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3 - Introduction To Machine Learning.mp4
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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.mp4
MP4
23.5 MB
5 - 05_Resources & Getting Started
5 - 05_Resources & Getting Started.mp4
MP4
24.9 MB
6 - Assignment Getting Started with Your AI Learning Journey
7 - S02L01 - What is Artificial Intelligence
7 - S02L01 - What is Artificial Intelligence.mp4
MP4
32.4 MB
8 - S02L02 - History of AI
8 - S02L02 - History of AI.mp4
MP4
30 MB
9 - S02L03 - Types of AI
9 - S02L03 - Types of AI.mp4
MP4
26.5 MB
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1 - Introduction to Machine Learning
3. ML vs Traditional Programming.mp4
MP4
5.1 MB
10 - Feature Engineering
11 - Saving & Loading Models
12 - Practical ML Projects
13 - Deployment Basics
52. Deployment Workflow 3. Test Integration.mp4
MP4
5.4 MB
14 - Best Practices & ML Workflow
15 - Interview Preparation
2 - Python for Machine Learning
3 - ML Libraries in Python
10. ML Libraries in Python.mp4
MP4
10.6 MB
11. NumPy Basics.mp4
MP4
4.7 MB
12. Pandas Basics.mp4
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3.8 MB
13. Scikit-Learn basics.mp4
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3.5 MB
14. Matplotlib & Seaborn basics.mp4
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3.4 MB
15. SciPy basics.mp4
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2.7 MB
4 - Data Handling & Preprocessing
16. Loading & Inspecting Data.mp4
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35.8 MB
17. Handling Missing Values & Feature Engineering.mp4
MP4
25.8 MB
18. Train-Test Split & Scaling Normalization.mp4
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19.2 MB
5 - Exploratory Data Analysis (EDA)
19. Understanding Your Data.mp4
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9.4 MB
20. Summary Statistics.mp4
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10.7 MB
21. Data Visualisation Basics.mp4
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19.3 MB
22. Scatter Plots & Bar Charts.mp4
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13.4 MB
23. Correlation Analysis.mp4
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19.1 MB
24. Distribution Plots.mp4
MP4
8.6 MB
6 - Supervised Learning - Regression
25. Understanding Regression Simple Linear Regression & Multiple Linear Regression.mp4
MP4
31.4 MB
26. Model Training & Evaluation.mp4
MP4
27.4 MB
27. MAE(Mean Absolute Error), MSE(Mean Squared Error), RMSE(Root Mean Squared Error).mp4
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10.9 MB
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Introduction to Artificial Intelligence
6 - Machine Learning Algorithms
3. K-means clustering.mp4
MP4
10.42 MB
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1 - Course Introduction
2 - What It Means For a Machine to Learn
3 - Linear Regression Learning From Error
11 - Overfitting and Underfitting.mp4
MP4
290.5 MB
8 - Linear Regression Predicting Numbers.mp4
MP4
202.8 MB
9 - Loss Functions Measuring Error.mp4
MP4
254.2 MB
4 - Logistic Regression Probabilities and Decisions
12 - Logistic Regression Predicting Classes.mp4
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230.6 MB
13 - Probabilities and Decision Thresholds.mp4
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75.5 MB
14 - Confusion Matrices and Classification Metrics.mp4
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171.8 MB
5 - k-Nearest Neighbors Learning By Similarity
16 - k-Nearest Neighbors Distance-Based Learning.mp4
MP4
255.2 MB
18 - Feature Scaling and Why It Matters.mp4
MP4
141.7 MB
6 - Decision Trees Learning Rules
20 - Decision Trees Learning Rules.mp4
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215.4 MB
21 - Tree Depth and Model Complexity.mp4
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178.4 MB
23 - Cross-Validation Testing Model Stability.mp4
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129.8 MB
7 - Random Forests Learning With Many Models
25 - Random Forests Learning With Many Models.mp4
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272.4 MB
27 - Bias Vs. Variance.mp4
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190.5 MB
8 - Course Conclusion
28 - Congratulations! And Next Steps.mp4
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148.2 MB
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1 - Introduction to Data Science and Python
2 - Python Fundamentals for Data Science
3 - Working with Data Using Pandas
4 - Numerical Computing with NumPy
