**Machine Learning Practical Workout | 8 Real-World Projects**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 14 Hours | 6.65 GB

Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks

“Deep Learning and Machine Learning are one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Machine/Deep Learning techniques are widely used in several sectors nowadays such as banking, healthcare, transportation and technology.

Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance. Deep Learning is a subset of Machine learning that utilizes multi-layer Artificial Neural Networks. Deep Learning is inspired by the human brain and mimics the operation of biological neurons. A hierarchical, deep artificial neural network is formed by connecting multiple artificial neurons in a layered fashion. The more hidden layers added to the network, the more “deep” the network will be, the more complex nonlinear relationships that can be modeled. Deep learning is widely used in self-driving cars, face and speech recognition, and healthcare applications.

The purpose of this course is to provide students with knowledge of key aspects of deep and machine learning techniques in a practical, easy and fun way. The course provides students with practical hands-on experience in training deep and machine learning models using real-world dataset. This course covers several technique in a practical manner, the projects include but not limited to:

(1) Train Deep Learning techniques to perform image classification tasks.

(2) Develop prediction models to forecast future events such as future commodity prices using state of the art Facebook Prophet Time series.

(3) Develop Natural Language Processing Models to analyze customer reviews and identify spam/ham messages.

(4) Develop recommender systems such as Amazon and Netflix movie recommender systems.

The course is targeted towards students wanting to gain a fundamental understanding of Deep and machine learning models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master deep and machine learning models and can directly apply these skills to solve real world challenging problems.”

What you’ll learn

- Deep Learning Practical Applications
- Machine Learning Practical Applications
- How to use ARTIFICIAL NEURAL NETWORKS to predict car sales
- How to use DEEP NEURAL NETWORKS for image classification
- How to use LE-NET DEEP NETWORK to classify Traffic Signs
- How to apply TRANSFER LEARNING for CNN image classification
- How to use PROPHET TIME SERIES to predict crime
- How to use PROPHET TIME SERIES to predict market conditions
- How to develop NATURAL LANGUAGE PROCESSING MODEL to analyze Reviews
- How to apply NATURAL LANGUAGE PROCESSING to develop spam filder
- How to use USER-BASED COLLABORATIVE FILTERING to develop recommender system

**Table of Contents**

**INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]**

1 Welcome Message

2 Updates on Udemy Reviews

3 Course overview

4 ML vs. DL vs. AI

5 ML Deep Dive

6 Download Course Materials

7 BONUS ML vs DL vs AI

8 BONUS 5 Benefits of Jupyter Notebook

**ANACONDA AND JUPYTER INSTALLATION**

9 Download and Set up Anaconda

10 What is Jupyter Notebook

11 Install Tensorflow

12 How to run a Jupyter Notebook

**PROJECT 1 ARTIFICIAL NEURAL NETWORKS – CAR SALES PREDICTION**

13 Introduction

14 Theory Part 1

15 Theory Part 2

16 Theory Part 3

17 Theory Part 4

18 Theory Part 5

19 Project Overview

20 Import Data

21 Data Visualization Cleaning

22 Model Training 1

23 Model Training 2

24 Model Evaluation

**PROJECT 2 DEEP NEURAL NETWORKS – CIFAR-10 CLASSIFICATION**

25 Introduction

26 Theory Part 1

27 Theory Part 2

28 Theory Part 3

29 Theory Part 4

30 Problem Statement

31 Data Vizualization

32 Data Preparation

33 Model Training Part 1

34 Model Training Part 2

35 Model Evaluation

36 Save the Model

37 Image Augmentation Part 1

38 Image augmentation Part 2

**PROJECT 3 PROPHET TIME SERIES – CHICAGO CRIME RATE**

39 Introduction

40 Project Overview

41 Import Dataset

42 Data Vizualization

43 Prepare the Data

44 Make Predictions

**PROJECT 4 PROPHET TIME SERIES – AVOCADO MARKET**

45 Introduction

46 Load Avocado Data

47 Explore Dataset

48 Make Predictions Part 1

49 Make Predictions Part 2 (Region Specific)

50 Make Prediction Part 2.1

**PROJECT 5 LE-NET DEEP NETWORK – TRAFFIC SIGN CLASSIFICATION**

51 Introduction

52 Project Overview

53 Load Data

54 Data Exploration

55 Data Normalization

56 Model Training

57 Model Evaluation

**PROJECT 6 NATURAL LANGUAGE PROCESSING – E-MAIL SPAM FILTER**

58 Introduction

59 Naive Bayes Theory Part 1

60 Naive Bayes Theory Part 2

61 Spam Project Overview

62 Visualize Dataset

63 Count Vectorizer

64 Model Training Part 1

65 Model Training Part 2

66 Testing

**PROJECT 7 NATURAL LANGUAGE PROCESSING – YELP REVIEWS**

67 Introduction

68 Theory

69 Project Overview

70 Load Dataset

71 Visualize Dataset Part 1

72 Visualize Dataset Part 2

73 Exercise 1

74 Exercise 2

75 Exercise 3

76 Apply NLP to Data

77 Apply Count Vectorizer to Data

78 Model Training Part 1

79 Model Training Part 2

80 Model Evaluation Part 1

81 Model Evaluation Part 2

**PROJECT 8 USER-BASED COLLABORATIVE FILTERING – MOVIE RECOMMENDER SYSTEM**

82 Introduction

83 Theory

84 Project Overview

85 Import Movie Dataset

86 Visualize Dataset

87 Collaborative Filter One Movie

88 Full Movie Recomendation

**Bonus Lectures**

89 YOUR SPECIAL BONUS

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