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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
3 - 03_Course Roadmap_What you will learn (Description).html
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921 B
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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8 - S02L02 - History of AI
8 - S02L02 - History of AI (Description).html
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9 - S02L03 - Types of AI
9 - S02L03 - Types of AI (Description).html
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1 - Introduction
2 - Python 3 (Optional)
3 - Installation of OS
4 - Circuit
5 - GPIO (Basic)
6 - GPIO (Intermediate)
7 - Image Processing (OpenCV 4)
8 - Machine Learning (Object Detection)
9 - Other Courses
59. Other Courses.html
HTML
6.3 KB
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1 - Course Introduction
2 - What It Means For a Machine to Learn
3 - Linear Regression Learning From Error
1 - Foundations of Supervised Learning.html
HTML
24.7 KB
10 - How to Linear Regression and Evaluation.html
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12.9 KB
11 - Overfitting and Underfitting (Description).html
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2.3 KB
8 - Linear Regression Predicting Numbers (Description).html
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2.3 KB
9 - Loss Functions Measuring Error (Description).html
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2.4 KB
4 - Logistic Regression Probabilities and Decisions
12 - Logistic Regression Predicting Classes (Description).html
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13 - Probabilities and Decision Thresholds (Description).html
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14 - Confusion Matrices and Classification Metrics (Description).html
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15 - How to Logistic Regression and Evaluation.html
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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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19 - How to KNN and Feature Scaling.html
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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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23 - Cross-Validation Testing Model Stability (Description).html
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2 KB
24 - How to Cross-Validation.html
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1 - AI Simplified for Everyone - Introduction
10 - Course wrap-up & AI Journey ahead
2 - AI Terminology & Concepts
3. Machine Learning (Description).html
HTML
1.2 KB
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1 - Introduction to Machine Learning
10 - Support Vector Machines
11 - K Nearest Neighbors for Classification and Regression
12 - Decision Tree Classifier Algorithm
13 - Random Forest Classifier Algorithm
14 - Naive Bayes Algorithm
15 - Resources
Notebooks
16 - KMeans Clustering
2 - Types of Machine Learning
3 - The Machine Learning Pipeline
11 - Important Libraries in Python.html
HTML
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1 - Introduction to AI & Quantum Computing
2 - AI & Machine Learning Foundations
3 - Quantum Computing Foundations
11. Hands-On Lab 3.html
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12.9 KB
4 - Quantum Algorithms & Optimization
15. Hands-On Lab 4.html
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5 - Quantum Machine Learning (QML)
19. Hands-On Lab 5.html
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6 - Real-World Applications of AI + Quantum
24. Hands-On Lab 6.html
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1 - Introduction
2 - Data Handling with Pandas in Python
3 - Data Visualization in Python
4 - Data summary descriptive and inferential statistic
5 - Data Management for Machine Learning in Python
19. Python Libraries for Machine Learning Dtata management (Description).html
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20. Define Class and Feature Variables in Python for machine learning (Description).html
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21. Drop Missing Values (Description).html
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22. Prepare the Class and Features (Description).html
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23. Create Training and Testing Data (Description).html
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24. Data Preprocessing in Python (Description).html
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6 - Classification Models in Python - LR, DT, KNN, SVM, RF
25. Libraries for Classification Models in Python (Description).html
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26. Logistic Regression Classifier in Python (Description).html
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27. Random Forest Classifier in Python (Description).html
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28. Build Multiple Classification Models in Python (Description).html
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Machine Learning & Data Science with Python, Kaggle & Pandas
2. NumPy Library Introduction
3. Quiz.html
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205 B
3. Creating NumPy Array in Python
10. Quiz.html
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205 B
7. Pandas Library Introduction
3. Quiz.html
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205 B
15. Pivot Tables in Pandas Library
3. Quiz.html
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205 B
17. First Contact with Machine Learning
3. Machine Learning Project Files.html
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254 B
33. Recommender System Algorithm in Machine Learning A-Z
3. Quiz.html
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205 B
35. Competition Section on Kaggle
3. Quiz.html
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205 B
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UDEMY - INTRODUCTION AU MACHINE LEARNING 2017.FR.WEB-DL.720P.H264
3. ---------------------------- Partie 2 - Régression ----------------------------
1. Bienvenue à la Partie 2 - Régression.html
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8. -------------------------- Partie 3 - Classification --------------------------
1. Bienvenue à la Partie 3 - Classification.html
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3. Titanic - Machine Learning from Disaster Project Notebook.html
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Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science
3. -------------------- Part 2 Regression --------------------
1. Welcome to Part 2 - Regression.html
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11. -------------------- Part 3 Classification --------------------