5 - Data Visualization
6 - Exploratory Data Analysis (EDA)
7 - Introduction to Statistics for Data Science
8 - Machine Learning with Scikit Learn
36. What is Machine Learning.mp4
MP4
38.7 MB
37. Supervised vs Unsupervised Learning.mp4
MP4
72.9 MB
38. ML Workflow (Prepare Train Evaluate Tune).mp4
MP4
77.2 MB
39. Regression Algorithms Linear Ridge Lasso.mp4
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108.8 MB
40. Classification Algorithms Logistic Regression KNN Decision Trees.mp4
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105.6 MB
41. Clustering K Means.mp4
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40.2 MB
9 - Advanced Topics (Optional Bonus)
42. Introduction to Time Series Analysis.mp4
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85.6 MB
43. Intro to Deep Learning with TensorFlow Keras.mp4
MP4
73.2 MB
44. Working with Big Data (Intro to PySpark).mp4
MP4
83.3 MB
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1 - Introduction To Ai Cybersecurity
2 - Generative Ai Security Automation
3 - Aipowered Threat Detection
4 - Machine Learning For Cybersecurity
10 - Machine Learning Fundamentals For Security.mp4
MP4
130.2 MB
11 - Deep Learning For Cyber Defense.mp4
MP4
115.9 MB
12 - Aibased Siem Soc Systems.mp4
MP4
84.1 MB
5 - Ethical Hacking Ai Attacks
13 - Ai For Ethical Hacking.mp4
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140.7 MB
14 - Ai Security Risks Model Vulnerabilities.mp4
MP4
180.1 MB
15 - Deepfakes Voice Cloning Social Engineering.mp4
MP4
123 MB
6 - Ai Cloud Future Cybersecurity
16 - Ai In Cloud Security.mp4
MP4
136 MB
17 - Ai Agents For Cybersecurity.mp4
MP4
147.9 MB
18 - Quantum Security Future Threats.mp4
MP4
148 MB
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1 - Quick Start
2 - Essential Web Fundamentals
3 - First Practical Project
4 - Learning to Program with Javascript
5 - Introduction to Back-End
34. Creating a Server in your machine.mp4
MP4
209 MB
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1 - Introduction
10 - Project Customer Segmentation using Clustering
11 - Project Fake News Detection using machine learning Python
12 - Project COVID19 Coronavirus Infection Probability using machine learning
13 - Miscellaneous
14 - Python Crash Course
2 - Installation and Colab
3 - Python for Data Analysis Numpy
4 - Python for Data Analysis Pandas
5 - Python for Data Visualization Matplotlib
6 - Supervised Machine Learning
29 - how to implement Logistic Regression.mp4
MP4
26.3 MB
34 - Introduction to Naive Bayes Classifier for Text Classification.mp4
MP4
12.1 MB
36 - Introduction to Decision Tree.mp4
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33.4 MB
7 - UnSupervised Machine Learning
8 - Reinforcement Learning
51 - Introduction to Reinforcement Learning.mp4
MP4
38.7 MB
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1 - Introduction to Machine Learning
3 - 3 Traditional Programming vs Machine Learning.mp4
MP4
21.9 MB
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
10 - 10 Introduction to iPython Environment.mp4
MP4
51.6 MB
6 - 6 The Machine Learning Pipeline Data Collection.mp4
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43.8 MB
7 - 7 Importance of Data Prepocessing.mp4
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17.9 MB
8 - 8 Importance of Feature Selection and Feature Engineering.mp4
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52.5 MB
9 - 9 The Machine Learning Terminologies.mp4
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36.5 MB
4 - Numpy Library
12 - 11Creating a numpy array.mp4
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49.6 MB
13 - 12 Processing the numpy arrays.mp4
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58.2 MB
14 - 13 Accessing Columns from Numpy Matrices.mp4
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18.6 MB
15 - 14 Statistical methods in Numpy.mp4
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56.2 MB
16 - 15 Matrix Operations in Numpy.mp4
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53.5 MB
17 - 16 Iterating through the numpy array.mp4
MP4
24.5 MB
5 - Pandas Library
18 - 17 An Intuition on Pandas Dataframe and Series.mp4
MP4
28.1 MB
19 - 18 Using numpy arrays to create Pandas Series.mp4
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28.8 MB
20 - 19 Using dictionary to create Pandas Series.mp4
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26.4 MB
21 - 20 Using a scalar to create Pandas Series.mp4
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10.5 MB
22 - 21 Series Processing.mp4
MP4
8.5 MB
