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_Faizan 3 weeks ago
    Excited to learn Statistics!
    Reply
    Muhammad Rameez 1 month ago
    Done sir
    Reply
    kashan malik 5 months ago
    DONE
    Reply
    Shahid Umar 7 months ago
    There are four scales of measurement nominal scale, ordinal scale, interval scale, and ratio scale.
    Reply
    saima saeed 7 months 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 7 months 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 7 months ago
    I learned nominal scale, ordinal scale, internal scale, and ratio scale.
    Reply
    Sibtain Ali 7 months ago
    I learned the nominal scale, ordinal scale, internal scale, and ratio scale.
    Reply
    Najeeb Ullah 7 months ago
    done
    Reply
    Javed Ali 7 months ago
    AOA, I learned in this lecture about scales of measurement like nominal scale, ordinal scale, interval scale, and ratio. ALLAH KAREEM aap ko dono jahan ki bhalaian aata kry AAMEEN.
    Reply
    Javed Ali 7 months ago
    A ratio scale is a measurement scale that deals with numeric variables that have true zeros and equal intervals between neighbouring points, like age, distance, time, weight and count data.
    Javed Ali 7 months ago
    Here are some examples of data that can be represented as an interval scale:Temperature: Celsius, Fahrenheit, KelvinHeight: Meters, centimetres, inchesWeight: Kilograms, grams, and poundsIQ scores: 0 to 200SAT scores: 200 to 800Credit scores: 300 to 850Time: Seconds, minutes, hours, days, yearsDistance: Meters, kilometres, milesElectrical current: AmperesVolume: Liters, milliliters, gallons
    Javed Ali 7 months ago
    Here are some examples of data that can be represented on an ordinal scale, like Customer satisfaction ratings, Movie ratings, School grades, Levels of pain, Stages of cancer, Levels of agreement or disagreement, Levels of importance, Levels of quality, Levels of difficulty and Levels of confidence.
    Javed Ali 7 months ago
    Here are some examples of data that can be represented on a nominal scale, like Eye colour, Blood type, Hair colour, Marital status, Occupation, Favourite color, Political affiliation, Religious affiliation, Country of origin, Type of animal and Mode of transportation.
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