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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    Shahid Umar 4 months ago
    Discussed 14 types of multi-layer perceptron (MLP) through slides i,e shallow neural networks, deep neural networks, classification MLPs, regression MLPs, feedforward MLPs, Recurrent nueral networks MLPs, sigmoid MLPs, ReLU MLPs, softmax MLPs, linear output MLPs, static MLPs, dynamic MLPs, basic MLPs, complex MLPs
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    Sibtain Ali 5 months ago
    I learned in this lecture the types of multi-layer perception 1. Based on the Number of Hidden Layers, 2. Based on Task, 3. Based on Activation Function, 4. Based on Network Topology, 5. Based on Output Layer Function, 6. Based on Adaptation Mechanism, and 7. Based on the Complexity of the Task.
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    tayyab Ali 5 months ago
    I learned in this lecture the types of multi-layer perception 1. Based on the Number of Hidden Layers, 2. Based on Task, 3. Based on Activation Function, 4. Based on Network Topology, 5. Based on Output Layer Function, 6. Based on Adaptation Mechanism, and 7. Based on the Complexity of the Task.
    Reply
    Javed Ali 5 months ago
    I learned in this lecture about types of MLP based on features that are1-Based on the Number of Hidden Layers(a)-Shallow NN ( one hidden layer, simple classifications and regressions tasks) (b)-Deep NN ( multiple hidden layers, image and speech recognition, NLP )2-Based on Task(a)-Classification MLPs ( output a discrete label or class, image classification, text categorization ) (b)-Regression MLPs ( predict a continuous output, real stat, stock market, temp prediction )3-Based on Activation Function(a)-Sigmoid MLPs ( use sigmoid function in hidden layers, early NN applications, binary classifications tasks ) (b)-ReLU MLPs ( utilize Rectified Linear Unit ReLU activation function )4-Based on Network Topology(a)-Feedforward MLPs ( standard form, no cycles in connections, common use of classification and regression ) (b)-Recurrent NN (RNN) ( loops in connections, time series analysis sequential data processing, language modeling, speech recognition )5-Based on Output Layer Function (a)-Softmax MLPs ( softmax function in the output layer for categorical probability distributions, multi-class classifications such as digit recognition, text classification ) (b)-Linear Output MLPs ( linear activation function in the output layer, regressions tasks where the output is continuous )6-Based on Adaptation Mechanism(a)-Static MLPs ( constant architecture and neuron parameters after training, tasks with consistent data pattern where adaptability is less crucial ) (b)-Dynamic MLPs ( adapt structure and neuron parameters based on input data or learning task, tasks requiring ongoing learning )7-Based on the Complexity of the Task(a)-Basic MLPs ( simpler task, entry-level neural network project ) (b)-Complex MLPs ( design for complex tasks, large-scale deep learning, high dimensional data analysis )ALLAH PAK aap ko sahat o aafiyat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey aur aap ke walid-e-mohtram ko karwat karwat jannat ata farmaye,Ameen.
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