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

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This Edureka Machine Learning Full Course video will aid you perceive and be taught Machine Learning Algorithms intimately. This Machine Learning Tutorial is good for each newbies in addition to professionals who wish to grasp Machine Learning Algorithms. Below are the subjects coated on 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’s 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 concerned 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 ok Means Clustering & Association Rule Mining

6:16:33 Types of Clustering

6:17:34 Ok-Means Clustering

6:17:59 Ok-Means Working

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

6:23:44 Ok-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. 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 you pls share the dataset and the source code?

@edureka! Please share the codes and datasets used in this video.

@edureka! Hey!!Great video& content ! But can you please provide/share the entire code and all the datasets used in this tutorial ??? That will be very helpful.

Hi Edureka, what is the difference between this course and https://www.edureka.co/machine-learning-certification-training?utm_source=youtube&utm_medium=description&utm_campaign=ml-full-course? Seems same, just the one is paid π

awesomely explained Entropy and Information Gain… Thumbs Up.

The content is great. But can you please share the source code of projects?

Thanks for the course. I am at 1:00:00 time and getting NaN when printing IRIS dataset with head() in jupyter notebook. Please help

Thanks a lot from deep of my heart. I am waiting this course for a long time !!!

ImportError: cannot import name 'KNeighboursClassifier' from 'sklearn.neighbors'

pls help me with this

This is awesome. Help me understand ML and helped me with my coursework.

plz send me the dataset and the code

Thanks Edureka, this is very helpful . could you please share datasets and codes.

Can you please share codes & data please @edureka!

Great content! Can you please provide the datasets and codes?

This video is awesome. From where I can find the codes for this projects??

Sir,i required complete code of this course

Where can i find the source code for the different examples shown?

Thank you for the video. Where can I get the source code and dataset

Team edureka was awesone !

Great content. Where can i find the source code for the different examples shown?

Is code and dataset used in the video available for free to everyone? Or is it only for paid students?

Thanks