Master Machine Learning in Just 20 Days.1745724742524
30.8 MB
Title:
Master Machine Learning in Just 20 Days - Your Ultimate Guide! π₯
Description:
Struggling to break into Data Science or ace ML interviews at top product-based companies?
This 20-day roadmap covers ML basics to advanced topics like tuning, deep learning, and deployment with top resources and practice questions!
Whatβs Inside:
β Supervised & Unsupervised Learning β Regression, Classification, Clustering
β Deep Learning & Neural Networks β CNNs, RNNs, LSTMs
β End-to-End ML Projects β Data Preprocessing, Feature Engineering, Deployment
β Model Optimization β Hyperparameter Tuning, Ensemble Methods
β Real-World ML Applications β NLP, AutoML, Scalable ML Systems
#MachineLearning #DeepLearning #DataScience #ArtificialIntelligence #MLEngineering #CareerGrowth #MLRoadmap
By: t.me/HusseinSheikhoβ
π― BEST DATA SCIENCE CHANNELS ON TELEGRAM π
Master Machine Learning in Just 20 Days - Your Ultimate Guide! π₯
Description:
Struggling to break into Data Science or ace ML interviews at top product-based companies?
This 20-day roadmap covers ML basics to advanced topics like tuning, deep learning, and deployment with top resources and practice questions!
Whatβs Inside:
β Supervised & Unsupervised Learning β Regression, Classification, Clustering
β Deep Learning & Neural Networks β CNNs, RNNs, LSTMs
β End-to-End ML Projects β Data Preprocessing, Feature Engineering, Deployment
β Model Optimization β Hyperparameter Tuning, Ensemble Methods
β Real-World ML Applications β NLP, AutoML, Scalable ML Systems
#MachineLearning #DeepLearning #DataScience #ArtificialIntelligence #MLEngineering #CareerGrowth #MLRoadmap
By: t.me/HusseinSheikho
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9 machine learning concepts for ML engineers!
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
1οΈβ£ 4 strategies for Multi-GPU Training.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
2οΈβ£ 4 ways to test models in production
- While testing a model in production might sound risky, ML teams do it all the time, and it isnβt that complicated.
- Implemented here: https://lnkd.in/g33mASMM
3οΈβ£ Training & inference time complexity of 10 ML algorithms
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
4οΈβ£ Regression & Classification Loss Functions.
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
5οΈβ£ Transfer Learning, Fine-tuning, Multitask Learning, and Federated Learning.
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
6οΈβ£ 15 Pandas to Polars to SQL to PySpark Translations.
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
7οΈβ£ 11 most important plots in data science
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
8οΈβ£ 11 types of variables in a dataset
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
9οΈβ£ NumPy cheat sheet for data scientists
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
π Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
π± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
- While testing a model in production might sound risky, ML teams do it all the time, and it isnβt that complicated.
- Implemented here: https://lnkd.in/g33mASMM
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
#MachineLearning #DataScience #MLEngineering #DeepLearning #AI #MLOps #BigData #Python #NumPy #Pandas #Visualization
Please open Telegram to view this post
VIEW IN TELEGRAM
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