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30-Day Money-Back Guarantee
Development Data Science Artificial Intelligence

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
Rating: 4.3 out of 54.3 (891 ratings)
9,595 students
Created by Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard, Ligency Team
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

11 sections • 90 lectures • 14h 14m total length

  • Preview02:43
  • Updates on Udemy Reviews
    01:04
  • Preview08:35
  • BONUS: Learning Path
    00:33
  • Preview16:15
  • ML Deep Dive
    13:22
  • Download Course Materials
    00:04
  • BONUS: ML vs DL vs AI
    00:26
  • BONUS: 5 Benefits of Jupyter Notebook
    00:59

  • 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

  • Preview01:08
  • 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
  • Preview07:14
  • Import Data
    10:14
  • Data Visualization Cleaning
    21:12
  • Model Training 1
    18:25
  • Model Training 2
    09:49
  • Model Evaluation
    12:30

  • Preview01:07
  • Theory Part 1
    05:56
  • Theory Part 2
    17:08
  • Theory Part 3
    12:59
  • Theory Part 4
    16:06
  • Preview09:13
  • 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
  • Preview16:19
  • Image augmentation Part 2
    13:06

  • Preview00:54
  • Project Overview
    07:16
  • Import Dataset
    07:27
  • Data Vizualization
    29:18
  • Prepare the Data
    04:55
  • Make Predictions
    08:46

  • Preview00:40
  • 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

  • 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

  • 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

  • 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

  • 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

Featured review

Jared Rentz
Jared Rentz
172 courses
50 reviews
Rating: 5.0 out of 5a year ago
Excellent course! There are a lot of mathematical details that the instructor covers so that you have a basic understanding of what you are doing and not just typing in code. I would highly recommend this to users who are looking to perform some data science projects using Neural Networks.

Instructors

Dr. Ryan Ahmed, Ph.D., MBA
Professor & Best-selling Udemy Instructor, 200K+ students
Dr. Ryan Ahmed, Ph.D., MBA
  • 4.5 Instructor Rating
  • 18,281 Reviews
  • 219,225 Students
  • 27 Courses

Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. 

Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 200,000+ students globally. He has over 15 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. 

Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.

* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.



Mitchell Bouchard
B.S, Host @RedCapeLearning 365,000 Students
Mitchell Bouchard
  • 4.4 Instructor Rating
  • 21,769 Reviews
  • 369,904 Students
  • 51 Courses

Mitch is a Canadian filmmaker from Harrow Ontario, Canada. In 2016 he graduated from Dakota State University with a B.S, in Computer Graphics specializing in Film and Cinematic Arts.

Currently, Mitch operates as the Chairman of Red Cape Studios, Inc. where he continues his passion for filmmaking. He is also the Host of Red Cape Learning and Produces / Directs content for Red Cape Films.

He has reached over 365,000 + Students on Udemy and Produced more than 3X Best-Selling Courses.

Mitch is currently working Producing Online Educational Courses thru Red Cape Studios Inc.

Winning several awards at Dakota State University such as "1st Place BeadleMania", "Winner College 10th Anniversary Dordt Film Festival" as well as "Outstanding Artist Award College of Arts and Sciences".

Mitch has been Featured on CBC's "Windsors Shorts" Tv Show and was also the Producer/Director for TEDX Windsor, featuring speakers from across the Country. 


Ligency Team
Helping Data Scientists Succeed
Ligency Team
  • 4.5 Instructor Rating
  • 470,218 Reviews
  • 1,689,449 Students
  • 109 Courses

Hi there,

We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets and more!

We are here to help you stay on the cutting edge of Data Science and Technology.

See you in class,

Sincerely,

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