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
    Zohaib Zeeshan 1 week ago
    good = c bad = b
    Reply
    Muhammad_Faizan 2 weeks ago
    we use the SEM when we are interested in the accuracy of the sample mean as an estimate of the population mean, especially in the context of hypothesis testing, confidence intervals, and other inferential statistics. The SD is used when we are interested in understanding the variability within the sample itself.
    Reply
    Syed Abdul Qadir Gilani 1 month ago
    For example, if we're conducting an experiment and taking multiple measurements, the standard deviation can tell us how much our measurements for each individual trial vary. On the other hand, the standard error of the mean can tell us how much our overall estimate of the mean is likely to vary.
    Reply
    Rana Anjum Sharif 2 months ago
    We use Standard Error (SE) instead of Standard Deviation (SD) when describing the variability of a sample statistic, like a mean, because SE accounts for the sample size and provides a more accurate estimate of the population parameter's uncertainty.
    Reply
    Rana Anjum Sharif 2 months ago
    Done
    Reply
    Zohaib Zeeshan 1 week ago
    ok
    kashan malik 5 months ago
    DONE
    Reply
    junaid amin 6 months ago
    Standard Deviation (SD): Measures the spread of individual data points within a single dataset. It tells you how much variation there is from the mean within that specific group. Standard Error of the Mean (SEM): Measures the expected variability of sample means around the true population mean. It tells you how much the sample mean is likely to differ from the population mean due to random sampling error.
    Reply
    Shahid Umar 7 months ago
    I assumed after reading and watching the topics of variability: 1. A range represents a broad overview of data 2. IQR is the shortest form of range 3. Variance is the shortest form of IQR 4. Standard Deviation is the shortest form of Variance 5. Standard Error is the shortest form of Standard Deviation
    Reply
    tayyab Ali 7 months ago
    Why do we need to use (standard error) of mean instead of SD? The use of standard error (SE) instead of standard deviation (SD) is often related to the context of inferential statistics and hypothesis testing. Both standard error and standard deviation are measures of the spread or variability of a set of data points, but they have different purposes. Standard Deviation (SD): Standard Error (SE) of the Mean.
    Reply
    tayyab Ali 7 months ago
    I learned Standard Error or Standard Deviation with 100% practice.
    Sibtain Ali 7 months ago
    why do we need to use (standard error) of men instead of SD?The standard error (SE) and standard deviation (SD) are both measures of the spread or variability of a set of values, but they serve different purposes. Standard Deviation (SD): Standard Error (SE):
    Reply
    Sibtain Ali 7 months ago
    I learned standard deviation/ standard error with 100% practice.
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