23 - 22 Creating Pandas Dataframe from series.mp4
MP4
24.5 MB
24 - 23 Using lists of data to create a Pandas Dataframe.mp4
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21 MB
25 - 24 Another approach to create Dataframes.mp4
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21.6 MB
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1 - Python Fundamentals
2 - Data Manipulation with Pandas
3 - Data Visualization with Matplotlib & Seaborn
4 - Data Analysis with NumPy
5 - Introduction to Machine Learning
23. Machine Learning Concepts.mp4
MP4
55.4 MB
24. Supervised Learning Algorithms.mp4
MP4
131.8 MB
25. Unsupervised Learning Algorithms.mp4
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101.7 MB
26. Model Evaluation and Selection.mp4
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132.1 MB
6 - Working with APIs and Web Data
27. Introduction to APIs.mp4
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50.7 MB
28. Making API Requests.mp4
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52.3 MB
29. Parsing JSON and XML Data.mp4
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86.4 MB
30. Web Scraping Techniques.mp4
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90.3 MB
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1 - Introduction to AI & Quantum Computing
2 - AI & Machine Learning Foundations
3 - Quantum Computing Foundations
10. Working with Qiskit.mp4
MP4
126.5 MB
8. Understanding Quantum Computing.mp4
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122.7 MB
9. Quantum Gates & Circuits.mp4
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123.7 MB
4 - Quantum Algorithms & Optimization
12. Introduction to Quantum Algorithms.mp4
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137.1 MB
13. Practical Quantum Algorithms.mp4
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64 MB
14. Quantum Computing for Optimization.mp4
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122.2 MB
5 - Quantum Machine Learning (QML)
16. Introduction to Quantum Machine Learning.mp4
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145.5 MB
17. Building Hybrid AI-Quantum Models.mp4
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127.2 MB
18. Quantum Neural Networks.mp4
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134.8 MB
6 - Real-World Applications of AI + Quantum
20. Quantum AI in Industry.mp4
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132.7 MB
21. Quantum Security & Cryptography.mp4
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168 MB
22. AI Agents & Future Intelligent Systems.mp4
MP4
133.9 MB
23. AI + Quantum Projects.mp4
MP4
67.8 MB
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1 - Introduction
2 - Introduction to Quantum Computing
3 - Fundamentals of Quantum Programming
4 - Practical Learning of Amazon Braket
23. Quantum Machine Learning on Amazon Braket.mp4
MP4
66.2 MB
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3 -Introduction to Statistics and Machine learning in Data Science.mp4
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90.2 MB
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Machine Learning & Data Science with Python, Kaggle & Pandas
41. Introduction to Machine Learning with Real Hearth Attack Prediction Project
3. Notebook Design to be Used in the Project.mp4
MP4
104.96 MB
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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 - Introduction To Machine Learning.mp4
MP4
145.6 MB
4 - This Is A Milestone.mp4
MP4
70.7 MB
5 - Building A Machine Learning Model.mp4
MP4
27.7 MB
3 - Basics Of Statistics
11 - Measures Of Dispersion.mp4
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14.4 MB
6 - Types Of Data.mp4
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20.7 MB
7 - Types Of Statistics.mp4
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7.9 MB
8 - Describing Data Graphically.mp4
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61.5 MB
9 - Measures Of Centers.mp4
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29.8 MB
4 - Setting Up Python And Jupyter Notebook
13 - Installing Python And Anaconda.mp4
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15.3 MB
14 - Opening Jupyter Notebook.mp4
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54.7 MB
15 - Introduction To Jupyter.mp4
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36.4 MB
16 - Arithmetic Operators In Python Python Basics.mp4
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10.5 MB
17 - Strings In Python Python Basics.mp4
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81.4 MB
