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 Shoaib 4 months ago
    1. Nominal Scale A Nominal Scale is the simplest level of measurement. It is used to label categories without any quantitative value. The categories are distinct, have no order, and cannot be ranked. 2. Ordinal Scale An Ordinal Scale represents categories that have a meaningful order or rank, but the difference between the categories is not uniform or meaningful. 3. Interval Scale An Interval Scale represents numeric data where the difference between two points is meaningful, but there is no true zero. It allows for ranking, and the intervals between values are equal.4. Ratio Scale A Ratio Scale is the highest level of measurement. It has all the characteristics of an interval scale, but with a true zero point, which allows for meaningful ratios.
    Reply
    Muhammad Shoaib 4 months ago
    nominal scale, ordinal scale, internal scale, and ratio scale.
    Reply
    Muhammad Faizan 11 months ago
    Excited to learn Statistics!
    Reply
    Muhammad Rameez 12 months ago
    Done sir
    Reply
    kashan malik 1 year ago
    DONE
    Reply
    Shahid Umar 1 year ago
    There are four scales of measurement nominal scale, ordinal scale, interval scale, and ratio scale.
    Reply
    saima saeed 1 year ago
    Education Levels: High school diploma, associate's degree, bachelor's degree, master's degree, etc. Socioeconomic Status: Lower class, middle class, upper class. Likert Scales: Agree strongly, agree, neutral, disagree, disagree strongly. Customer Satisfaction Ratings: Very satisfied, satisfied, neutral, dissatisfied, very dissatisfied.
    Reply
    saima saeed 1 year ago
    Nominal data is a type of categorical data that represents categories or labels with no inherent order or ranking. In nominal data, the categories are distinct and don't have a numerical value or a meaningful order.
    Reply
    tayyab Ali 1 year ago
    I learned nominal scale, ordinal scale, internal scale, and ratio scale.
    Reply
    Sibtain Ali 1 year ago
    I learned the nominal scale, ordinal scale, internal scale, and ratio scale.
    Reply
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