AI and Machine Learning

🧠 Machine Learning Mindmap

Resume key words for data scientist role explained in points: 1. Data Analysis:    - Proficient in extracting, cleaning, and analyzing data to derive insights.    - Skilled in using statistical methods and machine learning algorithms for data analysis.    - Experience with tools such as Python, R, or SQL for data manipulation and analysis. 2. Machine Learning:    - Strong understanding of machine learning techniques such as regression, classification, clustering, and neural networks. - Experience in model development, evaluation, and deployment.    - Familiarity with libraries like TensorFlow, scikit-learn, or PyTorch for implementing machine learning models. 3. Data Visualization:    - Ability to present complex data in a clear and understandable manner through visualizations.    - Proficiency in tools like Matplotlib, Seaborn, or Tableau for creating insightful graphs and charts.    - Understanding of best practices in data visualization for effective communication of findings. 4. Big Data:    - Experience working with large datasets using technologies like Hadoop, Spark, or Apache Flink.    - Knowledge of distributed computing principles and tools for processing and analyzing big data.    - Ability to optimize algorithms and processes for scalability and performance. 5. Problem-Solving:    - Strong analytical and problem-solving skills to tackle complex data-related challenges.    - Ability to formulate hypotheses, design experiments, and iterate on solutions.    - Aptitude for identifying opportunities for leveraging data to drive business outcomes and decision-making. Resume key words for a data analyst role 1. SQL (Structured Query Language):    - SQL is a programming language used for managing and querying relational databases.    - Data analysts often use SQL to extract, manipulate, and analyze data stored in databases, making it a fundamental skill for the role. 2. Python/R:    - Python and R are popular programming languages used for data analysis and statistical computing.    - Proficiency in Python or R allows data analysts to perform various tasks such as data cleaning, modeling, visualization, and machine learning. 3. Data Visualization:    - Data visualization involves presenting data in graphical or visual formats to communicate insights effectively.    - Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visualizations that help stakeholders understand complex data patterns and trends. 4. Statistical Analysis:    - Statistical analysis involves applying statistical methods to analyze and interpret data.    - Data analysts use statistical techniques to uncover relationships, trends, and patterns in data, providing valuable insights for decision-making. 5. Data-driven Decision Making:    - Data-driven decision making is the process of making decisions based on data analysis and evidence rather than intuition or gut feelings.    - Data analysts play a crucial role in helping organizations make informed decisions by analyzing data and providing actionable insights that drive business strategies and operations.

If you're getting started with Ai and Ai Agents then save these terms related to Ai Agents...

🔅 Building Blocks for Deep Learning in the Wolfram Language 📝 Learn how to construct neural networks in the Wolfram Language. 🌐 Author: Wolfram Research 🔰 Level: Advanced ⏰ Duration: 54m 📋 Topics: Wolfram Language, Deep Learning, Artificial Intelligence 🔗 Join Artificial intelligence for more courses

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🌟 DeepSearcher: AI Harvester for Your Data.

The project combines the use of LLM, vector databases to perform search, evaluation, and reasoning tasks based on the provided data (files, text, sources).

It is positioned by developers as a tool for enterprise knowledge management, intelligent QA systems and information search scenarios. DeepSearcher can use information from the Internet if necessary, is compatible with Milvus vector databases and their service provider Zilliz Cloud, Pymilvus, OpenAI and VoyageAI embeddings. It is possible to connect LLM DeepSeek and OpenAI via API directly or through TogetherAI and SiliconFlow. Local file download, connection of web crawlers FireCrawl, Crawl4AI and Jina Reader are supported. Our immediate plans include adding a web clipper feature, expanding the list of supported vector databases, and creating a RESTful API interface. ▶️ Local installation and launch: # Clone the repository

git clone https://github.com/zilliztech/deep-searcher.git

# Create a Python venv

python3 -m venv .venv
source .venv/bin/activate

# Install dependencies

cd deep searcher
pip install -e .

