In this machine learning tutorial you will learn what is machine learning, machine learning algorithms like linear regression, binary classification, decision tree, random forest and unsupervised algorithm like k means clustering in detail. What is Machine Learning? How does Machine Learning works? AI vs ML vs Deep Learning, What is Deep Learning? Machine Learning vs Deep Learning, Jupyter Notebook Tutorial, Understanding Linear Regression Algorithm, KNN Algorithm Working, Machine Learning Interview Questions ...

This Machine Learning Full Course 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:

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

5:44:25 Industrial 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

In this TensorFlow tutorial for beginners - TensorFlow on Neural Networks, you will learn TensorFlow concepts like what are Tensors, what are the program elements in TensorFlow , what are constants & placeholders in TensorFlow Python, how variable works in placeholder and a demo on MNIST.

**TensorFlow Tutorial for Beginners - TensorFlow on Neural Networks**

In this TensorFlow tutorial for beginners - TensorFlow on Neural Networks, you will learn TensorFlow concepts like what are Tensors, what are the program elements in Tensorflow, what are constants & placeholders in TensorFlow Python, how variable works in placeholder and a demo on MNIST.

In this TensorFlow 2.0 tutorial, you’ll understanding of how you can get started building machine learning models in Python with TensorFlow 2.0 as well as the other exciting available features!

Learn about the updates being made to TensorFlow in its 2.0 version. We’ll give an overview of what’s available in the new version as well as do a deep dive into an example using its central high-level API, Keras. You’ll walk away with a better understanding of how you can get started building machine learning models in Python with TensorFlow 2.0 as well as the other exciting available features!

In this TensorFlow full course tutorial for Beginners will help you learn about Deep Learning with TensorFlow in detail, understand the basics of Deep Learning, how to install TensorFlow 2.0 on Ubuntu, how to use TensorFlow in Python, how to use TensorFlow object detection API to detect objects in images as well as videos

This video on TensorFlow full course tutorial for Beginners will help you learn about Deep Learning with TensorFlow in detail. You will understand the basics of Deep Learning and learn the various applications in Deep Learning. You will get an idea about how to install TensorFlow on Ubuntu, followed by what is TensorFlow, and TensorFlow tutorial. You will use TensorFlow object detection API to detect objects in images as well as videos. Finally, you will perform a demo using TensorFlow in Python. Now, let's dive into learning TensorFlow.

The below topics are covered in this AWS full course tutorial:

Here are the topics covered with the timelines:

- Animated Video 01:04
- Deep Learning Applications 06:45
- Healthcare 07:27
- Entertainment 10:15
- Composing music 11:51
- Image coloring 12:51
- Robotics 13:30
- Image Captioning 15:42
- Advertising 16:18
- Earthquake prediction 17:45
- Deep Learning Frameworks 18:27
- TensorFlow 19:42
- Keras 21:09
- PyTorch 23:15
- Theano 24:45
- DL4J 26:09
- Caffe 28:06
- Chainer 29:45
- Microsoft CNTK 32:03
- Installing TensorFlow on Ubuntu 34:27
- Deep Learning in Python 1:02:15
- What is Deep Learning 01:03:58
- Biological Neuron vs Artificial Neuron 01:04:46
- What is Neural Network 01:06:13
- How do Neural networks work 01:18:07
- Deep Learning Platforms 01:26:52
- Introduction to TensorFlow 01:28:01
- Why TensorFlow 01:58:16
- What is TensorFlow 02:00:28
- What is Data Flow Graph 02:04:22
- TensorFlow Program Basics 02:13:55
- Use Case Implementation using TensorFlow 02:45:25
- Detecting Diabetic Retinopathy 03:04:58
- Linear Regression using TensorFlow 03:06:04
- Introduction Recurrent Neural Networks 03:21:37
- How does a Recurrent Neural Networks look like 03:23:10
- Types of RNN 03:25:55
- Use Case Implementation of RNN 03:28:04
- TensorFlow Object Detection API Tutorial 03:46:07
- Libraries Required 03:56:55
- COCO Dataset 03:57:16
- Use Case Implementation of TensorFlow 04:08:25