18 - Lists Part 1.mp4
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19 - Lists Part 2.mp4
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13.4 MB
20 - Tuples And Dictionaries.mp4
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5 - Important Python Libraries
21 - Working With Numpy Library Of Python.mp4
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52.9 MB
22 - Working With Pandas Library Of Python.mp4
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55.9 MB
23 - Working With Seaborn Library Of Python.mp4
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6 - Data Preprocessing
26 - Gathering Business Knowledge.mp4
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8.7 MB
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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
4 - Natural Language Processing (NLP) & Speech
5 - Generative AI Workloads & Large Language Models (LLMs)
6 - Implementation with Microsoft AI Foundry
52. Introduction to Microsoft AI Foundry The Unified Platform.mp4
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1 - Introduction To The Course
3 - Why Spark Mllib For Machine Learning Projects.mp4
MP4
8.1 MB
2 - Setting Up The Environment
3 - Download Resources
4 - Zeppelin Basics
5 - Zeppelin With Apache Spark
6 - Machine Learning Project
35 - Understanding Spark Imports For Ml.mp4
MP4
14.5 MB
36 - Loading Source Data In Spark.mp4
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23.1 MB
37 - Preparing Training Data.mp4
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24.9 MB
38 - Understanding Stringindexer In Spark.mp4
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39 - Defining The Pipeline In Spark Mllib.mp4
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40 - Split The Data.mp4
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41 - Using Vectorassembler To Prepare Training Data.mp4
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42 - Train A Regression Model In Spark.mp4
MP4
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43 - Prepare The Testing Data.mp4
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44 - Testing The Regression Model In Spark.mp4
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51.9 MB
45 - Evaluating The Regression Model In Spark.mp4
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46 - Evaluating Model Performance Using Rmse.mp4
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7 - Introduction
47 - Introduction.mp4
MP4
31.5 MB
8 - Download Resources
SourceCode
9 - Project Begins
49 - Introduction To Spark.mp4
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42.6 MB
50 - Old Free Account Creation In Databricks.mp4
MP4
7.7 MB
51 - New Free Account Creation In Databricks.mp4
MP4
8.7 MB
52 - Tips To Improve Your Course Taking Experience.mp4
MP4
3 MB
53 - Provisioning A Spark Cluster.mp4
MP4
10 MB
54 - Introduction To Machine Learning.mp4
MP4
35 MB
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Machine Learning Made Easy - Beginner to Advanced using R
Machine Learning Made Easy - Beginner to Advanced using R
1. Introduction to R
3. R Packages.mp4
MP4
11.06 MB
3. Basic Statistics and Graph
1. Introduction and Sampling.mp4
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6.41 MB
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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.mp4
MP4
320.9 MB
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1 - Introduction
2 - Machine Learning Paradigms & Bias-Variance Bounds
3 - Deep Neural Networks & Gradient Propagation Models
16 - Introduction to Alignment Drift.mp4
MP4
57 MB
4 - Natural Language Processing & Embedding Geometries
19 - Introduction to Reward Modeling.mp4
MP4
57.9 MB
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3. Introduction to Machine Learning.mp4
MP4
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[FreeTutorials.Us] Udemy - Mathematical Foundation For Machine Learning and AI
3. Multivariate Calculus
1. Introduction to Derivatives.mp4
MP4
143.3 MB
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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
65. Intro to Exercises on Conjugate Priors.mp4
MP4
16.9 MB
8 - Appendix FAQ Intro
9 - Setting Up Your Environment (FAQ by Student Request)
71. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4
MP4
192.3 MB
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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
23. Data Science Core Concepts.mp4
MP4
11.8 MB
24. ML Regression Data Prep.mp4
MP4
23.8 MB
