Course Content
How and Why to Register
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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
    Najeeb Ullah 5 days ago
    installed tensorflow and pytorch in a separate environment
    Reply
    Muhammad Adil Naeem 1 month ago
    I have installed TensorFlow in tf_env and Pytorch in pytorch_env.
    Reply
    Fatima Majeed 5 months ago
    Chainer MXnet Pytorch Keras(for experiment) Tensorflow(for development)
    Reply
    tayyab Ali 5 months ago
    I have done this assignment.
    Reply
    Sibtain Ali 5 months ago
    I have done this assignment.
    Reply
    Javed Ali 5 months ago
    Assigements of Deep learningHow many libraries can be used in deep learning and machine learning tasks?1-TensorFlow: an open-source library for deep learning and numerical computations, developed by Google.2-PyTorch: a dynamic computational framework for building deep learning models, known for its research-friendly interface.3-Keras: A high-level deep learning library built on top of TensorFlow, providing an easy-to-use API for neural network construction.4-Scikit-learn: is a popular machine learning library in Python, offering a wide range of algorithms and tools for various tasks.5-Theano: Python library for efficient mathematical operations and optimization, commonly used as a backend for deep learning frameworks.6-Caffe: A deep learning framework known for its speed and efficiency, particularly for computer vision applications.7-MXNet: A flexible and efficient deep learning library supporting both imperative and symbolic programming.8-Microsoft Cognitive Toolkit (CNTK): Deep learning library by Microsoft, offering efficient computation and distributed training capabilities.9-H2O: Open-source machine learning platform with a high-level API for building and deploying models, capable of scaling to big data environments.10-Spark MLlib: A machine learning library provided by Apache Spark, offering distributed implementations of various algorithms.
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