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

4.46 (1716 reviews)
Udemy
platform
English
language
Data Science
category
18,136
students
14.5 hours
content
Mar 2024
last update
$74.99
regular price

What you will 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

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."

Content

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

Welcome Message
Updates on Udemy Reviews
Course overview
ML vs. DL vs. AI
ML Deep Dive
Download Course Materials
BONUS: ML vs DL vs AI
BONUS: 5 Benefits of Jupyter Notebook

ANACONDA AND JUPYTER INSTALLATION

Download and Set up Anaconda
What is Jupyter Notebook
Install Tensorflow
How to run a Jupyter Notebook

PROJECT #1: ARTIFICIAL NEURAL NETWORKS - CAR SALES PREDICTION

Introduction
Theory Part 1
Theory Part 2
Theory Part 3
Theory Part 4
Theory Part 5
Project Overview
Import Data
Data Visualization Cleaning
Model Training 1
Model Training 2
Model Evaluation

PROJECT #2: DEEP NEURAL NETWORKS - CIFAR-10 CLASSIFICATION

Introduction
Theory Part 1
Theory Part 2
Theory Part 3
Theory Part 4
Problem Statement
Data Vizualization
Data Preparation
Model Training Part 1
Model Training Part 2
Model Evaluation
Save the Model
Image Augmentation Part 1
Image augmentation Part 2

PROJECT #3: PROPHET TIME SERIES - CHICAGO CRIME RATE

Introduction
Project Overview
Import Dataset
Data Vizualization
Prepare the Data
Make Predictions

PROJECT #4: PROPHET TIME SERIES - AVOCADO MARKET

Introduction
Load Avocado Data
Explore Dataset
Make Predictions Part 1
Make Predictions Part 2 (Region Specific)
Make Prediction Part 2.1

PROJECT #5: LE-NET DEEP NETWORK - TRAFFIC SIGN CLASSIFICATION

Introduction
Project Overview
Load Data
Data Exploration
Data Normalization
Model Training
Model Evaluation

PROJECT #6: NATURAL LANGUAGE PROCESSING - E-MAIL SPAM FILTER

Introduction
Naive Bayes Theory Part 1
Naive Bayes Theory Part 2
Spam Project Overview
Visualize Dataset
Count Vectorizer
Model Training Part 1
Model Training Part 2
Testing

PROJECT #7: NATURAL LANGUAGE PROCESSING - YELP REVIEWS

Introduction
Theory
Project Overview
Load Dataset
Visualize Dataset Part 1
Visualize Dataset Part 2
Exercise #1
Exercise #2
Exercise #3
Apply NLP to Data
Apply Count Vectorizer to Data
Model Training Part 1
Model Training Part 2
Model Evaluation Part 1
Model Evaluation Part 2

PROJECT #8: USER-BASED COLLABORATIVE FILTERING - MOVIE RECOMMENDER SYSTEM

Introduction
Theory
Project Overview
Import Movie Dataset
Visualize Dataset
Collaborative Filter One Movie
Full Movie Recomendation

Bonus Lectures

***YOUR SPECIAL BONUS***

Screenshots

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Reviews

Andrii
October 17, 2023
Seems a bit amateur, most of code examples are outdated, some sections are not completely finished (especially TF-IDF related). Code challenges are simply copy-pasting tasks with changing only one or two pieces. First 4 sections were better than rest of them. In general can get the main idea of each project and how it's implemented, but nothing more.
Gowtham
October 7, 2023
FAKE ,you can get all these dummy dataset projects on youtube, they are not real world datasets .Don't waste your money
Gerardus
July 21, 2023
thank you for delivering the orientation videos with enthusiasm. I really am looking forward to practicing through the project.
Abhik
July 16, 2023
A mix of theory with practical applications was presented. Other videos from Coursera in ML only started with loads of theory. The instructor was very knowledgable and explained the theory and code very well. Thank you for uploading!
Salim
February 27, 2023
I really enjoyed the course, Dr Ryan Sir. It is very crisp and to the point for anyone with some basics on ML and DL. Thank you sir. Amazing way of explanation.!!
Mena
December 19, 2022
Perfect content + very clear explanation. If you are a beginner, I recommend starting with this course first: "Modern AI with Zero Coding"
Hong
November 22, 2022
This is more hands-on type of class, it is well paced with practical projects; seeing how things work makes it much easier to under the theory. I'm software engineer with limited Python experience, and no prior exposure to data science. Surprisingly it doesn't give me much trouble to finish all the projects, I'm starting to apply FB prophet to my work. a few issues that I ran into - a few functions have been dropped in Python 3.10 and latest Keras, has to use newer functions - in project 8, "Movies_id_Titles" file is missing .csv extension, need to manually add the file extension
Carlos
November 12, 2022
I am learning a lot about machine learning. Dr. Ahmed is great at describing the content. I have not used python much, and a beginner at OOP, but I am grasping what Dr. Ahmed is teaching.
Alex
August 18, 2022
Comprehensive and clear, at least in the first half of the course. Around halfway, it's less concise, he makes more errors in the videos, and the code he provides is outdated, which throws errors. This takes up time, although it's good practice troubleshooting and updating the code.
BB
July 14, 2022
I love this guy. He's so personable. The repetition is great. Definitely need to see the code and techniques in many different context and applications. You do need ml and python background to understand 100%, but beginners can still get a lot of of the course.
Junior
June 7, 2022
Terrible, not much explanation. It is not updated. Sample codes don't fully work. Support team does not bring solutions to code problems. Not worth it!
Josue
May 27, 2022
Great materials, Dr. Ahmed is super awesome and knowledgeable, the lectures are well organized and progressively elaborated. Enjoying the learning a lot!
Micah
March 8, 2022
There were some activities that were simple and thus didn't need to be repeated in each section. The author did not explain how the machine learning libraries were selected to be a good fit for the problem to be solved. The author also did not explain how to choose the best parameters to feed into model training such as batch size or number of epocs.
Mehrdad
January 18, 2022
A decent mix of Concept explanation and reviewing practical problems included in this course. P.S. needs a preliminary knowledge of python, pandas and machine learning concept to make maximum use of the course content.
Matthew
January 17, 2022
Please give more specific version information of the API's being used / update the version info to be up to date. When following along many of the latest python API's (which get installed with jupyter notebook / anaconda) are deprecated (e.g. scipy.misc.pilutil) - leaving it to the learner to try and figure out how to adapt to the new API on the fly.

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2367072
udemy ID
5/14/2019
course created date
9/9/2019
course indexed date
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