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
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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
    Faizan Ahmad 6 days ago
    Aj e bari eid ka din ha, aur aj e yeh misaal ... <3
    Reply
    Rana Anjum Sharif 4 weeks ago
    Done
    Reply
    Muhammad Rameez 4 weeks ago
    Done
    Reply
    Sibtain Ali 6 months ago
    I learned in this video Ensemble (Bagging (Bootstrap Aggregating), Bosting, and Stacking(Stacked Generalization))
    Reply
    tayyab Ali 6 months ago
    I learned in this lecture Ensemble Methods (Bootstrap Aggregating), Bosting, and Stacking(Stacked Generalization).
    Reply
    Shahid Umar 6 months ago
    This lecture contains the new concept of Ensemble Algorithms which is near to best in place of neural networks.
    Reply
    Javed Ali 6 months ago
    AOA, I learned in this lecture about the ML algorithm of Ensemble Methods and its types Which are1- Bagging ( aggregation) ( parallel tree growing with sub-samples ) 2- Boosting ( sequential tree growth with weighted samples ) 3- Stacking ( Stacked Generalization )And also learned the uses of Ensemble Algorithms which are1-For Accuracy 2-For Stability 3-For Reduced Overfitting I also learned about the applications of Ensemble Algorithms which are1- Finance (for credit scoring and algorithmic trading) 2- Healthcare (for disease prediction and diagnosis) 3- E-commerce (for recommendation systems) 4-Stock market ( prediction )I also learned the disadvantages of the Ensemble Algorithm which are1-Complexity ( computationally expensive and take more time ) 2-Interpretability ( harder to interpret ) 3-Parameter Tuning ( requires careful tuning of parameters )I also learned the advantages of the Ensemble Algorithm which are1-Enhanced accuracy of the model 2-Robust the model 3-Generalized the model 4-Improve the model 5-Versatility ( used for both classification and regression tasks) 6-Easy to use ( requires little tuning of parameters )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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