Python Bootcamp

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Python Bootcamp (Size: 3.8 GB)
  1 - About your certificate.html 921.6 B
  1 - Bonus Lecture.html 9.1 KB
  1 -Defining and Using Functions.mp4 70.7 MB
  1 -For Loops in Python.mp4 76.9 MB
  1 -Getting Started with Matplotlib Library.mp4 39.5 MB
  1 -Introduction to Conditional Statements.mp4 31.7 MB
  1 -Introduction to Deep Learning.mp4 100.8 MB
  1 -Introduction to Machine Learning.mp4 100.8 MB
  1 -Introduction to Pandas DataFrames.mp4 40.8 MB
  1 -Introduction to PyTorch.mp4 65.9 MB
  1 -Introduction to Scikit-Learn and Environment Setup.mp4 43.3 MB
  1 -Introduction to TensorFlow.mp4 56.5 MB
  1 -Introduction to the Seaborn Library.mp4 71.5 MB
  1 -Introduction.mp4 18.6 MB
  1 -Lists in Python.mp4 75 MB
  1 -Overview of NumPy and Its Core Concepts.mp4 60.8 MB
  1 -Reading and Writing Files in Python.mp4 69.4 MB
  1 -What is Python & Why Learn It.mp4 37.1 MB
  2 - Course resources.html 102.4 B
  2 -Data Loading Utilities in Scikit-Learn.mp4 61.6 MB
  2 -Indexing and Selecting Data in NumPy Arrays.mp4 33.5 MB
  2 -Operators and Advanced Conditions.mp4 79.7 MB
  2 -Plotting Fundamentals Creating and Customizing Visuals.mp4 116.5 MB
  2 -Tensor Operations in PyTorch.mp4 49.9 MB
  2 -This is a Milestone!.mp4 43.7 MB
  2 -Understanding Recursion.mp4 52.6 MB
  2 -Understanding Tuples.mp4 41.6 MB
  2 -Visualizing Distributions Univariate and Bivariate Analysis.mp4 50.4 MB
  2 -While Loops in Python.mp4 51.6 MB
  2 -Working with Series and DataFrames.mp4 47.3 MB
  2 -Working with Tensors and TensorFlow Operations.mp4 64.9 MB
  3 -Advanced Plots in Seaborn Pairplots and Barplot Customization.mp4 88.6 MB
  3 -Building Neural Networks with PyTorch.mp4 76.8 MB
  3 -Core Methods for Data Analysis in Pandas.mp4 111.6 MB
  3 -Key Components of TensorFlow.mp4 27.9 MB
  3 -Lambda Functions in Python.mp4 49.5 MB
  3 -Subplots and Scatter Plots Comparative and Relational Analysis.mp4 78.1 MB
  3 -Supervised Learning with Scikit-Learn.mp4 46.3 MB
  3 -Understanding Array Data Types, Shapes, and Stacking.mp4 53 MB
  3 -Understanding Variables in Python.mp4 67.5 MB
  3 -Working with Dictionaries.mp4 36.8 MB
  4 -Bar Charts, Histograms, and Pie Charts Distribution and Composition Insights.mp4 85.8 MB
  4 -Building Models with Keras in TensorFlow.mp4 135.9 MB
  4 -Complex Visualizations Countplots and Heatmaps.mp4 63.9 MB
  4 -Handling Missing and Null Data.mp4 109.2 MB
  4 -Project – Melanoma Cancer Prediction with PyTorch.mp4 49.6 MB
  4 -Python Data Types.mp4 38.9 MB
  4 -Sets in Python.mp4 29.9 MB
  4 -Techniques for Creating Arrays in NumPy.mp4 42.7 MB
  4 -Unsupervised Learning with Scikit-Learn.mp4 63.6 MB
  5 -Data Transformation Techniques in Scikit-Learn.mp4 74.7 MB
  5 -DataFrame Transformation and Manipulation.mp4 110.6 MB
  5 -Performing Mathematical and Statistical Operations with Arrays.mp4 46.1 MB
  5 -Project Extension – Data Augmentation for Cancer Prediction.mp4 55 MB
  5 -Understanding the Variety of Layers in Neural Networks.mp4 41.8 MB
  5 -Working with Strings in Python.mp4 48.2 MB
  6 -Model Selection and Evaluation in Scikit-Learn.mp4 77.3 MB
  6 -Project Extension – Defining a Custom Neural Network.mp4 96.9 MB
  6 -Project – Fashion MNIST Classification with TensorFlow.mp4 178.9 MB
  6 -Useful String Methods.mp4 82.5 MB
  7 -Evaluating Models with Confusion Matrix in PyTorch.mp4 35.9 MB
  7 -Visualization Tools in Scikit-Learn.mp4 50.5 MB
  8 -Saving and Reusing Models in Scikit-Learn.mp4 44.9 MB
  8 -The final milestone!.mp4 12.7 MB
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  Housing.csv 29.3 KB
  Housing.xlsx 37.5 KB
  Housing_with_null.csv 29.1 KB
  ML Intro PPT.pptx 8.9 MB
  Numpy.ipynb 53.1 KB
  Pandas.ipynb 233.8 KB
  PyTorch_Tutorial.ipynb 13.2 KB
  Python_Tutorial.ipynb 139.8 KB
  Scikit_Learn_Tutorial.ipynb 4.8 KB
  Seaborn.ipynb 2 MB
  deep learning intro.pptx 997.8 KB
  example.txt 0 B
  tensorflow_Tutorial.ipynb 17.3 KB
  ▲ 79 total files

Description


Python Bootcamp

https://WebToolTip.com

Published 9/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 9h 36m | Size: 3.78 GB

Master Python and unlock power of data with NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, and PyTorch

What you'll learn
Gain a thorough understanding of Python syntax, script writing, and core concepts such as variables, data types, and string operations
Master the use of conditional statements and loops in Python to automate and optimize data processing tasks
Learn to design reusable Python functions to perform repetitive tasks efficiently, including recursion and lambda functions
Understand how to use NumPy arrays for complex mathematical computations and effectively handle large datasets with high performance
Master the use of Pandas for data manipulation and analysis; learn how to explore, clean, and transform data into a suitable format
Develop the ability to create insightful visual representations of data using Matplotlib and Seaborn libraries of Python
Gain hands-on experience with Scikit-Learn, applying supervised and unsupervised learning algorithms to solve real-world machine learning problems
Understand the fundamentals of Deep Learning and neural networks, forming the foundation to work with TensorFlow and PyTorch frameworks
Build and evaluate deep learning models in PyTorch, including projects such as Fashion MNIST classification and cancer prediction

Requirements
No prior experience in Python or data analysis is required; just basic computer skills and access to a computer with an internet connection are necessary to start this course.

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