1. Welcome to Part 3 - Classification.html
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19. Evaluating Classification Models Performance
6. Conclusion of Part 3 - Classification.html
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3. Introduction to Machine learning quiz.html
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3. Introduction to Machine learning quiz.html
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3. FAQ about Complete data science with R, deep learning, machine learning.html
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1 - The Modern AI Landscape & Ethical Frameworks
10 - Practice Test
2 - Fundamental Principles of Machine Learning on Azure
3 - Computer Vision Workloads in Azure
3. Quiz.html
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20.4 KB
4 - Natural Language Processing (NLP) & Speech
4. Quiz.html
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20.4 KB
5 - Generative AI Workloads & Large Language Models (LLMs)
5. Quiz.html
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20.5 KB
6 - Implementation with Microsoft AI Foundry
6. Quiz.html
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20.3 KB
7 - Agentic AI & The Future of Autonomy
7. Quiz.html
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20.6 KB
8 - Document Intelligence & Specialized AI
8. Quiz.html
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20.5 KB
9 - Final Preparation Strategy
9. Quiz.html
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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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SourceCode
9 - Project Begins
59 - File Content.html
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62 - Bonus Lecture.html
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Machine Learning Made Easy - Beginner to Advanced using R
Machine Learning Made Easy - Beginner to Advanced using R
3. Basic Statistics and Graph
6. Basic Statistics and graph quiz.html
HTML
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1 - Introduction to AI in Market Strategy
2 - Core AI Technologies and Tools
3. Understanding AI, Machine Learning, and Data Analytics (Description).html
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1 - Introduction
3. Machine Learning Concepts Quick Reference.html
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1 - Introduction
3. The Supervised Machine Learning Model (Description).html
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3. Introduction to Machine Learning Quiz.html
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[FreeCoursesOnline.Me] UDACITY - Machine Learning Engineer Nanodegree v4.0.0
Part 02-Module 01-Lesson 02_Software Engineering Practices Pt I
23. Scenario #3.html
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12.01 KB
Part 04-Module 03-Lesson 01_Get Quick Feedback on Your GitHub Profile
14. Interview with Art - Part 3.html
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7.94 KB
Part 09-Module 01-Lesson 01_Web Development
26. Flask+Plotly+Pandas Part 3.html
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1 - Introduction and Outline
10 - Extra Help With Python Coding for Beginners (FAQ by Student Request)
11 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)
12 - Appendix FAQ Finale
2 - The High-Level Picture
3 - Bayes Rule and Probability Review
4 - Traditional A B Testing
5 - Bayesian A B Testing
6 - Bayesian A B Testing Extension
7 - Practice Makes Perfect
1. Bayes Rule & Probability Review Quiz.html
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2. Traditional A B Testing Quiz.html
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3. Adaptive Methods Quiz.html
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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
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1 - Introduction to ML CFD Integration
3. Emergence of machine learning in fluid mechanics (Description).html
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1.2 KB
2 - Fundamentals of Fluid Mechanics for ML Models
3 - Overview of Machine Learning in Physics Based Systems
12. Data driven vs Physics informed approches (Description).html
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13. Neural network architectures for physical systems (Description).html
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14. Proper orthogonal decomposition (POD) and reduced order models (ROMs) (Description).html
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15. Embedding physical constraints in ML models (Description).html
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16. Case studies turbulence modelling heat transfer prediction (Description).html
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4 - Synthetic Data Generation for ML CFD
17. Reduced physics CFD simulations (Description).html
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18. Dataset design geometries flow regimes boundary diversity (Description).html
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19. Voxelization of geometric inputs (256³ resolution) (Description).html
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20. Ensuring physical consistency of synthetic data (Description).html
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21. Data augmention strategies and bias mitigation (Description).html
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22. Statical verification of dataset (Description).html
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5 - Convolutional Neural Networks for CFD Prediction
23. CNN encoder decoder architectures (Description).html
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24. Convolution in volumetric data (Description).html
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25. Loss functions incorporating physical constraints (Description).html
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26. Training workflow (Description).html
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27. Hyperparameter optimization (Description).html
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28. Avoiding overfitting and promoting generalization (Description).html
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29. Implementation best practices (Description).html
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Data Science Practical Automated Machine Learning(Auto ML)
[TutsNode.com] - Data Science Practical Automated Machine Learning(Auto ML)
3. Auto Sklearn Part 1
2. Download the project files.html
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1 - Pandas
10 - Lets apply what we learned Machine Learning Project Classification
11 - Lets apply what we learned Machine Learning Project Regression
12 - Bonus Section
2 - Numpy
3 - Feature Engineering
4 - Evaluation of the Model Performances
