6. Capstone Project Build a Data science project from scratch: After completing all the above courses take Harvard’s data science capstone project. Assess your skills in data visualization, statistics, data wrangling and machine learning.
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Machine Learning Fundamentals: Overfitting Mitigation and Modeling Approaches
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5. Machine Learning This course will teach you the basics of machine learning, techniques to mitigate overfitting, supervised and unsupervised modelling approaches, and recommendation systems.
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Data Pre-Processing: Preparing Data for Machine Learning Models
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3. Data Pre-Processing This will teach you to prepare data and convert it into a format that is easily digestible by machine learning models.
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Harvard Free Online Courses: Python, Data Science, Machine Learning
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Harvard University is offering online courses on Python, Data Science, Machine Learning, Data Preprocessing, Data Visualization & Statistics Here are 5 FREE courses you don't want to miss:
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Essential AI Libraries and Frameworks for Developers
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AI builders, #hivemind survey time! What are your current favorite go-to libraries and frameworks for your AI projects? The ones you couldn’t live without! Add name, short desc, and why below. Will summarize first 100~200 replies into an article
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Chat with PDF LLM Applications Repository Guide
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Please check the instructions here: https://
github.com/Shubhamsaboo/a
wesome-llm-apps/tree/main/chat_with_pdf
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Hardware Configuration Impact on AI Code Performance
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Totally dependent on the config of your local machine like RAM, GPU, etc. It has nothing to do with the code.
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Building Production-Quality Generative AI Applications Guide
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Learn best practices for building production-quality #GenAI apps in The Big Book of Generative AI! You'll find technical content and code samples to create and deploy a custom #LLM trained on your data — check it out https://
dbricks.co/49MNsmv -
LLM Course Reaches 30k Stars on GitHub
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The LLM Course hit 30k stars on GitHub! This is beyond anything I imagined. To compare, vLLM has 20k stars and Jax has 28k. We're not at llama.cpp (58k) or PyTorch (78k) yet, but this already feels indecent. Thanks for all the support! I'll be adding new sections and
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Development Tools for Code Efficiency and Legacy Modernization
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This guide covers tools designed for specific coding tasks and #integration into #development environments, enhancing #code efficiency, bug fixing, and even translating legacy code into modern #languages.