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Python Machine Learning -all concepts a-z

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Python Machine Learning -all concepts a-z (Size: 992.3 MB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~Get Your Files Here !
  1 - Introduction to Machine Learning
  1. Course Introduction.mp4 38.6 MB
  10 - Feature Engineering
  11 - Saving & Loading Models
  12 - Practical ML Projects
  13 - Deployment Basics
  14 - Best Practices & ML Workflow
  15 - Interview Preparation
  2 - Python for Machine Learning
  3 - ML Libraries in Python
  10. ML Libraries in Python.mp4 10.6 MB
  11. NumPy Basics.mp4 4.7 MB
  12. Pandas Basics.mp4 3.8 MB
  13. Scikit-Learn basics.mp4 3.5 MB
  14. Matplotlib & Seaborn basics.mp4 3.4 MB
  15. SciPy basics.mp4 2.7 MB
  4 - Data Handling & Preprocessing
  16. Loading & Inspecting Data.mp4 35.8 MB
  17. Handling Missing Values & Feature Engineering.mp4 25.8 MB
  18. Train-Test Split & Scaling Normalization.mp4 19.2 MB
  5 - Exploratory Data Analysis (EDA)
  19. Understanding Your Data.mp4 9.4 MB
  20. Summary Statistics.mp4 10.7 MB
  21. Data Visualisation Basics.mp4 19.3 MB
  22. Scatter Plots & Bar Charts.mp4 13.4 MB
  23. Correlation Analysis.mp4 19.1 MB
  24. Distribution Plots.mp4 8.6 MB
  6 - Supervised Learning - Regression
  25. Understanding Regression Simple Linear Regression & Multiple Linear Regression.mp4 31.4 MB
  26. Model Training & Evaluation.mp4 27.4 MB
  27. MAE(Mean Absolute Error), MSE(Mean Squared Error), RMSE(Root Mean Squared Error).mp4 10.9 MB
  7 - Supervised Learning - Classification
  28. Logistic Regression.mp4 25.6 MB
  29. K-Nearest Neighbors.mp4 36.6 MB
  30. Decision Trees.mp4 15.7 MB
  31. Random Forest.mp4 20 MB
  32. Model Evaluation Metrics Accuracy, Precision, Recall, F1 Score.mp4 46.5 MB
  8 - Model Evaluation & Validation
  33. Confusion Matrix.mp4 55.3 MB
  34. Cross-Validation.mp4 29.8 MB
  35. Overfitting, Underfitting, Bias-Variance Tradeoff.mp4 23.6 MB
  9 - Unsupervised Learning
  36. K-Means Clustering.mp4 16.7 MB
  37. Hierarchical Clustering.mp4 24.1 MB
  38. Principal Component Analysis.mp4 26.6 MB
  7. Why Python for ML.mp4 5.4 MB
  8. Python ML Ecosystem.mp4 17.1 MB
  9. Installation & Environment Setup with practical Demo.mp4 28.4 MB
  61. Fundamental Concepts.mp4 7.7 MB
  62. Algorithm Deep Dives.mp4 4.3 MB
  63. Practical Scenarios & Coding Challenges.mp4 7.6 MB
  64. Common Interview Topics & Final Tips.mp4 7.8 MB
  55. Project Structure.mp4 6.5 MB
  56. Ethical ML & Privacy.mp4 6.3 MB
  57. Reproducibility.mp4 5.6 MB
  58. Data Versioning.mp4 5 MB
  59. Code Quality Standards.mp4 4.7 MB
  60. Documentation Essentials.mp4 4.9 MB
  49. What is Model Deployment.mp4 9.2 MB
  50. Deployment Workflow 1. Save Trained Model.mp4 8.7 MB
  51. Deployment Workflow 2. Create API Endpoint.mp4 1.4 MB
  52. Deployment Workflow 3. Test Integration.mp4 5.4 MB
  53. Deployment Workflow 4. Deploy to Production.mp4 4.8 MB
  54. Introduction to FastAPI for ML.mp4 2 MB
  48. Practical ML Project- Live Example.mp4 35.7 MB
  43. Model Persistence.mp4 19.9 MB
  44. Training Phase.mp4 19.6 MB
  45. Saving with Pickle.mp4 10.2 MB
  46. Using Joblib.mp4 8 MB
  47. Loading for Prediction.mp4 28.3 MB
  39. Encoding Categorical Variables.mp4 21.6 MB
  40. Handling Outliers.mp4 16.5 MB
  41. Feature Scaling.mp4 26.2 MB
  42. Creating New Features.mp4 20.9 MB
  2. What is Machine Learning.mp4 7 MB
  3. ML vs Traditional Programming.mp4 5.1 MB
  4. Supervised Learning.mp4 4.1 MB
  5. Unsupervised Learning.mp4 3.5 MB
  6. Reinforcement Learning.mp4 4 MB

Description


Python Machine Learning -all concepts a-z
https://WebToolTip.com
Published 4/2026

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English | Duration: 2h 1m | Size: 992.3 MB
A Step-by-Step Guide from Basics to Advanced Machine Learning
What you'll learn

Understand the core concepts of Machine Learning and how algorithms learn from data

Work with real datasets using NumPy and Pandas

Build and train machine learning models using Scikit-learn

Implement supervised and unsupervised learning algorithms

Perform data preprocessing, feature scaling, and model evaluation

Apply classification, regression, and clustering techniques

Avoid common ML mistakes such as overfitting and underfitting

Interpret model results and improve performance
Requirements

Basic knowledge of Python is helpful, but no prior Machine Learning experience is required. All ML concepts are explained from scratch in a simple and intuitive way.

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