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
    This lecture contains information about the definition, components, structure, and history of Neural Network
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    Sibtain Ali 5 months ago
    A neural network is a computational model inspired by the way biological neural networks in the human brain work. It is used for various tasks such as pattern recognition, classification, regression, and decision-making. Neural networks consist of interconnected nodes or neurons organized in layers, and these networks are trained on data to learn patterns and make predictions. (1. Input layer, 2. Hidden layers and 3.Output layers).
    Reply
    tayyab Ali 5 months ago
    A neural network is a computational model inspired by how biological neural networks work in the human brain. It is used in machine learning and artificial intelligence to solve various tasks, such as pattern recognition, classification, regression, and decision-making. Neural networks consist of interconnected nodes, or artificial neurons, organized into layers. The three main types of layers in a neural network are. 1. Input Layer: 2. Hidden Layers: 3. Output Layer:
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    Javed Ali 5 months ago
    I learned in this lecture about the neural network ( functional unit of deep learning). We will see how deep learning and neural networks are linked to each other. First of all, Deep Learning is a subtype of Machine Learning and Machine Learning is a subtype of AI And AI is a subtype of Algorithms.NEURAL NETWORK: A neural network is a series of algorithms that endeavor to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.And I also learned about the components of neural network1- Neurons 2-Layers (a)Input layer (b)Hidden layer (c)Output layer 3-Weights and Biases 4-Activation FunctionsALLAH 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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