Machine Learning Practical Workout 8 RealWorld Projects
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
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.”
Overview
Section 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 BONUS: Learning Path
Lecture 5 ML vs. DL vs. AI
Lecture 6 ML Deep Dive
Lecture 7 Download Course Materials
Lecture 8 BONUS: ML vs DL vs AI
Lecture 9 BONUS: 5 Benefits of Jupyter Notebook
Section 2: ANACONDA AND JUPYTER INSTALLATION
Lecture 10 Download and Set up Anaconda
Lecture 11 What is Jupyter Notebook
Lecture 12 Install Tensorflow
Lecture 13 How to run a Jupyter Notebook
Section 3: PROJECT #1: ARTIFICIAL NEURAL NETWORKS – CAR SALES PREDICTION
Lecture 14 Introduction
Lecture 15 Theory Part 1
Lecture 16 Theory Part 2
Lecture 17 Theory Part 3
Lecture 18 Theory Part 4
Lecture 19 Theory Part 5
Lecture 20 Project Overview
Lecture 21 Import Data
Lecture 22 Data Visualization Cleaning
Lecture 23 Model Training 1
Lecture 24 Model Training 2
Lecture 25 Model Evaluation
Section 4: PROJECT #2: DEEP NEURAL NETWORKS – CIFAR-10 CLASSIFICATION
Lecture 26 Introduction
Lecture 27 Theory Part 1
Lecture 28 Theory Part 2
Lecture 29 Theory Part 3
Lecture 30 Theory Part 4
Lecture 31 Problem Statement
Lecture 32 Data Vizualization
Lecture 33 Data Preparation
Lecture 34 Model Training Part 1
Lecture 35 Model Training Part 2
Lecture 36 Model Evaluation
Lecture 37 Save the Model
Lecture 38 Image Augmentation Part 1
Lecture 39 Image augmentation Part 2
Section 5: PROJECT #3: PROPHET TIME SERIES – CHICAGO CRIME RATE
Lecture 40 Introduction
Lecture 41 Project Overview
Lecture 42 Import Dataset
Lecture 43 Data Vizualization
Lecture 44 Prepare the Data
Lecture 45 Make Predictions
Section 6: PROJECT #4: PROPHET TIME SERIES – AVOCADO MARKET
Lecture 46 Introduction
Lecture 47 Load Avocado Data
Lecture 48 Explore Dataset
Lecture 49 Make Predictions Part 1
Lecture 50 Make Predictions Part 2 (Region Specific)
Lecture 51 Make Prediction Part 2.1
Section 7: PROJECT #5: LE-NET DEEP NETWORK – TRAFFIC SIGN CLASSIFICATION
Lecture 52 Introduction
Lecture 53 Project Overview
Lecture 54 Load Data
Lecture 55 Data Exploration
Lecture 56 Data Normalization
Lecture 57 Model Training
Lecture 58 Model Evaluation
Section 8: PROJECT #6: NATURAL LANGUAGE PROCESSING – E-MAIL SPAM FILTER
Lecture 59 Introduction
Lecture 60 Naive Bayes Theory Part 1
Lecture 61 Naive Bayes Theory Part 2
Lecture 62 Spam Project Overview
Lecture 63 Visualize Dataset
Lecture 64 Count Vectorizer
Lecture 65 Model Training Part 1
Lecture 66 Model Training Part 2
Lecture 67 Testing
Section 9: PROJECT #7: NATURAL LANGUAGE PROCESSING – YELP REVIEWS
Lecture 68 Introduction
Lecture 69 Theory
Lecture 70 Project Overview
Lecture 71 Load Dataset
Lecture 72 Visualize Dataset Part 1
Lecture 73 Visualize Dataset Part 2
Lecture 74 Exercise #1
Lecture 75 Exercise #2
Lecture 76 Exercise #3
Lecture 77 Apply NLP to Data
Lecture 78 Apply Count Vectorizer to Data
Lecture 79 Model Training Part 1
Lecture 80 Model Training Part 2
Lecture 81 Model Evaluation Part 1
Lecture 82 Model Evaluation Part 2
Section 10: PROJECT #8: USER-BASED COLLABORATIVE FILTERING – MOVIE RECOMMENDER SYSTEM
Lecture 83 Introduction
Lecture 84 Theory
Lecture 85 Project Overview
Lecture 86 Import Movie Dataset
Lecture 87 Visualize Dataset
Lecture 88 Collaborative Filter One Movie
Lecture 89 Full Movie Recomendation
Section 11: Bonus Lectures
Lecture 90 ***YOUR SPECIAL BONUS***
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
Course Information:
Udemy | English | 14h 13m | 7.60 GB
Created by: Dr. Ryan Ahmed, Ph.D., MBA
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