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4. Introduction to Machine Learning.html
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1 - Introduction
10 - S02L04 - Machine Learning Explained
11 - S02L05 - Generative AI and Applications
12 - Assignment Understanding AI Fundamentals
13 - 01_S03L01 - ChatGPT Getting Started and Interface
2 - 02_Meet Your Instructor Learning Approach
3 - 03_Course Roadmap_What you will learn
4 - 04_How to Use This Course_Effectively
4 - 04_How to Use This Course_Effectively (Description).html
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921 B
5 - 05_Resources & Getting Started
5 - 05_Resources & Getting Started (Description).html
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921 B
6 - Assignment Getting Started with Your AI Learning Journey
6 - Assignment Getting Started with Your AI Learning Journey.html
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9.6 KB
7 - S02L01 - What is Artificial Intelligence
7 - S02L01 - What is Artificial Intelligence (Description).html
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921 B
8 - S02L02 - History of AI
8 - S02L02 - History of AI (Description).html
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921 B
9 - S02L03 - Types of AI
9 - S02L03 - Types of AI (Description).html
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819 B
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1 - Course Introduction
2 - What It Means For a Machine to Learn
4 - What Learning Means in Machine Learning (Description).html
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2.6 KB
3 - Linear Regression Learning From Error
4 - Logistic Regression Probabilities and Decisions
12 - Logistic Regression Predicting Classes (Description).html
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2.2 KB
13 - Probabilities and Decision Thresholds (Description).html
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2.1 KB
14 - Confusion Matrices and Classification Metrics (Description).html
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2.3 KB
15 - How to Logistic Regression and Evaluation.html
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13.4 KB
2 - Classification and Decision-Making.html
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24.7 KB
5 - k-Nearest Neighbors Learning By Similarity
16 - k-Nearest Neighbors Distance-Based Learning (Description).html
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2 KB
17 - How to Choose k.html
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10.6 KB
18 - Feature Scaling and Why It Matters (Description).html
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2.1 KB
19 - How to KNN and Feature Scaling.html
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13.2 KB
3 - Similarity-Based Learning.html
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25.9 KB
6 - Decision Trees Learning Rules
20 - Decision Trees Learning Rules (Description).html
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1.8 KB
21 - Tree Depth and Model Complexity (Description).html
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1.6 KB
22 - How to Decision Trees.html
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13.5 KB
23 - Cross-Validation Testing Model Stability (Description).html
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2 KB
24 - How to Cross-Validation.html
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9.1 KB
4 - Decision Trees and Model Complexity.html
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26.3 KB
7 - Random Forests Learning With Many Models
25 - Random Forests Learning With Many Models (Description).html
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1.9 KB
26 - How to Random Forests.html
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13.6 KB
27 - Bias Vs. Variance (Description).html
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2 KB
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1 - Introduction to AI & Quantum Computing
2 - AI & Machine Learning Foundations
3 - Quantum Computing Foundations
4 - Quantum Algorithms & Optimization
15. Hands-On Lab 4.html
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11.4 KB
5 - Quantum Machine Learning (QML)
19. Hands-On Lab 5.html
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11.8 KB
6 - Real-World Applications of AI + Quantum
24. Hands-On Lab 6.html
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12.2 KB
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Machine Learning & Data Science with Python, Kaggle & Pandas
41. Introduction to Machine Learning with Real Hearth Attack Prediction Project
4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html
HTML
108 B
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1 - Introduction To The Course
2 - Setting Up The Environment
3 - Download Resources
4 - Zeppelin Basics
5 - Zeppelin With Apache Spark
6 - Machine Learning Project
7 - Introduction
8 - Download Resources
48 - Download Resources.html
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204 B
SourceCode
9 - Project Begins
59 - File Content.html
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22.9 KB
62 - Bonus Lecture.html
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8.3 KB
3 matching files, 68 total files.
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1 - Welcome to Introduction to Databricks
2 - Databricks Core Components
3 - Databricks Architecture
4 - Databricks in Action
5 - Machine Learning - Regression Analysis
22. Get historical Bitcoin data from here.html
HTML
204 B
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1 - Introduction to ML CFD Integration
2 - Fundamentals of Fluid Mechanics for ML Models
3 - Overview of Machine Learning in Physics Based Systems
4 - Synthetic Data Generation for ML CFD
17. Reduced physics CFD simulations (Description).html
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1.6 KB
18. Dataset design geometries flow regimes boundary diversity (Description).html
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1 KB
19. Voxelization of geometric inputs (256³ resolution) (Description).html
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1.6 KB
20. Ensuring physical consistency of synthetic data (Description).html
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1.4 KB
21. Data augmention strategies and bias mitigation (Description).html
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1.3 KB
22. Statical verification of dataset (Description).html
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1.3 KB
5 - Convolutional Neural Networks for CFD Prediction
23. CNN encoder decoder architectures (Description).html
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1.4 KB
24. Convolution in volumetric data (Description).html
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1.3 KB
25. Loss functions incorporating physical constraints (Description).html
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1.1 KB
26. Training workflow (Description).html
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819 B
27. Hyperparameter optimization (Description).html
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2.9 KB
28. Avoiding overfitting and promoting generalization (Description).html
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921 B
29. Implementation best practices (Description).html
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1.2 KB
13 matching files, 63 total files.
