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🔥 Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training
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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(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

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edureka! says:

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

Harshad says:

Can I know the fees of Machine Learning Master Course?

Muskaan Singh says:

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

heena shaikh says:

Thank U so much…….

Sumit Agrawal says:

It's awesome..

khushboo alvi says:

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

McSpenzer Casuga says:

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

Ananya Ujire says:

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 ☺

Yashi Rajput says:

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

Mahima Shukla says:

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.

Abubakr Fatmi says:

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

Ashish Sarkar says:

guys can you email me the datasets

Harsh Ranjan says:

Thanks a lot

Aarushi singh❤️ says:

Literally thanks from my bottom of my 💓 heart

sanjana k says:

Does anybody have the solution for the Bayes' theorem example?
I got 0.647. I'm not sure I got it right.

Dheeraj Sai says:

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

Abhiranjan Jagannath says:

This is wealth! Good one Edureka!

ariyo kabir says:

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

payshvi ghodwall says:

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

Pratyush Shivam says:

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

Jude Emeka says:

thanks Edureka! please share the data sets and code

Tom Snively says:

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

Amandeep Kaur says:

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

Karthikdon Don says:

@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

Sonali Gupta says:

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.

Nabhit Arora says:

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

lilly lidiya says:

Thanks, Edureka for this tutorial. It was really good and informative
Can you please provide the datasets and code

Thasna C A says:

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

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