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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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Sibtain Ali 6 months ago
I have done this video with 100% practice.
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tayyab Ali 6 months ago
I have done this lecture with 100% practice.
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Muhammad Naeem 7 months ago
Done
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Javed Ali 7 months ago
AOA, Day-28: Mastering NumPy for Data Science and AI (Part-1) is done with 100% practice.
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Javed Ali 7 months ago
1D Arrays:Usage: 1D arrays, also known as vectors, are used to store and manipulate a collection of data points in a single dimension.2D Arrays:Usage: 2D arrays, also known as matrices, are used to represent data in a two-dimensional grid format with rows and columns.Multidimensional (nD) Arrays:Usage: Multidimensional arrays extend the concept to more than two dimensions, allowing for the representation of complex data structures.
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Sana Shah 7 months ago
interesting numpy
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Shahid Umar 7 months ago
Basic coding concept of the numpy library in this lecture. Numpy is used for array operations which is the fundamental of data structure.
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Mohammad Sohail 8 months ago
complete Alhamdulila
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SHAFIQ TOOR 8 months ago
In data science, arrays are fundamental data structures used to store and manipulate data. They are used to represent and work with multi-dimensional data, such as numerical values, images, time series, and more. The three most common types of arrays used in data science are 1D (one-dimensional), 2D (two-dimensional), and 3D (three-dimensional) arrays.
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SHAFIQ TOOR 8 months ago
3D Arrays:Definition: A 3D array is an extension of the 2D array, where elements are arranged in a three-dimensional structure with multiple layers. Usage in Data Science: Representing volumetric data, such as medical imaging data (e.g., MRI or CT scans). Storing time series data with multiple variables, such as sensor data with measurements over time for different sensors. Representing video data, where each element represents a pixel value at a specific time frame. These array structures are used extensively in data preprocessing, feature engineering, model training, and model evaluation in various data science tasks, including machine learning, image processing, signal processing, and more. The choice of array type depends on the nature and requirements of the data being analyzed or processed.
SHAFIQ TOOR 8 months ago
2D Arrays:Definition: A 2D array, also known as a matrix or a two-dimensional table, is a collection of elements arranged in a grid-like structure with rows and columns. Usage in Data Science: Storing tabular data, such as datasets in a spreadsheet-like format. Representing images, where each element represents a pixel value. Working with mathematical operations that involve matrices, such as linear algebra and matrix factorization.
SHAFIQ TOOR 8 months ago
1D Arrays:Definition: A 1D array, also known as a vector or a one-dimensional sequence, is a collection of elements arranged in a linear manner. Usage in Data Science: Representing a series of data points, such as stock prices over time. Storing feature vectors in machine learning algorithms, where each element represents a feature. Representing time series data, such as temperature measurements over time.
SHAFIQ TOOR 8 months ago
Why we have 1D, 2D and 3D arrays and where we need them in data science?
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