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1 - Introduction
Logistic Regression in Python
Data Files
Others
Presentation
10 - Knearest Neighbors Classifier
11 - Understanding The Results
12 - Appendix 1 Linear Regression In Python
ML Practical task 3
ML Task 1
ML Task 2
13 - Congratulations About Your Certificate
2 - Introduction To Machine Learning
3 - Basics Of Statistics
4 - Setting Up Python And Jupyter Notebook
5 - Important Python Libraries
24 - Python File For Additional Practice.html
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307 B
25 - About The Upcoming Role Play.html
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1.1 KB
6 - Data Preprocessing
30 - Project Exercise 1.html
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512 B
33 - Project Exercise 2.html
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204 B
36 - Project Exercise 3.html
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204 B
39 - Project Exercise 4.html
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204 B
43 - Project Exercise 5.html
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204 B
46 - Project Exercise 6.html
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204 B
7 - Classification Models
51 - Project Exercise 7.html
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307 B
55 - Project Exercise 8.html
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307 B
60 - Project Exercise 9.html
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204 B
8 - Linear Discriminant Analysis Lda
63 - Project Exercise 10.html
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204 B
9 - Testtrain Split
65 - More About Testtrain Split.html
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512 B
67 - Project Exercise 11.html
HTML
204 B
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[FreeTutorials.Us] Master Computer Vision OpenCV4 in Python with Deep Learning
1. Course Introduction and Setup
4. READ THIS - Guide to installing and setting up your OpenCV4.0.1 Virtual Machine.html
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2.6 KB
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[FreeTutorials.Us] Udemy - Feature Engineering for Machine Learning
1. Introduction
4. How to approach this course.html
HTML
1.76 KB
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4. Introduction to machine learning.html
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102 B
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Complete 2020 Data Science and Machine Learning Bootcamp [UdemyLibrary.com]
Course
1. Introduction to the Course
4. Top Tips for Succeeding on this Course.html
HTML
2.09 KB
4.1 App Brewery Cornell Notes Template.html
HTML
141 B
4. Introduction to Optimisation and the Gradient Descent Algorithm
25. Any Feedback on this Section.html
HTML
520 B
24. Download the Complete Notebook Here.html
HTML
242 B
7. Python Loops Coding Exercise.html
HTML
156 B
1.1 Course Resources.html
HTML
122 B
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Data Science Masterclass With R Machine Learning
2. INTRODUCTION TO DATA SCIENCE
4. Introduction to Machine Learning.html
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10.79 KB
4. INTRODUCTION TO R
2. Introduction to R.html
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8.2 KB
10. Introduction To Statistics
4. Introduction To Statistic - Part 2 STUDY NOTE.html
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8.79 KB
8. Part 4 Introduction To Statistic STUDY NOTE.html
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3.11 KB
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Complete 2020 Data Science & Machine Learning Bootcamp
Complete 2020 Data Science & Machine Learning Bootcamp
1. Introduction to the Course
4. Top Tips for Succeeding on this Course.html
HTML
2.09 KB
4.1 App Brewery Cornell Notes Template.html
HTML
141 B
4. Introduction to Optimisation and the Gradient Descent Algorithm
1.1 Course Resources.html
HTML
122 B
7. Python Loops Coding Exercise.html
HTML
149 B
24. Download the Complete Notebook Here.html
HTML
242 B
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[Tutorialsplanet.NET] Udemy - Master Computer Vision™ OpenCV4 in Python with Deep Learning
1. Course Introduction and Setup
4. READ THIS - Guide to installing and setting up your OpenCV4.0.1 Virtual Machine.html
HTML
2.6 KB
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Data Science Masterclass With R! 4 Projects+8 Case Studies
Data Science Masterclass With R! 4 Projects+8 Case Studies
2. INTRODUCTION TO DATA SCIENCE
4. Introduction to Machine Learning.html
HTML
10.79 KB
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