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Machine Learning Practical Workout | 8 Real-World Projects

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Machine Learning Practical Workout | 8 Real-World Projects, available at $74.99, has an average rating of 4.62, with 95 lectures, based on 1854 reviews, and has 19345 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Scientists who want to apply their knowledge on Real World Case Studies or Deep Learning practitioners who want to get more Practical Assigmetns or Machine Learning Enthusiasts who look to add more projects to their Portfolio It is particularly useful for Data Scientists who want to apply their knowledge on Real World Case Studies or Deep Learning practitioners who want to get more Practical Assigmetns or Machine Learning Enthusiasts who look to add more projects to their Portfolio.

Enroll now: Machine Learning Practical Workout | 8 Real-World Projects

Summary

Title: Machine Learning Practical Workout | 8 Real-World Projects

Price: $74.99

Average Rating: 4.62

Number of Lectures: 95

Number of Published Lectures: 90

Number of Curriculum Items: 95

Number of Published Curriculum Objects: 90

Original Price: $199.99

Quality Status: approved

Status: Live

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

Who Should Attend

  • 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

Target Audiences

  • 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

“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.”

Course Curriculum

Chapter 1: INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]

Lecture 1: Welcome Message

Lecture 2: Updates on Udemy Reviews

Lecture 3: Course overview

Lecture 4: EXTRA: Learning Path

Lecture 5: ML vs. DL vs. AI

Lecture 6: ML Deep Dive

Lecture 7: Download Course Materials

Lecture 8: EXTRA: ML vs DL vs AI

Lecture 9: EXTRA: 5 Benefits of Jupyter Notebook

Chapter 2: ANACONDA AND JUPYTER INSTALLATION

Lecture 1: Download and Set up Anaconda

Lecture 2: What is Jupyter Notebook

Lecture 3: Install Tensorflow

Lecture 4: How to run a Jupyter Notebook

Chapter 3: PROJECT #1: ARTIFICIAL NEURAL NETWORKS – CAR SALES PREDICTION

Lecture 1: Introduction

Lecture 2: Theory Part 1

Lecture 3: Theory Part 2

Lecture 4: Theory Part 3

Lecture 5: Theory Part 4

Lecture 6: Theory Part 5

Lecture 7: Project Overview

Lecture 8: Import Data

Lecture 9: Data Visualization Cleaning

Lecture 10: Model Training 1

Lecture 11: Model Training 2

Lecture 12: Model Evaluation

Chapter 4: PROJECT #2: DEEP NEURAL NETWORKS – CIFAR-10 CLASSIFICATION

Lecture 1: Introduction

Lecture 2: Theory Part 1

Lecture 3: Theory Part 2

Lecture 4: Theory Part 3

Lecture 5: Theory Part 4

Lecture 6: Problem Statement

Lecture 7: Data Vizualization

Lecture 8: Data Preparation

Lecture 9: Model Training Part 1

Lecture 10: Model Training Part 2

Lecture 11: Model Evaluation

Lecture 12: Save the Model

Lecture 13: Image Augmentation Part 1

Lecture 14: Image augmentation Part 2

Chapter 5: PROJECT #3: PROPHET TIME SERIES – CHICAGO CRIME RATE

Lecture 1: Introduction

Lecture 2: Project Overview

Lecture 3: Import Dataset

Lecture 4: Data Vizualization

Lecture 5: Prepare the Data

Lecture 6: Make Predictions

Chapter 6: PROJECT #4: PROPHET TIME SERIES – AVOCADO MARKET

Lecture 1: Introduction

Lecture 2: Load Avocado Data

Lecture 3: Explore Dataset

Lecture 4: Make Predictions Part 1

Lecture 5: Make Predictions Part 2 (Region Specific)

Lecture 6: Make Prediction Part 2.1

Chapter 7: PROJECT #5: LE-NET DEEP NETWORK – TRAFFIC SIGN CLASSIFICATION

Lecture 1: Introduction

Lecture 2: Project Overview

Lecture 3: Load Data

Lecture 4: Data Exploration

Lecture 5: Data Normalization

Lecture 6: Model Training

Lecture 7: Model Evaluation

Chapter 8: PROJECT #6: NATURAL LANGUAGE PROCESSING – E-MAIL SPAM FILTER

Lecture 1: Introduction

Lecture 2: Naive Bayes Theory Part 1

Lecture 3: Naive Bayes Theory Part 2

Lecture 4: Spam Project Overview

Lecture 5: Visualize Dataset

Lecture 6: Count Vectorizer

Lecture 7: Model Training Part 1

Lecture 8: Model Training Part 2

Lecture 9: Testing

Chapter 9: PROJECT #7: NATURAL LANGUAGE PROCESSING – YELP REVIEWS

Lecture 1: Introduction

Lecture 2: Theory

Lecture 3: Project Overview

Lecture 4: Load Dataset

Lecture 5: Visualize Dataset Part 1

Lecture 6: Visualize Dataset Part 2

Lecture 7: Exercise #1

Lecture 8: Exercise #2

Lecture 9: Exercise #3

Lecture 10: Apply NLP to Data

Lecture 11: Apply Count Vectorizer to Data

Lecture 12: Model Training Part 1

Lecture 13: Model Training Part 2

Lecture 14: Model Evaluation Part 1

Lecture 15: Model Evaluation Part 2

Chapter 10: PROJECT #8: USER-BASED COLLABORATIVE FILTERING – MOVIE RECOMMENDER SYSTEM

Lecture 1: Introduction

Lecture 2: Theory

Lecture 3: Project Overview

Lecture 4: Import Movie Dataset

Lecture 5: Visualize Dataset

Lecture 6: Collaborative Filter One Movie

Lecture 7: Full Movie Recomendation

Chapter 11: Congratulations!! Don't forget your Prize 🙂

Instructors

Rating Distribution

  • 1 stars: 30 votes
  • 2 stars: 36 votes
  • 3 stars: 166 votes
  • 4 stars: 612 votes
  • 5 stars: 1010 votes

Frequently Asked Questions


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