Course Content
How and Why to Register
Dear, to register for the 6 months AI and Data Science Mentorship Program, click this link and fill the form give there: https://shorturl.at/fuMX6
0/2
Day-17: Complete EDA on Google PlayStore Apps
0/1
Day-25: Quiz Time, Data Visualization-4
0/1
Day-27: Data Scaling/Normalization/standardization and Encoding
0/2
Day-30: NumPy (Part-3)
0/1
Day-31: NumPy (Part-4)
0/1
Day-32a: NumPy (Part-5)
0/1
Day-32b: Data Preprocessing / Data Wrangling
0/1
Day-37: Algebra in Data Science
0/1
Day-56: Statistics for Data Science (Part-5)
0/1
Day-69: Machine Learning (Part-3)
0/1
Day-75: Machine Learning (Part-9)
0/1
Day-81: Machine Learning (Part-15)-Evaluation Metrics
0/2
Day-82: Machine Learning (Part-16)-Metrics for Classification
0/1
Day-85: Machine Learning (Part-19)
0/1
Day-89: Machine Learning (Part-23)
0/1
Day-91: Machine Learning (Part-25)
0/1
Day-93: Machine Learning (Part-27)
0/1
Day-117: Deep Learning (Part-14)-Complete CNN Project
0/1
Day-119: Deep Learning (Part-16)-Natural Language Processing (NLP)
0/2
Day-121: Time Series Analysis (Part-1)
0/1
Day-123: Time Series Analysis (Part-3)
0/1
Day-128: Time Series Analysis (Part-8): Complete Project
0/1
Day-129: git & GitHub Crash Course
0/1
Day-131: Improving Machine/Deep Learning Model’s Performance
0/2
Day-133: Transfer Learning and Pre-trained Models (Part-2)
0/1
Day-134 Transfer Learning and Pre-trained Models (Part-3)
0/1
Day-137: Generative AI (Part-3)
0/1
Day-139: Generative AI (Part-5)-Tensorboard
0/1
Day-145: Streamlit for webapp development and deployment (Part-1)
0/3
Day-146: Streamlit for webapp development and deployment (Part-2)
0/1
Day-147: Streamlit for webapp development and deployment (Part-3)
0/1
Day-148: Streamlit for webapp development and deployment (Part-4)
0/2
Day-149: Streamlit for webapp development and deployment (Part-5)
0/1
Day-150: Streamlit for webapp development and deployment (Part-6)
0/1
Day-151: Streamlit for webapp development and deployment (Part-7)
0/1
Day-152: Streamlit for webapp development and deployment (Part-8)
0/1
Day-153: Streamlit for webapp development and deployment (Part-9)
0/1
Day-154: Streamlit for webapp development and deployment (Part-10)
0/1
Day-155: Streamlit for webapp development and deployment (Part-11)
0/1
Day-156: Streamlit for webapp development and deployment (Part-12)
0/1
Day-157: Streamlit for webapp development and deployment (Part-13)
0/1
How to Earn using Data Science and AI skills
0/1
Day-160: Flask for web app development (Part-3)
0/1
Day-161: Flask for web app development (Part-4)
0/1
Day-162: Flask for web app development (Part-5)
0/1
Day-163: Flask for web app development (Part-6)
0/1
Day-164: Flask for web app development (Part-7)
0/2
Day-165: Flask for web app deployment (Part-8)
0/1
Day-167: FastAPI (Part-2)
0/1
Day-168: FastAPI (Part-3)
0/1
Day-169: FastAPI (Part-4)
0/1
Day-170: FastAPI (Part-5)
0/1
Day-171: FastAPI (Part-6)
0/1
Day-174: FastAPI (Part-9)
0/1
Six months of AI and Data Science Mentorship Program
    Join the conversation
    Muhammad Rameez 4 weeks ago
    https://www.kaggle.com/muhammadrameez242 , ye meri kaggle ki I'd hai yha par Ap mukamal assignments simple roman urdu mein mill jai gi codes mein
    Reply
    Rana Anjum Sharif 4 weeks ago
    Done
    Reply
    Shahid Umar 6 months ago
    Very detailed understanding of evaluation metrics for classification techniques and formula usage in these metrics.
    Reply
    tayyab Ali 6 months ago
    I learned in this lecture Accuracy, Precision, Recall, and F1 score. Confusion matrix.
    Reply
    Sibtain Ali 6 months ago
    I learned in this video Accuracy, Precision, Recall, and F1 (Confusion metrics).
    Reply
    Javed Ali 6 months ago
    AOA, In this lecture, I learned how evaluation metrics help us measure model accuracy. We know that there are some types of machine learning algorithms and I also learned about Classification metrics which are1-Accuracy Description: Proportion of correctly predicted observations to the total observations. Pros: Simple and intuitive. Cons: Can be misleading in imbalanced datasets. Example: For 100 predictions with 90 correct predictions, accuracy is 90%2-Precision Description: Proportion of correctly predicted positive observations to the total predicted positive observation. Pros: It focuses on the relevancy of results. Cons: Does not consider true negative results. Example: For 30 true positive prediction out of 40 total positive predictions, precision is 75%3-Recall (Sensitivity)Description: Proportion of correctly predicted positive observations to all observation in actual class Pros: It is useful in cases where False negatives are costly. Cons: Can lead to ignoring the true negatives. Example: For 30 true positive predictions out of 40 actual positive instances, recall is 75% 4-F1 ScoreDescription: Harmonic means of Precision and Recall. Pros: Balances Precision and Recall. Cons: May not be a good measure when there is an imbalance between precision and Recall Example: With Precision =75% and Recall = 75% F1 Score is 2*(0.75*0.75)/(0.75+0.75) = 75%5-Area Under the ROC Curve (AUC-ROC)Description: Measures the ability of a classifier to distinguish between classes Pros: Effective for binary classification problems Cons: Less information for multi-class problems Example: If the AUC is 0.90, there is a 90% chance that the model will be able to distinguish between positive and negative6-Confusion Matrix Description: A table showing actual vs predicted values Pros: Provides a detailed breakdown of correct and incorrect classification Cons: More complex to interpret Example: A matrix showing TP, FN, FP, and TNALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey or ap k walid-e-mohtram ko karwat karwat jannat ata farmay,Ameen.
    Reply
    0% Complete