25. Converting SQL table to Pyspark table.mp4
MP4
53.9 MB
26. Training Regression Model on Bitcoin Prices.mp4
MP4
29.3 MB
27. Displaying Bitcoin Prediction Price in Pan.mp4
MP4
34.3 MB
28. Importing Python Notebook into Databricks.mp4
MP4
8.3 MB
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1 - Introduction to ML CFD Integration
3. Emergence of machine learning in fluid mechanics.mp4
MP4
49 MB
2 - Fundamentals of Fluid Mechanics for ML Models
3 - Overview of Machine Learning in Physics Based Systems
12. Data driven vs Physics informed approches.mp4
MP4
55.5 MB
13. Neural network architectures for physical systems.mp4
MP4
41.2 MB
14. Proper orthogonal decomposition (POD) and reduced order models (ROMs).mp4
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46.5 MB
15. Embedding physical constraints in ML models.mp4
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72.7 MB
16. Case studies turbulence modelling heat transfer prediction.mp4
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49.5 MB
4 - Synthetic Data Generation for ML CFD
17. Reduced physics CFD simulations.mp4
MP4
53.4 MB
18. Dataset design geometries flow regimes boundary diversity.mp4
MP4
65.2 MB
19. Voxelization of geometric inputs (256³ resolution).mp4
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60.7 MB
20. Ensuring physical consistency of synthetic data.mp4
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58.5 MB
21. Data augmention strategies and bias mitigation.mp4
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59.8 MB
22. Statical verification of dataset.mp4
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59.9 MB
5 - Convolutional Neural Networks for CFD Prediction
23. CNN encoder decoder architectures.mp4
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33.3 MB
24. Convolution in volumetric data.mp4
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51.1 MB
25. Loss functions incorporating physical constraints.mp4
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52.2 MB
26. Training workflow.mp4
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23.7 MB
27. Hyperparameter optimization.mp4
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23.2 MB
28. Avoiding overfitting and promoting generalization.mp4
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44.6 MB
29. Implementation best practices.mp4
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57.2 MB
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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
1. Introduction to mobile price prediction.mp4
MP4
37.5 MB
2. Introduction to AutoML
3. Introduction to Auto Keras.mp4
MP4
31.06 MB
2 matching files, 72 total files.
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3. Introduction to Machine Learning.mp4
MP4
37.6 MB
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01 - Introduction
02 - 1. An Overview of Full-Stack Deep Learning
03 - 2. MLOps with MLflow
04 - 3. Model Training and Evaluation Using MLflow
05 - 4. Hyperparameter Tuning with Optuna
06 - 5. Model Deployment and Predictions
02 - Workaround to get model artifacts on local machine.mp4
MP4
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1 - Introduction to Machine Learning
3 - Traditional Programming vs Machine Learning Approach.mp4
MP4
40.7 MB
2 - Data and Feature Engineering
3 - Supervised Learning Core Concepts
10 - Regression Algorithms Concepts Only.mp4
MP4
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11 - Classification Algorithms Concepts Only.mp4
MP4
40.9 MB
12 - Evaluating Supervised Models.mp4
MP4
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9 - Introduction to Supervised Learning.mp4
MP4
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4 - Unsupervised Learning Discovering Hidden Patterns
13 - Introduction to Unsupervised Learning.mp4
MP4
51 MB
14 - Clustering Techniques.mp4
MP4
28.1 MB
15 - Dimensionality Reduction.mp4
MP4
33.6 MB
5 - Reinforcement Learning Conceptual Overview
16 - Introduction to Reinforcement Learning.mp4
MP4
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17 - Core Concepts in RL.mp4
MP4
24.9 MB
18 - Popular RL Algorithms Conceptual.mp4
MP4
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6 - Model Evaluation and Improvement
19 - Train Validation and Test Splits.mp4
MP4
34.7 MB
20 - CrossValidation Concept.mp4
MP4
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21 - Model Optimization and Hyperparameter Tuning.mp4
MP4
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22 - Bias and Variance Revisited.mp4
MP4
