Course Content
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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
    Join the conversation
    Muhammad Rameez 1 month ago
    Done
    Reply
    Rana Anjum Sharif 1 month ago
    Done
    Reply
    Abdullah Khan Kakar 6 months ago
    Very Important Lecture. Because without learning these techniques, we are unable to train our model. Or we can say that model will ignore our data, model will say you are poor and I am rich so go out from here and wear clean clothes, then meet me.
    Reply
    tayyab Ali 6 months ago
    I learned in this lecture about (Algorithm Compatibility, Efficiency and Performance, Feature Representation, Support Unseen categories, Less Memory Usage, One Hot Encoding).
    Reply
    Sibtain Ali 6 months ago
    I learned from this video about (1- Algorithm Compatibility, 2- Efficiency and Performance, 3- Feature Representation, 4- Support Unseen category, 5- Less Memory Usage, and 6-One Hot Encoding)
    Reply
    Javed Ali 6 months ago
    AOA, I also learned how encoding is necessary. There are a few reasons why encoding is important; let's look at them. 1- Algorithm Compatibility ( work at numeric data ) 2- Efficiency and Performance ( less computation power ) 3- Feature Representation ( remove bias) 4- Support Unseen category 5- Less Memory Usage 6-One Hot Encoding ALLAH KAREEM ap ko dono jahan ki bhalian ata kray AAMEEN.
    Reply
    Shahid Umar 6 months ago
    The main benefits of feature encoding are (1) algorithm compatibility match (2) efficiency and performance increase (3) feature representation becomes easy (5) less memory usage (6) one-hot simple understandable encoding
    Reply
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