Udemy - Machine Learning Prediction Model from Scratch for Everyone

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[ DevCourseWeb.com ] Udemy - Machine Learning Prediction Model from Scratch for Everyone
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 1. Basics Of Machine Learning
    • 1. Overview of the course and Resource Materials.mp4 (39.6 MB)
    • 1.1 dataset1.1.csv (1.9 KB)
    • 1.2 depression_prediction_code.ipynb (102.3 KB)
    • 2. Introduction.mp4 (19.3 MB)
    • 3. Types of Machine Learning.mp4 (26.5 MB)
    2. Data Collection and Preprocessing
    • 1. Designing Form for Data Collection.mp4 (60.1 MB)
    • 2. Data Preprocessing.mp4 (60.1 MB)
    • 3. Data Preprocessing and preparation.mp4 (49.0 MB)
    3. Prediction Model Using Python Programming Language from Scratch
    • 1. Introduction to Google Collab and Python.mp4 (41.6 MB)
    • 2. Importing Dataset and Preparation at Google Colab.mp4 (56.3 MB)
    • 3. Data Analysis with Python Libraries.mp4 (34.4 MB)
    • 4. Prediction Model Naive Bayes and Random Forest.mp4 (37.3 MB)
    • 5. Prediction Model SVM with Confusion Matrix.mp4 (70.4 MB)
    • 6. Testing Model with Random Data.mp4 (33.3 MB)
    4. What's NEXT
    • 1. Congratulations and Future Direction.mp4 (21.7 MB)
    • 2. QUIZ.html (0.2 KB)
    • Bonus Resources.txt (0.4 KB)

Description

Machine Learning Prediction Model from Scratch for Everyone

https://DevCourseWeb.com

Published 9/2023
Created by Mehedi Hasan Abid
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 13 Lectures ( 56m ) | Size: 550 MB

From Scratch (Data Collection to Apply Algorithms) learn to Develop Naïve Bayes | Random Forest | SVM and Much More

What you'll learn
Machine learning fundamentals: supervised, unsupervised, reinforcement learning, and practical applications.
Master data preprocessing: handle missing data, scale features, and transform datasets for ML.
Explore various ML algorithms, understanding when and how to use them effectively
Evaluate models with metrics like accuracy, precision, recall, and understand the importance of cross-validation.
Develop coding skills from scratch, understanding the inner workings of ML models.
Engage in hands-on projects, building a portfolio nstrating practical ML expertise.
And Finally publish Research Articles or do project with own understanding

Requirements
No prerequisites required. You will learn everything you need to know.



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seeders:12
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Udemy - Machine Learning Prediction Model from Scratch for Everyone


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