English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 73 lectures (11h 40m) | 4.54 GB
Solve 6 real Business Problems. Build Robust AI, DL and NLP models for Sales, Marketing, Operations, HR and PR projects.
Are you looking to land a top-paying job in Data Science?
Or are you a seasoned AI practitioner who want to take your career to the next level?
Or are you an aspiring entrepreneur who wants to maximize business revenue with Data Science and Artificial Intelligence?
If the answer is yes to any of these questions, then this course is for you!
Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, transportation and technology.
In business, Data Science is applied to optimize business processes, maximize revenue and reduce cost. The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.
In this course, we will assume that you are an experienced data scientist who have been recently as a data science consultant to several clients. You have been tasked to apply data science techniques to the following 6 departments: (1) Human Resources, (2) Marketing, (3) Sales, (4) Operations, (5) Public Relations, (6) Production/Maintenance. Your will be provided with datasets from all these departments and you will be asked to achieve the following tasks:
Task #1 @Human Resources Department: Develop an AI model to Reduce hiring and training costs of employees by predicting which employees might leave the company.
Task #2 @Marketing Department: Optimize marketing strategy by performing customer segmentation
Task #3 @Sales Department: Develop time series forecasting models to predict future product prices.
Task #4 @Operations Department: Develop Deep Learning model to automate and optimize the disease detection processes at a hospital.
Task #5 @Public Relations Department: Develop Natural Language Processing Models to analyze customer reviews on social media and identify customers sentiment.
Task #6 @Production/Maintenance Departments: Develop defect detection, classification and localization models.
What you’ll learn
- Develop an AI model to Reduce hiring and training costs of employees by predicting which employees might leave the company.
- Develop Deep Learning model to automate and optimize the disease detection processes at a hospital.
- Develop time series forecasting models to predict future product prices.
- Develop defect detection, classification and localization models.
- Optimize marketing strategy by performing customer segmentation
- Develop Natural Language Processing Models to analyze customer reviews on social media and identify customers sentiment.
Table of Contents
Course Introduction and Welcome Message
1 Updates on Udemy Reviews
2 Introduction
3 Key Tips and Best Practices
4 Course Outline and Key Learning Outcomes
5 Get the Materials
Human Resources Department
6 Introduction to Case Study and Key Learning Outcomes
7 Task #1 Problem Statement and Business Case
8 Task #2 Import Libraries and Datasets
9 Task #3 Explore Dataset
10 Task #3 Explore Dataset
11 Task #3 Explore Dataset
12 Task #3 Explore Dataset
13 Task #4 Perform Data Cleaning
14 Task #5 Understand intuition of Random Forest, Logistic Regression, and ANNs
15 Task #6 Understand Classification KPIs
16 Task #7 Build and Train Logistic Regression Classifier
17 Task #8 Build and Train Random Forest Classifier Model
18 Task #9 Build and Train Artificial Neural Network Classifier Model
Marketing Department
19 Introduction to Case Study and Key Learning Outcomes
20 Task #1 Understand Problem Statement and Business Case
21 Task #2 Import Libraries and Datasets
22 Task #3 Perform Data Visualization
23 Task #4 Understand the Theory and Intuition behind K
24 Task #5 Obtain Optimal Number of Clusters K
25 Task #6 Apply K
26 Task #7 Understand the Intuition Behind Principal Component Analysis (PCA)
27 Task #8 Understand the Intuition Behind Autoencoders
28 Task #9 Build and Train Autoencoder
29 Build and Train Autoencoder
Sales Department
30 Introduction to Case Study and Key Learning Outcomes
31 Task #1 Understand the Problem Statement and Business Case
32 Task #2 Import Datasets
33 Task #2 Import Datasets
34 Task #3 Explore Data
35 Task #3 Explore Data
36 Task #3 Explore Data
37 Task #3 Explore Data
38 Task #4 Understand Facebook Prophet intuition
39 Task #5 Train The Model
40 Task #6 Train The Model
Operations Department
41 Introduction to Case Study and Key Learning Outcomes
42 Task #1 Understand the Business Case and Problem Statement
43 Task #2 Load and Explore Dataset
44 Task #3 Visualize Datasets
45 Task #4 Understand Intuition Behind Convolutional Neural Networks (CNNs)
46 Task #5 Understand Intuition Behind Transfer Learning
47 Task #6 Load Model with Pretrained Weights
48 Task #7 Build and Train ResNet
49 Task #8 Evaluate Trained Model Performance
Public Relations Department
50 Introduction to Case Study and Key Learning Outcomes
51 Task #1 Understand Problem Statement and Business Case
52 Task #2 Import Libraries and Datasets
53 Task #3 Explore Dataset
54 Task #3 Explore Dataset
55 Task #4 Perform Data Cleaning
56 Task #5 Remove Punctuation
57 Task #6 Remove Stopwords
58 Task #7 Perform Tokenization Count Vectorization
59 Task #8 Perform Text Cleaning pipeline
60 Task #9 Naive Bayes Intuition
61 Task #10 Train a Naive Bayes Classifier
62 Task #11 Evaluate Trained Naive Bayes Classifier
63 Task #12 Train and Evaluate a Logistic Regression Classifier
Production Manufacturing Maintenance Department
64 Introduction and Welcome Message
65 Task #1
66 Task #2
67 Task #3
68 Task #4
69 Task #5
70 Task #6
71 Task #7
72 Task #8
73 Task #9
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