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title: Best Machine Learning Mastery posts — July 2024 | daily.dev
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# Best of Machine Learning Mastery — July 2024

1. 1  
[](https://daily.dev/posts/7-free-resource-to-master-llms-ziotzkt2f "7 Free Resource to Master LLMs")  
Article  
![Avatar of mlm](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/795781ffb32446c39eba040feb64f505)Machine Learning Mastery · 2y  
7 Free Resource to Master LLMs  
Large Language Models (LLMs) are increasingly popular, with many companies seeking expertise in this area for AI-driven automation and optimization. This post reviews seven free resources, including courses from Cohere, Stanford, and Microsoft, as well as roadmaps and tutorials on GitHub and DataCamp. These resources aim to equip learners with the skills needed to understand, build, and deploy LLMs in various applications.  
162  
2
2. 2  
[](https://daily.dev/posts/5-tips-for-getting-started-with-deep-learning-hrxdjivml "5 Tips for Getting Started with Deep Learning")  
Article  
![Avatar of mlm](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/795781ffb32446c39eba040feb64f505)Machine Learning Mastery · 2y  
5 Tips for Getting Started with Deep Learning  
Deep learning, a subset of machine learning inspired by the human brain, has become essential in areas like computer vision, speech recognition, and text generation. To get started, focus on understanding machine learning basics, select a comfortable deep-learning framework (such as TensorFlow, PyTorch, or Keras), learn neural network architectures, start with simple projects, and practice regularly while engaging with the community for feedback and guidance.  
59  
1
3. 3  
[](https://daily.dev/posts/tips-for-effectively-training-your-machine-learning-models-64icvlzyr "Tips for Effectively Training Your Machine Learning Models")  
Article  
![Avatar of mlm](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/795781ffb32446c39eba040feb64f505)Machine Learning Mastery · 2y  
Tips for Effectively Training Your Machine Learning Models  
Achieving optimal machine learning model performance involves several critical steps: efficient data preprocessing such as handling missing values and scaling features, effective feature engineering including creating interaction and binning features, addressing class imbalance through resampling and adjusting class weights, and using cross-validation and hyperparameter tuning to ensure robust model evaluation and selection. By comparing models with cross-validation scores, one can select and optimize the best model for the data.  
20

[See all Machine Learning Mastery archives](/sources/mlm/best-of)

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