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
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    Rana Anjum Sharif 4 weeks ago
    Done
    Reply
    Muhammad Rameez 4 weeks ago
    Done
    Reply
    Asad Mujeeb 1 month ago
    I enjoyed a lot Aamar baba
    Reply
    Sibtain Ali 6 months ago
    I learned in this video (1-MAE, 2-MSE, 3-RMSE, 4-R-squared, 5-Adjusted R-squared, and 6-MAPE).
    Reply
    tayyab Ali 6 months ago
    I learned in this lecture (MAE, MSE, RMSE, R-squared, Adjusted R-squared, and MAPE).
    Reply
    Shahid Umar 6 months ago
    To make the right regression model we need to find the different values from these metrics (1) MAE - Mean Absolute Error (2) MSE - Mean Squared Error (3) RMSE - Root Mean Squared Error (4) R^2 - R-Squared Error (5) AdjR62 - Adjusted R-Squared Error (6) ME - Mean Error (6) MAPE - Mean Absolute Percentage Error.
    Reply
    Javed Ali 6 months ago
    AOA, I also learned about Regression metrics which are1-MAE The absolute difference between prediction and actual observation Interpretation: lower values are better. A value of 0 indicates no error Value cannot be negative2-MSE Average squared difference between the estimated value and actual value Interpretation: Like MAE, lower values are better MSE is more sensitive to outliers than MAE3-RMSE The square root of the mean of the squared errors Interpretation: RMSE is more sensitive to outliers than MAE4-R-squared Coefficient of determination Interpretation: value ranges from 0 to 1. A higher R-squared indicates a better fit between the model and the data5-Adjusted R-squared A modified version of R-squared Adjust the number of predictors in the model Interpretation: compares the explanatory power of regression model that contain different numbers of predictors6-MAPE Provides error in terms of percentage Can be infinite or undefined for y_i = 0And I also learned about how to choose the right metrics for regression.ALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey,Ameen.
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