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 3 weeks ago
    The main metrics used to evaluate logistic regression models are:1. Accuracy:The fraction of correct predictions out of the total number of predictions. It is calculated as (correct predictions) / (total predictions). 2. Precision: The fraction of true positive predictions out of all positive predictions. It measures how precise the model is at predicting the positive class. 3. Recall (Sensitivity): The fraction of true positive predictions out of all actual positive instances. It measures how well the model identifies positive instances. 4. F1-Score: The harmonic mean of precision and recall. It provides a balanced metric that considers both precision and recall. ROC (Receiver Operating Characteristic) Curve: A plot of the true positive rate (recall) against the false positive rate (1 - specificity) at different classification thresholds. The AUC (Area Under the Curve) is a useful metric derived from the ROC curve. 5. Log Loss (Cross-Entropy Loss): The negative log-likelihood of the true labels given the predicted probabilities. It penalizes confident incorrect predictions more heavily.
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    Rana Anjum Sharif 3 weeks ago
    The main metrics used to evaluate logistic regression models are: Accuracy: The fraction of correct predictions out of the total number of predictions. It is calculated as (correct predictions) / (total predictions). Precision: The fraction of true positive predictions out of all positive predictions. It measures how precise the model is at predicting the positive class. Recall (Sensitivity): The fraction of true positive predictions out of all actual positive instances. It measures how well the model identifies positive instances. F1-Score: The harmonic mean of precision and recall. It provides a balanced metric that considers both precision and recall. ROC (Receiver Operating Characteristic) Curve: A plot of the true positive rate (recall) against the false positive rate (1 - specificity) at different classification thresholds. The AUC (Area Under the Curve) is a useful metric derived from the ROC curve. Log Loss (Cross-Entropy Loss): The negative log-likelihood of the true labels given the predicted probabilities. It penalizes confident incorrect predictions more heavily.
    Reply
    Fatima Zulfiqar 5 months ago
    ✍️done
    Reply
    tayyab Ali 6 months ago
    I learned in this lecture regression and classification matrics.
    Reply
    Waseem Hassan 6 months ago
    I learned in this lecture what methods would apply in finding the numerical data and/or categorical data.
    Sibtain Ali 6 months ago
    I learned Regression and Classification.
    Reply
    Shahid Umar 6 months ago
    Regression and Classification Matrices overview discussed in this lecture. (1) Mean Squared Error - MSE (2) R2-Coefficient of Determination (3) Root Mean Squared Error (4) Mean Absolute Error (5) Accuracy Score (6) Recall Score (7) Precision Score (8) F1 Score (9) Confusion Matrix (10) Classification Report.
    Reply
    Javed Ali 6 months ago
    AOA, I learned in this lecture about the difference between regression and classification and what their basic metrics are.1. Regression ( numerical metrics, which are MSE, R^2, RMSE, and MAE ) 2: Classification (categorical metrics, which are accuracy score, recall score, precision score, f1 score, and confusion matrix). 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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