Course Content
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Dear, to register for the 6 months AI and Data Science Mentorship Program, click this link and fill the form give there: https://shorturl.at/fuMX6
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Day-17: Complete EDA on Google PlayStore Apps
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Day-25: Quiz Time, Data Visualization-4
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Day-27: Data Scaling/Normalization/standardization and Encoding
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Day-30: NumPy (Part-3)
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Day-31: NumPy (Part-4)
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Day-32a: NumPy (Part-5)
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Day-32b: Data Preprocessing / Data Wrangling
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Day-37: Algebra in Data Science
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Day-56: Statistics for Data Science (Part-5)
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Day-69: Machine Learning (Part-3)
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Day-75: Machine Learning (Part-9)
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Day-81: Machine Learning (Part-15)-Evaluation Metrics
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Day-82: Machine Learning (Part-16)-Metrics for Classification
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Day-85: Machine Learning (Part-19)
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Day-89: Machine Learning (Part-23)
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Day-91: Machine Learning (Part-25)
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Day-93: Machine Learning (Part-27)
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Day-117: Deep Learning (Part-14)-Complete CNN Project
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Day-119: Deep Learning (Part-16)-Natural Language Processing (NLP)
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Day-121: Time Series Analysis (Part-1)
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Day-123: Time Series Analysis (Part-3)
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Day-128: Time Series Analysis (Part-8): Complete Project
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Day-129: git & GitHub Crash Course
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Day-131: Improving Machine/Deep Learning Model’s Performance
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Day-133: Transfer Learning and Pre-trained Models (Part-2)
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Day-134 Transfer Learning and Pre-trained Models (Part-3)
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Day-137: Generative AI (Part-3)
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Day-139: Generative AI (Part-5)-Tensorboard
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Day-145: Streamlit for webapp development and deployment (Part-1)
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Day-146: Streamlit for webapp development and deployment (Part-2)
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Day-147: Streamlit for webapp development and deployment (Part-3)
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Day-148: Streamlit for webapp development and deployment (Part-4)
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Day-149: Streamlit for webapp development and deployment (Part-5)
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Day-150: Streamlit for webapp development and deployment (Part-6)
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Day-151: Streamlit for webapp development and deployment (Part-7)
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Day-152: Streamlit for webapp development and deployment (Part-8)
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Day-153: Streamlit for webapp development and deployment (Part-9)
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Day-154: Streamlit for webapp development and deployment (Part-10)
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Day-155: Streamlit for webapp development and deployment (Part-11)
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Day-156: Streamlit for webapp development and deployment (Part-12)
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Day-157: Streamlit for webapp development and deployment (Part-13)
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How to Earn using Data Science and AI skills
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Day-160: Flask for web app development (Part-3)
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Day-161: Flask for web app development (Part-4)
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Day-162: Flask for web app development (Part-5)
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Day-163: Flask for web app development (Part-6)
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Day-164: Flask for web app development (Part-7)
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Day-165: Flask for web app deployment (Part-8)
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Day-167: FastAPI (Part-2)
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Day-168: FastAPI (Part-3)
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Day-169: FastAPI (Part-4)
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Day-170: FastAPI (Part-5)
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Day-171: FastAPI (Part-6)
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Day-174: FastAPI (Part-9)
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Six months of AI and Data Science Mentorship Program
About Lesson

Here is the cheat sheet for Conda commands: Conda cheatsheet

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Tauseef Ahmad 1 month ago
Thanks Sir
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syeda fatima 2 months ago
Python Important libraries (1)Pandas: Used for data manipulation and analysis. (2)NumPy: Fundamental for data science and machine learning. (3)Matplotlib: For plotting and graph visualization. (4)Scikit-learn: For machine learning. (5)BeautifulSoup: For web scraping. (6)Keras: For deep neural networks. (7)TensorFlow: For machine learning and deep learning. (8)SciPy: For scientific computation. (9)PyTorch: For deep learning.
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Ilya Haider 6 months ago
Data Manipulation and Analysis:NumPy Pandas Machine Learning:Scikit-learn TensorFlow PyTorch Data Visualization:Matplotlib Seaborn Plotly Web Development:Django Flask GUI Development:Tkinter PyQt Kivy Database Access:SQLAlchemy SQLite Psycopg2 (for PostgreSQL) Network Programming:Requests (for HTTP requests) Socket (built-in) Web Scraping:BeautifulSoup Scrapy Natural Language Processing (NLP):NLTK SpaCy Scientific Computing:SciPy Image Processing:OpenCV Pillow Testing:PyTest unittest (built-in) Development and Debugging:pdb (built-in) logging (built-in) Asynchronous Programming:asyncio (built-in) Automation and Scripting:os (built-in) shutil (built-in) Regular Expressions:re (built-in) Cybersecurity:PyCryptodome Scapy Web Frameworks for APIs:FastAPI Flask-RESTful Game Development:Pygame Deep Learning and Neural Networks:Keras (now integrated with TensorFlow) fastai
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Ilya Haider 6 months ago
i have make account on onenode
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Ilya Haider 6 months ago
3.114 h sir
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Ilya Haider 6 months ago
I have downloaded conda cheat sheet but don't know where to practices
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Muhammad Haroon 6 months ago
NumPy: Ideal for scientific computing, offering powerful N-dimensional array objects, tools for integrating C/C++ code, and linear algebra operations.Pandas: Perfect for data manipulation and analysis with easy-to-use data structures and functions, making handling structured data effortless.Matplotlib: Excellent for creating static, interactive, and animated visualizations in Python, providing functionalities similar to MATLAB.Scikit-learn: A robust machine learning library offering simple and efficient tools for data mining and data analysis.TensorFlow and PyTorch: These are powerful frameworks for machine learning and deep learning, enabling the creation and training of neural networks.Requests: A versatile HTTP library for making requests and working with APIs, simplifying the process of sending HTTP requests.Beautiful Soup: Perfect for web scraping, allowing easy extraction of data from HTML and XML files.Django and Flask: Both are popular web frameworks. Django is a high-level web framework that encourages rapid development and clean, pragmatic design. Flask is a microframework that's lightweight and easy to use for building web applications.NLTK (Natural Language Toolkit): Designed for working with human language data, it provides easy-to-use interfaces to over 50 corpora and lexical resources.OpenCV: A computer vision library that provides a wide range of tools for image and video analysis, including object detection and face recognition.
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Muhammad Hidayat Ullah Khan 7 months ago
Thanks for explaining in easy way.
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Muhammad Ilyas 7 months ago
3.11.4
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Muhammad Hashir 7 months ago
TensorFlow Scikit-Learn Numpy Keras PyTorch LightGBM Eli5 SciPy Theano Pandas NuPIC Ramp Pipenv PyBrain Matplotlib
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