# Quick start demo

from deepsearcher.configuration import Configuration, init_config
from deepsearcher.online_query import query

config = Configuration()

# Customize your config here

config.set_provider_config("llm", "OpenAI", {"model": "gpt-4o-mini"})
init_config(config = config)

# Load your local data

from deepsearcher.offline_loading import load_from_local_files
load_from_local_files(paths_or_directory=your_local_path)

# (Optional) Load from web crawling (FIRECRAWL_API_KEY env variable required)

from deepsearcher.offline_loading import load_from_website
load_from_website(urls=website_url)

# Query

result = query("Write a report about xxx.") # Your question here

🌐 GitHub: https://github.com/zilliztech/deep-searcher

🤝 Build AI Model From Scratch

🔅 Deep Learning with Python: Sequence Models and Transformers 📝 The course introduces sequence data, sequence data problems, and how to solve sequence data problems using sequence models. 🌐 Author: Frederick Nwanganga 🔰 Level: Intermediate ⏰ Duration: 1h 26m 📋 Topics: Deep Learning, Python 🔗 Join Artificial intelligence for more courses

🌟 ChatTTS — a generative text2speech model with an emphasis on realism

 import ChatTTS
from IPython.display import Audio

chat = ChatTTS.Chat()
chat.load_models()

texts = ["<PUT YOUR TEXT HERE>",]

wavs = chat.infer(texts, use_decoder=True)
Audio(wavs[0], rate=24_000, autoplay=True)

ChatTTS is a text-to-speech model designed specifically for conversational scenarios such as LLM assistant. ChatTTS supports both English and Chinese (if this is relevant). 🖥 GitHub 🤗 Play Hugging Face 🟡 ChatTTS Page

🔥 Voice mode + video chat mode is now available in chat.qwenlm.ai chat Moreover, the Chinese have posted the code of their Qwen2.5-Omni-7B - a single omni-model that can understand text, audio, images and video. They developed a "thinker-talker" architecture that enables a model to think and talk simultaneously. They promise to release open source models for an even greater number of parameters soon. Simply top-notch, run and test it. 🟢 Try it : https://chat.qwenlm.ai 🟢 Paper : https://github.com/QwenLM/Qwen2.5-Omni/blob/main/assets/Qwen2.5_Omni.pdf 🟢 Blog : https://qwenlm.github.io/blog/qwen2.5-omni 🟢 GitHub : https://github.com/QwenLM/Qwen2.5-Omni 🟢 Hugging Face : https://huggingface.co/Qwen/Qwen2.5-Omni-7B 🟢 ModelScope : https://modelscope.cn/models/Qwen/Qwen2.5-Omni-7B

🔅 Building Deep Learning Applications with Keras 📝 Get a thorough introduction to Keras, a versatile deep learning framework, and learn how to build, deploy, and monitor robust deep learning models. 🌐 Author: Isil Berkun 🔰 Level: Intermediate ⏰ Duration: 1h 50m 📋 Topics: Keras, Deep Learning, Application Development 🔗 Join Artificial intelligence for more courses

🔰 Aiopandas is a lightweight patch for Pandas that adds native async support for the most popular data processing methods: map, apply, applymap, aggregate and transform. Allows you to pass async functions to these methods without any problems. The library will automatically run them asynchronously, controlling the number of tasks executed simultaneously using the max_parallel parameter. ✨ Key features: ▪️ Easy integration: Use as a replacement for standard Pandas functions, but now with full support for async functions. ▪️ Controlled parallelism: Automatically execute your coroutines asynchronously, with the ability to limit the maximum number of parallel tasks (max_parallel). Ideal for managing the load on external services! ▪️ Flexible error handling: Built-in options for managing runtime errors: raise, ignore, or log. ▪️ Progress Indication: Built-in tqdm support for visually tracking the progress of long operations in real time. 🌐 Github : https://github.com/telekinesis-inc/aiopandas

Shipped an app using Apple's secret weapon 🍎 FoundationModels framework + ImagePlayground API Result: A virtual pet with genuine AI personality • Generates unique responses (not scripted) • Creates pixel art scenes in real-time • Runs interactive story adventures • Hosts dynamic quiz games Everything runs on-device. Neural Engine goes brrrr. The pet literally develops differently based on how you interact with it. No two RoboGochis are the same. RoboGochi - App Store (iPhone 15 Pro+ / M-chip iPad required)

🔅 AI for Beginners: Inside Large Language Models ⏲ 3 hours 📁 326 Lessons

📔 Understand how LLMs actually work under the hood from scratch with practical and fun lessons. No prior knowledge required!

🎙 Taught by: Scott Kerr 📤 Download All Courses