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7 - Deep Learning Conceptual Foundations
23 - What is Deep Learning.mp4
MP4
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24 - Artificial Neural Networks The Concept.mp4
MP4
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25 - Learning Process in Neural Networks.mp4
MP4
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26 - Applications of Deep Learning.mp4
MP4
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8 - Machine Learning Project Lifecycle
27 - Steps of a Machine Learning Project.mp4
MP4
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[FreeAllCourse.Com] Udemy - Python for Data Science and Machine Learning Bootcamp
14. Introduction to Machine Learning
3. Supervised Learning Overview.mp4
MP4
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10 Python Courses For Beginners 2020 [UdemyLibrary.com]
Machine Learning Crash Course for Beginners using Python [UdemyLibrary.com]
1. Introduction to the Course
3. Python Practical of the Course.mp4
MP4
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11. Deep Learning Overview
3. Introduction to Recurrent Neural Networks (CNN).mp4
MP4
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[Tutorialsplanet.NET] Udemy - Python for Data Science and Machine Learning Bootcamp
14. Introduction to Machine Learning
3. Introduction to Machine Learning.mp4
MP4
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3. Introduction to Machine Learning.mp4
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videos
Neural Networks for Machine Learning 8.3 Introduction to the full Bayesian approach.mp4
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14. Introduction to Machine Learning
3. Supervised Learning Overview.mp4
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Python for Data Science
[TutsNode.com] - Python for Data Science
1. Introduction to Machine Learning and It’s Technologies
3. Segment - 04-doing-data-science.mp4
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[Tutorialsplanet.NET] Udemy - The Complete Python 3 Course Beginner to Advanced!
14. Project #10 - Speech Recognition & AI
1. Introduction to machine learning.mp4
MP4
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1 - Introduction to AI in Insurance (InsurTech Overview)
10 - Insurance AI Architecture & Implementation Strategy
11 - Capstone Case Studies & Future of InsurTech
2 - Foundations – Insurance Data, AI & Machine Learning Basics
3 - AI-Driven Underwriting Automation
3. AI-Driven Underwriting Automation.mp4
MP4
111.2 MB
4 - Claims Processing Automation Using Computer Vision & NLP
4. Claims Processing Automation Using Computer Vision & NLP.mp4
MP4
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5 - Fraud Detection in Insurance Claims Using AI
5. Fraud Detection in Insurance Claims Using AI.mp4
MP4
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6 - Dynamic Pricing Models Using Real-Time Data
6. Dynamic Pricing Models Using Real-Time Data.mp4
MP4
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7 - Usage-Based Insurance (UBI) Powered by IoT & AI
7. Usage-Based Insurance (UBI) Powered by IoT & AI.mp4
MP4
131.9 MB
8 - AI for Customer Experience & Personalization
8. AI for Customer Experience & Personalization.mp4
MP4
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9 - AI Governance, Ethics, Bias & Regulation in Insurance
9. AI Governance, Ethics, Bias & Regulation in Insurance.mp4
MP4
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1 - Introduction
2 - Machine Learning Paradigms & Bias-Variance Bounds
3 - Deep Neural Networks & Gradient Propagation Models
17. Introduction to Classifier-Free Guidance.mp4
MP4
66.7 MB
4 - Natural Language Processing & Embedding Geometries
20. Introduction to Forward-Backward Noise Injection.mp4
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003 2.3 Introduction to Machine Learning.mp4
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Udemy - Complete Machine Learning and Data Science Zero to Mastery
6. Pandas Data Analysis
3. Pandas Introduction.mp4
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1 - Introduction
10 - Methods and Techniques for the Testing of AI-Based Systems
11 - Test Environments for AI-Based Systems
12 - Using AI for Testing
14 - Sample exam
2 - Introduction to AI
3 - Quality Characteristics for AI-Based Systems
4 - Machine Learning (ML) – Overview
20. Forms of ML.mp4
MP4
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21. ML Workflow.mp4
MP4
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22. Selecting a Form of ML.mp4
MP4
