| 001 Data Preprocessing EDA Part 1.en.srt | 11.9 KB | ||
| 001 Data Preprocessing EDA Part 1.mp4 | 69.5 MB | ||
| 001 Decision Tree Theory and Steps.en.srt | 14.7 KB | ||
| 001 Decision Tree Theory and Steps.mp4 | 90 MB | ||
| 001 Hyper Parameter Tuning.en.srt | 8.8 KB | ||
| 001 Hyper Parameter Tuning.mp4 | 35.9 MB | ||
| 001 Introduction of Project.en.srt | 14.7 KB | ||
| 001 Introduction of Project.mp4 | 39.5 MB | ||
| 001 Project Steps.en.srt | 10.2 KB | ||
| 001 Project Steps.mp4 | 33.9 MB | ||
| 002 Data Preprocessing EDA Part 2.en.srt | 9.5 KB | ||
| 002 Data Preprocessing EDA Part 2.mp4 | 54.4 MB | ||
| 002 Decision Tree Theory and Steps Continue.en.srt | 11.1 KB | ||
| 002 Decision Tree Theory and Steps Continue.mp4 | 31 MB | ||
| 002 Hyper Parameter Tuning Continue.en.srt | 7.6 KB | ||
| 002 Hyper Parameter Tuning Continue.mp4 | 60.4 MB | ||
| 002 Import Files.en.srt | 9 KB | ||
| 002 Import Files.mp4 | 25.1 MB | ||
| 003 Data Preprocessing EDA Part 3.en.srt | 12 KB | ||
| 003 Data Preprocessing EDA Part 3.mp4 | 49.3 MB | ||
| 003 Installation of Graphviz and Pydotplus.en.srt | 6.3 KB | ||
| 003 Installation of Graphviz and Pydotplus.mp4 | 43.1 MB | ||
| 003 More on Hyperparameter Tuning.en.srt | 6.8 KB | ||
| 003 More on Hyperparameter Tuning.mp4 | 69.2 MB | ||
| 004 Data Preprocessing EDA Part 4.en.srt | 10 KB | ||
| 004 Data Preprocessing EDA Part 4.mp4 | 44.5 MB | ||
| 004 Decision Tree Code Explanation.en.srt | 14.7 KB | ||
| 004 Decision Tree Code Explanation.mp4 | 90 MB | ||
| 005 Exploratory Data Analysis.en.srt | 13.3 KB | ||
| 005 Exploratory Data Analysis.mp4 | 75.3 MB | ||
| 005 Random Forest Code.en.srt | 11.6 KB | ||
| 005 Random Forest Code.mp4 | 75 MB | ||
| 006 Splitting Data.en.srt | 4.9 KB | ||
| 006 Splitting Data.mp4 | 24.9 MB | ||
| 007 Confusion Matrix.en.srt | 8 KB | ||
| 007 Confusion Matrix.mp4 | 38.5 MB | ||
| 008 Confusion Matrix and ROC.en.srt | 11.7 KB | ||
| 008 Confusion Matrix and ROC.mp4 | 62.3 MB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 40 total files | |||
Credit Default Prediction using Logistic Regression
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 19 lectures (2h 47m) | Size: 804 MB
Learn the concepts and application of Predictive Modeling tools and visualize data using them
What you'll learn:
Learn mathematical and Physics formulas which are used to define many scientific events such as Linear Regression
Get to know more about data mining, text analytics, and predictive modeling, learn more about terms such as Risk assessment and Fraud detection
Requirements
You should be self-driven and always motivated to learn. Participation in this Predictive Analytics Training requires consistently meeting deadlines
Individuals should be familiar with programming languages such as Python and R as well as coding experience in these languages will be quite helpful in the course.
He/She should have a strong grasp of inferential and descriptive statistics.
He/She should have a strong understanding of the basic programming concepts such as variables, conditions, loops, functions, and some basic data structures such as arrays, objects, lists, etc.
Description
This Video tutorial starts the introduction to the project explaining all its goals and perspective. Further, we’ll discuss the Project Steps and files needed to be imported. Next, we’ll learn about Data Processing EDA along with concepts such as the Splitting Data and Confusion Matrix. In Continuation, we’ll go through the topic Hyper Parameter Tuning and end the video with the concept of Decision Tree, its theory, and code explanation.
Along with the Predictive Analytics Training, we’ll get used to many new skills and techniques which help in improving your knowledge base and add weight to your CV. Going along the course you will get to taste many of the mathematical and Physics formulas which are used to define many scientific events such as Linear Regression, etc. Further, we’ll get to know more about data mining, text analytics, and predictive modeling, learn more about terms such as Risk assessment and Fraud detection, and also we’ll get to know the data cycle defined in stages as data preprocessing, data preparation, model evaluation, and deployment. Programming skills, as well as Analytic skills, are also attained while going through the curriculum of the course. Hence, this Predictive Analytics Course gives you full exposure to the world of predictive analytics and help you step into its world.
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