5 - Machine Learning Supervised vs Unsupervised
6 - Data Set Analysis Feature Engineering for Regression Tasks
17 - Data Set.html
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7 - Data Set Analysis Feature Engineering for Classification Tasks
20 - Data Set.html
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6. More Data Mining and Machine Learning Techniques
6.3 Python Markov Decision Process Toolbox.html
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8. Apache Spark Machine Learning on Big Data
3.1 winutils.exe.html
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1 - First Contact With Machine Learning
3 - Machine Learning Project Files.html
HTML
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10 - Hyperparameter Optimization
11 - Decision Tree Algorithm In Machine Learning Az
12 - Random Forest Algorithm In Machine Learning Az
13 - Support Vector Machine Algorithm In Machine Learning Az
14 - Unsupervised Learning With Machine Learning
15 - K Means Clustering Algorithm In Machine Learning Az
16 - Hierarchical Clustering Algorithm In Machine Learning Data Science
17 - Principal Component Analysis Pca In Machine Learning Az
18 - Recommender System Algorithm In Machine Learning Az
19 - Extra
2 - Installations For Python
3 - Evaluation Metrics In Machine Learning
3 - Machine Learning Az Quiz.html
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[DesireCourse.Net] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science
11. -------------------- Part 3 Classification --------------------
1. Welcome to Part 3 - Classification.html
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19. Evaluating Classification Models Performance
6. Conclusion of Part 3 - Classification.html
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3. -------------------- Part 2 Regression --------------------
1. Welcome to Part 2 - Regression.html
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3 - Machine Learning and Data Science Framework
26 - Overfitting and Underfitting Definitions.html
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29 - Optional Elements of AI.html
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1 - Introduction
10 - PyTorch Cybersecurity
11 - Splunk for Cybersecurity AI
12 - Presidential protection
13 - VirtualBox in Cybersecurity
2 - Understand AI CyberSecurity
3 - ChatGPT Cybersecurity
4 - Gemini Cybersecurity
5 - Tools and Techniques
6 - Deep Learning Machine Learning
15. Understand TensorFlow (Description).html
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16. Machine Learning Deep Learning (Description).html
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17. Deep Neural Networks (Description).html
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18. Building Training a Deep Learning Model (Description).html
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19. Phishing Email System (Description).html
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7 - Python for Cybersecurity AI
20. Understand Python (Description).html
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21. Defend Against DDoS Attacks with Python (Description).html
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8 - Elasticsearch with Cybersecurity AI
22. Elasticsearch Cybersecurity (Description).html
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921 B
9 - Amazon GuardDuty with Cybersecurity
23. Amazon GuardDuty (Description).html
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819 B
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Building Machine Learning Systems That Don't Suck (3).html
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Excel VBA programming by Examples (MS Excel 2016)
[TutsNode.com] - Excel VBA programming by Examples
4. Special queries
3. Bonus Topic - Analytics Data Science Machine Learning Interview questions.html
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[FreeAllCourse.Com] Udemy - Python for Data Science and Machine Learning Bootcamp
1. Course Introduction
3. Course FAQs.html
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3. Jupyter Overview
1. Updates to Notebook Zip.html
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197 B
3.1 Updated Link to Manage Environments with Conda.html
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14. Introduction to Machine Learning
1.3 Slides for this section to view (please do not request edit access).html
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3. Zero-Day Vulnerability Detection with Machine Learning Advanced Ethical Hacking.html
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[Tutorialsplanet.NET] Udemy - Python for Data Science and Machine Learning Bootcamp
1. Course Introduction
3. Course FAQs.html
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5.34 KB
14. Introduction to Machine Learning
3.1 Link for ISLR.html
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123 B
3. Jupyter Overview
1. Updates to Notebook Zip.html
HTML
197 B
3.1 Updated Link to Manage Environments with Conda.html
HTML
144 B
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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
10 - Practice Exercise 1.html
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307 B
12 - Practice Exercise 2.html
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307 B
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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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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39 - Project Exercise 4.html
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43 - Project Exercise 5.html
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46 - Project Exercise 6.html
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7 - Classification Models
51 - Project Exercise 7.html
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55 - Project Exercise 8.html
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60 - Project Exercise 9.html
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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
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204 B
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2. Set-Up Docker on Local Machine
3. Course Chat with Students Learning DevOps.html
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[FreeTutorials.Us] Master Computer Vision OpenCV4 in Python with Deep Learning
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[FreeAllCourse.Com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science
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[FreeTutorials.Us] Udemy - Feature Engineering for Machine Learning
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Udemy - Introduction au Machine Learning - 2017
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