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4. Matplotlib Introduction.mp4
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1 - Introduction
10 - Fundamentals of AI Agents
11 - Building Your First Agentic AI Agent
12 - LangChain for Agent Development – Industry tool
13 - RAG - Retrieval-Augmented Generation
14 - Practical AI Prompts for Real-World Projects
15 - Deployment & Scaling – Production-ready skills
2 - Agentic AI Fundamentals
3 - Mastering the Future Essential AI Tools for Every Professional
4 - Generative AI Basic concepts
5 - Setting up and Exploring the Power of ChatGPT
6 - Python Setting up and Local Development
24. Installing libraries numpy pandas matplotlib.mp4
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14.4 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
22. Plotting with Matplotlib.mp4
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102.1 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
15 - Matplotlib Introduction.mp4
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45.5 MB
16 - Matplotlib Bar Graphs.mp4
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24.9 MB
17 - Matplotlib Histogram.mp4
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15.6 MB
18 - Matplotlib Scatter Plot.mp4
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18.9 MB
19 - Matplotlib Area Plot.mp4
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16.2 MB
20 - Matplotlib Pie Chart.mp4
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9.8 MB
21 - Matplotlib Subplots.mp4
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12.7 MB
6 - Supervised Machine Learning
22 - What is Supervised Machine Learning and Linear Regression Algorithm.mp4
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42 MB
23 - Implementation of Linear Regression.mp4
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42 MB
24 - Plot Linear Regression.mp4
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28.9 MB
25 - Implementation of Multi Linear Regression.mp4
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29 MB
26 - Example 2 Multilinear Regression.mp4
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28.7 MB
27 - Use Case Boston Housing Price Prediction.mp4
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82.3 MB
28 - Save Model.mp4
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36.3 MB
29 - how to implement Logistic Regression.mp4
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26.3 MB
30 - Logistic Regression on Iris Flower Dataset.mp4
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58.8 MB
31 - Logistic Regression on Digits Dataset.mp4
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43.3 MB
32 - Naive Bayes Classifier.mp4
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33.6 MB
33 - Implementation of Naive Bayes classifier on Wine Dataset.mp4
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45.6 MB
34 - Introduction to Naive Bayes Classifier for Text Classification.mp4
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12.1 MB
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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
4 - Numpy Library
5 - Pandas Library
6 - Analysis of Datasets using Pandas and Matplotlib Library
27 - 26 Loading the dataset Important.mp4
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32.3 MB
28 - 27 Analysis of Datasets I.mp4
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42 MB
29 - 28 Analysis of Datasets by Plotting II.mp4
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71.1 MB
7 - The Scikitlearn Library and Preprocessing Techniques
30 - 29 Working with Iris Dataset from sklearn.mp4
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194.1 MB
31 - 30 Binarization.mp4
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43.2 MB
32 - 31 Feature Scaling.mp4
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46.9 MB
8 - Supervised Learning Linear Regression
33 - 32 Analysis of Linear Regression.mp4
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73.8 MB
34 - Use of Gradient Descent Optimizer.mp4
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33 MB
35 - The Gradient Descent Optimizer Algorithm.mp4
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81.1 MB
36 - 33 Demand vs Price Problem to understand Linear Regression.mp4
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71.3 MB
37 - 34 Implementation of Linear Regression I.mp4
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75.8 MB
38 - 35 Implementation of Linear Regression II.mp4
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48.2 MB
39 - 36 Visualizing the LBF using matplotlib.mp4
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23.3 MB
9 - Logistic Regression for Classification Problems
40 - 37 Why does Linear Regression fail for a classification problem.mp4
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39.1 MB
41 - 38 The Sigmoid function in Logistic Regression.mp4
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24 MB
42 - 39 The Confusion Matrix.mp4
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60.1 MB
43 - 40 Implementation of Logistic Regression I.mp4
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150.3 MB
44 - 41 Creating an heatmap of the confusion matrix.mp4
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22.1 MB
18 matching files, 146 total files.
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1 - Python Fundamentals
2 - Data Manipulation with Pandas
3 - Data Visualization with Matplotlib & Seaborn
4 - Data Analysis with NumPy
19. Introduction to NumPy Arrays.mp4
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98.7 MB
5 - Introduction to Machine Learning
23. Machine Learning Concepts.mp4
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55.4 MB
24. Supervised Learning Algorithms.mp4
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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
9 matching files, 32 total files.
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1 - Introduction
2 - Python Basic
3 - Python Object Oriented Programming
4 - Python with Data analysis
17 - Matplotlib.mp4
MP4
39.2 MB
1 matching file, 28 total files.
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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
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
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192.3 MB
1 matching file, 168 total files.
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4. Matplotlib Introduction.mp4
MP4
83.6 MB
1 matching file, 225 total files.
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  • Age: 7 years ago
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