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23. Factors Involved in ML Algorithm Selection.mp4
MP4
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24. Overfitting and Underfitting.mp4
MP4
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5 - ML - Data
25. Data Preparation as Part of the ML Workflow.mp4
MP4
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26. Challenges in Data Preparation.mp4
MP4
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27. Training, Validation and Test Datasets in the ML Workflow.mp4
MP4
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28. Dataset Quality Issues.mp4
MP4
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29. Data Quality and its Effect on the ML Model.mp4
MP4
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30. Data Labelling for Supervised Learning.mp4
MP4
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6 - ML Functional Performance Metrics
31. Confusion Matrix.mp4
MP4
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32. ML Performance Metrics (Classification, Regression, Clustering).mp4
MP4
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33. Limitations of ML Functional Performance Metrics.mp4
MP4
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34. Selecting ML Functional Performance Metrics.mp4
MP4
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35. Benchmark Suites for ML.mp4
MP4
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7 - ML - Neural Networks and Testing
36. Neural Networks.mp4
MP4
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37. Coverage Measures for Neural Networks.mp4
MP4
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8 - Testing AI-Based Systems Overview
38. Specification of AI-Based Systems.mp4
MP4
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39. Test Levels for AI-Based Systems.mp4
MP4
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17 - PART #3 MACHINE AND DEEP LEARNING IN FINANCE
001 Introduction to Part #3 Machine and Deep Learning in Finance.mp4
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Learn Machine Learning 10 Projects In Finance & Health Care
[TutsNode.com] - Learn Machine Learning 10 Projects In Finance & Health Care
3. Mortality Prediction In ICU Using ANN
1. Introduction to mortality prediction.mp4
MP4
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Unsupervised Machine Learning Hidden Markov Models in Python
[TutsNode.com] - Unsupervised Machine Learning Hidden Markov Models in Python
1. Introduction and Outline
3. Where to get the Code and Data.mp4
MP4
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Complete 2020 Data Science and Machine Learning Bootcamp [UdemyLibrary.com]
Course
4. Introduction to Optimisation and the Gradient Descent Algorithm
11. How to Create 3-Dimensional Charts.mp4
MP4
193.48 MB
3. Introduction to Cost Functions.mp4
MP4
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9. Introduction to Neural Networks and How to Use Pre-Trained Models
3. Costs and Disadvantages of Neural Networks.mp4
MP4
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Data Science Masterclass With R Machine Learning
2. INTRODUCTION TO DATA SCIENCE
3. Introduction to Machine Learning.mp4
MP4
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4. INTRODUCTION TO R
3. Setting up R.mp4
MP4
124.3 MB
10. Introduction To Statistics
3. Intro To Stat - Part 2.mp4
MP4
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5. Intro To Stat - Part 3.mp4
MP4
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[FreeAllCourse.Com] Udemy - Python for Data Science and Machine Learning Bootcamp
14. Introduction to Machine Learning
3. Supervised Learning Overview.mp4
MP4
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25. Neural Nets and Deep Learning
3. Introduction to Artificial Neural Networks (ANN).mp4
MP4
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Complete 2020 Data Science & Machine Learning Bootcamp
Complete 2020 Data Science & Machine Learning Bootcamp
4. Introduction to Optimisation and the Gradient Descent Algorithm
3. Introduction to Cost Functions.mp4
MP4
66.2 MB
11. How to Create 3-Dimensional Charts.mp4
MP4
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9. Introduction to Neural Networks and How to Use Pre-Trained Models
3. Costs and Disadvantages of Neural Networks.mp4
MP4
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[DesireCourse.Net] Udemy - Cluster Analysis and Unsupervised Machine Learning in Python
1. Introduction to Unsupervised Learning
3. Why Use Clustering.mp4
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