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 2 weeks ago
    Done
    Reply
    Muhammad Abdullah Jamal Khan 6 months ago
    yes its right that depend on our data , wanted solutions and nature of data. in my opinion now a days neutral network mostly used for such a big data of the industry.
    Reply
    Shahid Umar 6 months ago
    This lecture contains the detailed answer to "Can Boosting Algorithms be better than Neural Networks?"
    Reply
    Sibtain Ali 6 months ago
    Did I learn in this video this question: Can boosting algorithms be better than neural networks? The choice between boosting algorithms and neural networks depends on the specific problem you are trying to solve, the nature of your data, and various other factors. Both boosting algorithms and neural networks are powerful techniques, but they have different strengths and weaknesses. Boosting algorithms, such as AdaBoost, Gradient Boosting, and XGBoost, are ensemble methods that combine the predictions of multiple weak learners (typically decision trees) to create a strong model. They are often effective for structured/tabular data and are known for their simplicity, interpretability, and ability to handle outliers well. Boosting algorithms can perform well with relatively small amounts of data and are less prone to overfitting. Neural networks, on the other hand, are powerful models that can capture complex relationships in data. They are particularly well-suited for tasks such as image recognition, natural language processing, and other tasks involving large amounts of unstructured data. Neural networks can automatically learn hierarchical representations of features, making them suitable for tasks with intricate patterns and dependencies.
    Reply
    tayyab Ali 6 months ago
    Did I learn in those lecture this question: Can boosting algorithms be better than neural networks? The choice between boosting algorithms and neural networks depends on the specific problem you are trying to solve, the nature of your data, and various other factors. Both boosting algorithms and neural networks are powerful techniques, but they have different strengths and weaknesses. Boosting algorithms, such as AdaBoost, Gradient Boosting, and XGBoost, are ensemble methods that combine the predictions of multiple weak learners (typically decision trees) to create a strong model. They are often effective for structured/tabular data and are known for their simplicity, interpretability, and ability to handle outliers well. Boosting algorithms can perform well with relatively small amounts of data and are less prone to overfitting.Neural networks, on the other hand, are powerful models that can capture complex relationships in data. They are particularly well-suited for tasks such as image recognition, natural language processing, and other tasks involving large amounts of unstructured data. Neural networks can automatically learn hierarchical representations of features, making them suitable for tasks with intricate patterns and dependencies.
    Reply
    Javed Ali 6 months ago
    I also learned the answer to a very important question in this lecture and that question is: Can boosting algorithms be better than neural networks?) The answer to the question is thisAnswer: It is not about one being universally better than the other; it is about choosing the right tool for the job. The decision should be based on the specific requirements of the task, the nature of the data, available resources, and the desired outcome. Often, the best approach might even be a hybrid one, leveraging the strengths of both boosting algorithms and neural networks. ALLAH 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
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