23:25 Statistics Terminologies

1:24:30 Sampling Techniques

1:27:15 Random Sampling

1:28:05 Systematic Sampling

1:28:35 Stratified Sampling

1:29:35 Types of Statistics

1:32:21 Descriptive Statistics

1:37:36 Measures of Spread

1:44:01 Information Gain & Entropy

1:56:08 Confusion Matrix

2:00:53 Probability

2:03:19 Probability Terminologies

2:04:55 Types of Events

2:05:35 Probability of Distribution

2:10:45 Types of Probability

2:11:10 Marginal Probability

2:11:40 Joint Probability

2:12:35 Conditional Probability

2:13:30 Use-Case

2:17:25 Bayes Theorem

2:23:40 Inferential Statistics

2:24:00 Point Estimation

2:26:50 Interval Estimate

2:30:10 Margin of Error

2:34:20 Hypothesis Testing

2:41:25 Supervised Learning 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 Data Analysis

3:58:39 Decision Tree

3:58:59 what is Classification?

4:01:24 Types of Classification

4:08:35 Decision Tree

4:14:20 Decision Tree Terminologies

4:18:05 Entropy

4:44:05 Credit Risk Detection Use-case

4:51:45 Random Forest

5:00:40 Random Forest Use-Cases

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 Types of Naive Bayes

5:51:25 Steps involved in Naive Bayes

5:52:05 PIMA Diabetic Test Use Case

6:04:55 Support Vector Machine

6:10:20 Non-Linear SVM

6:12:05 SVM Use-case

6:13:30 k Means Clustering & Association Rule Mining

6:16:33 Types of Clustering

6:17:34 K-Means Clustering

6:17:59 K-Means Working

6:21:54 Pros & Cons of K-Means Clustering

6:23:44 K-Means Demo

6:28:44 Hirechial Clustering

6:31:14 Association Rule Mining

6:34:04 Apriori Algorithm

6:39:19 Apriori Algorithm Demo

6:43:29 Reinforcement Learning

6:46:39 Reinforcement Learning: Counter-Strike Example

6:53:59 Markov's Decision Process

6:58:04 Q-Learning

7:02:39 The Bellman Equation

7:12:14 Transitioning to Q-Learning

7:17:29 Implementing Q-Learning

7:23:33 Machine Learning Projects

7:38:53 Who is a ML Engineer?

7:39:28 ML Engineer Job Trends

7:40:43 ML Engineer Salary Trends

7:42:33 ML Engineer Skills

7:44:08 ML Engineer Job Description

7:45:53 ML Engineer Resume

7:54:48 Machine Learning Interview Questions

Can I know the fees of Machine Learning Master Course?

I truely appreciate u all, u guys are doing such a great thing for the learners..

Thank U so much…….

It's awesome..

Thank you very much.Could you please send me the datasets and code used in this lecture….

Hi! Thanks for this informative video. Can I please have a copy of the datasets?

One of the best channel I have ever seen 😍.. The best part is u are teaching from the basics.. Anyone can understand without any difficulty ☺

Could you please share the code ? It'll be great help , I'll be grateful to you

This is really a very informative lecture on ML. Can u please share the codes used in this lecture for better understanding of the topic.

@edureka great job on the video, could you please share the codes used in the video

guys can you email me the datasets

Thanks a lot

Literally thanks from my bottom of my 💓 heart

Does anybody have the solution for the Bayes' theorem example?

I got 0.647. I'm not sure I got it right.

Great job .It would have been great if you would have put the code in the description

This is wealth! Good one Edureka!

#Edureka, please where can i get the CODE and DATASET used in the tutorial?

12:00

This course is too good…Highly recommend🙌…but can you plz share the source code and dataset used in these projects please ..!!

Thank you very much! Can u please send the datasets and code??

thanks Edureka! please share the data sets and code

Could I please have access to the code and datasets? Thanks!

Adept course! Highly recommend it! Thanks, Edureka team!

@edureka Where you guys download the data like Titanic or that head size and weight of brain in linear regression project can you kindly say where to get the datasets for practice plzzz

Amazing Tutorial

Can the datasets and the codes used by the instructors be shared with us? Also, it would be great to have some sort of notes of the tutorial if those can be shared as well.

can anyone help me out in finding the data sets showed in the video. Thanks a bunch!

Thanks, Edureka for this tutorial. It was really good and informative

Can you please provide the datasets and code

Thank you very much! Can u please send the datasets and code??