Machine Learning Practical Workout | 8 Real-World Projects
4.4 (500 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,500 students enrolled

Machine Learning Practical Workout | 8 Real-World Projects

Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks
4.4 (500 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,500 students enrolled
Last updated 5/2020
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 14 hours on-demand video
  • 6 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 90 lectures 14:14:35
+ INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]
9 lectures 44:18
Updates on Udemy Reviews
01:04
BONUS: Learning Path
00:51
ML Deep Dive
13:22
Download Course Materials
00:02
BONUS: ML vs DL vs AI
00:26
BONUS: 5 Benefits of Jupyter Notebook
00:59
+ ANACONDA AND JUPYTER INSTALLATION
4 lectures 18:28
Download and Set up Anaconda
04:12
What is Jupyter Notebook
03:34
Install Tensorflow
00:05
How to run a Jupyter Notebook
10:37
+ PROJECT #1: ARTIFICIAL NEURAL NETWORKS - CAR SALES PREDICTION
12 lectures 02:02:48
Theory Part 1
13:01
Theory Part 2
06:58
Theory Part 3
10:14
Theory Part 4
06:37
Theory Part 5
05:26
Import Data
10:14
Data Visualization Cleaning
21:12
Model Training 1
18:25
Model Training 2
09:49
Model Evaluation
12:30
+ PROJECT #2: DEEP NEURAL NETWORKS - CIFAR-10 CLASSIFICATION
14 lectures 02:45:44
Theory Part 1
05:56
Theory Part 2
17:08
Theory Part 3
12:59
Theory Part 4
16:06
Data Vizualization
15:38
Data Preparation
09:58
Model Training Part 1
16:56
Model Training Part 2
12:14
Model Evaluation
14:24
Save the Model
04:40
Image augmentation Part 2
13:06
+ PROJECT #3: PROPHET TIME SERIES - CHICAGO CRIME RATE
6 lectures 58:36
Project Overview
07:16
Import Dataset
07:27
Data Vizualization
29:18
Prepare the Data
04:55
Make Predictions
08:46
+ PROJECT #4: PROPHET TIME SERIES - AVOCADO MARKET
6 lectures 45:23
Load Avocado Data
09:02
Explore Dataset
14:07
Make Predictions Part 1
09:52
Make Predictions Part 2 (Region Specific)
05:28
Make Prediction Part 2.1
06:14
+ PROJECT #5: LE-NET DEEP NETWORK - TRAFFIC SIGN CLASSIFICATION
7 lectures 01:33:01
Introduction
01:25
Project Overview
09:11
Load Data
12:43
Data Exploration
07:46
Data Normalization
14:03
Model Training
26:38
Model Evaluation
21:15
+ PROJECT #6: NATURAL LANGUAGE PROCESSING - E-MAIL SPAM FILTER
9 lectures 01:27:20
Introduction
01:19
Naive Bayes Theory Part 1
16:06
Naive Bayes Theory Part 2
14:55
Spam Project Overview
09:25
Visualize Dataset
09:54
Count Vectorizer
14:12
Model Training Part 1
09:01
Model Training Part 2
05:08
Testing
07:20
+ PROJECT #7: NATURAL LANGUAGE PROCESSING - YELP REVIEWS
15 lectures 02:15:29
Introduction
00:54
Theory
03:12
Project Overview
06:11
Load Dataset
13:41
Visualize Dataset Part 1
18:01
Visualize Dataset Part 2
10:52
Exercise #1
09:19
Exercise #2
11:21
Exercise #3
10:52
Apply NLP to Data
13:40
Apply Count Vectorizer to Data
04:53
Model Training Part 1
07:55
Model Training Part 2
05:31
Model Evaluation Part 1
06:17
Model Evaluation Part 2
12:50
+ PROJECT #8: USER-BASED COLLABORATIVE FILTERING - MOVIE RECOMMENDER SYSTEM
7 lectures 01:22:58
Introduction
00:41
Theory
08:07
Project Overview
03:39
Import Movie Dataset
15:15
Visualize Dataset
20:36
Collaborative Filter One Movie
21:55
Full Movie Recomendation
12:45
Requirements
  • Deep Learning and Machine Learning basics
  • PC with Internet connetion
Description

"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."

Who this course is for:
  • Data Scientists who want to apply their knowledge on Real World Case Studies
  • Deep Learning practitioners who want to get more Practical Assigmetns
  • Machine Learning Enthusiasts who look to add more projects to their Portfolio