Udemy - Machine Learning Project - Social Media Marketing in Python

seeders: 14
leechers: 4
Added 4 months ago by freecoursewb in Other

Download Fast Safe Anonymous
movies, software, shows...

Files

Udemy - Machine Learning Project - Social Media Marketing in Python (Size: 1.6 GB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~Get Your Files Here !
  1 - Welcome
  1. Introduction and Outline.mp4 83.4 MB
  2 - Fine-Tuning and Evaluating Language Models on Comment Scores
  10. Baseline Update.mp4 31.4 MB
  11. Course Takeaways.mp4 43.8 MB
  5. Fine-Tuning a Language Model for Classification.mp4 539.3 MB
  6. Fine-Tuning a Language Model for Regression.mp4 311.8 MB
  7. Evaluating Zero-Shot Classification Using an LLM Generative AI.mp4 185.3 MB
  8. Results Using More Data.mp4 50.7 MB
  9. Baseline Experiments.mp4 347.3 MB
  2. Before You Start.mp4 35 MB
  3. How to succeed in this course.mp4 38.1 MB
  4. Code-Link.url 102.4 B
  4. Github-Link.url 102.4 B
  4. Troubleshooting.url 102.4 B
  4. Where to get the code.mp4 12.4 MB

Description


Machine Learning Project: Social Media Marketing in Python

https://WebToolTip.com

Published 1/2026
Created by Lazy Programmer Inc.
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 11 Lectures ( 2h 31m ) | Size: 1.64 GB

Reddit Comment Score Prediction and Data Analytics with AI Language Models

What you'll learn
✓ Complete an end-to-end machine learning project using state-of-the-art AI tools
✓ Fine-tune a transformer language model to predict Reddit comment scores
✓ Perform zero-shot prediction using a state-of-the-art AI / LLM with the OpenAI API
✓ Understand the correct approach to a machine learning project, including establishing a baseline

Requirements
● Experience with classification and regression using neural networks
● Knowledge of loss functions for classification and regression
● Python programming experience
● Knowledge of metrics like accuracy, MSE, F1-score
● Understand train-test splitting, overfitting, generalization
● Knowledge of text-preprocessing: tokenization, truncation
● Understand the importance of context window length / maximum sequence length for sequence models
● Understand the concept of fine-tuning (the code and syntax will be shown to you)

Related Torrents

torrent name size uploader age seed leech
10
0
3
9
7