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 1 week ago
    I tried to learn these tests. I'll practice and I'm sure that I'll master these.
    Reply
    Muhammad Bilal Naeem 1 month ago
    Sir, I have a question, Practicing on the penguins dataset, I checked normal distribution, I got two categories as normal (p > 0.05), while one category came non-normal (island vs bill length). Now, how to apply the parametric/non-parametric tests to find out whether type of island has significant effect on bill length of penguins?
    Reply
    Rana Anjum Sharif 2 months ago
    Done
    Reply
    kashan malik 5 months ago
    DONE
    Reply
    Shahid Umar 7 months ago
    If you want to prove your analysis is powerful then you must choose the right statistical test for your data.
    Reply
    hasaan khan 6 months ago
    Absolutely Yes
    tayyab Ali 7 months ago
    I learned the Right statistical Method, statistical tests, and normalized data.
    Reply
    Sibtain Ali 7 months ago
    I learned the Right statistical Method, statistical tests, and normalized data.
    Reply
    komal Baloch 7 months ago
    done
    Reply
    Javed Ali 7 months ago
    AOA, I learned in this lecture about parametric (fulfilling the assumption) and non-parametric (not fulfilling the assumption). Now we talk about the assumptions of the normality test, which is the Shapiro-Wilk test and the Kalmogrove-Smirnow test, and then we check the homogeneity test. For that, we check through the Levine test, and this is done so that we know the purpose. After that, it is also important to know what type of data (categorical or numerical) it is, and then it is also important that we choose the statistical test (chi-squared, t-test/ANOVA, correlation). ALLAH PAK ap ko dono jahan ki bhalaian aata kry AAMEEN.
    Reply
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