Python Machine Learning -all concepts a-z

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size992.3 MB
  • Uploaded Byfreecoursewb
  • Downloads76
  • Last checkedApr. 17th '26
  • Date uploadedApr. 17th '26
  • Seeders 17
  • Leechers25

Infohash : 6D6FF669F2A0C8529917916A738F15896D440AED

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.

Files:

[ WebToolTip.com ] Python Machine Learning -all concepts a-z
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction to Machine Learning
    • 1. Course Introduction.mp4 (38.6 MB)
    • 2. What is Machine Learning.mp4 (7.0 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 (4.0 MB)
    10 - Feature Engineering
    • 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)
    11 - Saving & Loading Models
    • 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 (8.0 MB)
    • 47. Loading for Prediction.mp4 (28.3 MB)
    12 - Practical ML Projects
    • 48. Practical ML Project- Live Example.mp4 (35.7 MB)
    13 - Deployment Basics
    • 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 (2.0 MB)
    14 - Best Practices & ML Workflow
    • 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 (5.0 MB)
    • 59. Code Quality Standards.mp4 (4.7 MB)
    • 60. Documentation Essentials.mp4 (4.9 MB)
    15 - Interview Preparation
    • 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)
    2 - Python for Machine Learning
    • 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)
    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 (20.0 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)
    • Bonus Resources.txt (0.1 KB)

Code:

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