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    Machine Learning with  Python

    Machine Learning with Python
    Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 3.28 GB
    Genre: eLearning Video | Duration: 5.5 hour | Language: English

    What you'll learn

    Machine Learning, Deep Learning, AI and Data Science Basic Concepts
    Applications of ML/AI/DS and Job prospects
    Supervised, Un-supervised Learning
    Environment Setup : Anaconda and Jupyter Notebook
    Python package "Numpy" for numerical computation, Python package "MatDescriptionlib" for visualization and Descriptionting, Python package "pandas" for data analysis
    Basics of Probability Theory
    Understanding different types of data
    Examining distribution of the variables
    Examining relationship among variables
    Exploratory data analysis using Python
    Linear regression model / hypothesis
    Linear regression on bi-variate data
    Multivariate Regression
    Polynomial regression
    Python implementation of Gradient descent algorithm for regression.
    Using in-built Python libraries for solving linear regression problem.
    Logistic regression for binary classification problem.
    Logistic regression for multiclass classification problem.
    Python implementation of Gradient Descent update rule for logistic regression.
    Using Python built in library for logistic regression problem.
    K-Nearest Neighbour Classifier, Naïve Bayes Classifier, Decision Tree Classifier, Support Vector Machine Classifier, Random Forest Classifier (We shall use Python built-in libraries to solve classification problems using above mentioned classification algorithms)
    High dimensionality in data set and its problems.
    Linear Algebra Review: Eigen value decomposition.
    Feature Selection and Feature Extraction techniques
    Principal Component Analysis (PCA)
    Implementation of PCA in python.
    k-Means clustering algorithm and its limitation
    Implementation of k-Means clustering algorithm in python
    Hierarchical Clustering.
    Implementation of Hierarchical clustering in Python.
    Perceptron and its learning rule and its limitations.
    Multi-layered Perceptron (MLP) and its architecture.
    Learning Rule : Back-Propagation
    Building an MLP in Python.


    Mathematics Prerequisite : Basic concepts of Function & Curve tracking, basics of Multivariable Calculus : Partial Derivatives, Optimization : finding maxima and minima of a function, Linear Algebra: Vector & Matrices
    Statistics Prerequisite : Basic Concepts of frequency distribution and histogram Description, Cumulative frequency distribution and ogive, Basic understanding of probablity
    Python Prerequisite : Basic Idea, Data Type, Function, OOPS concepts


    This course will guide you to learn this thing:

    Installation of Anaconda Distribution and Jupyter Notebook

    Introduction to NumpyIntroduction to MatDescriptionlib

    Introduction to PandasProbablity Theory

    IntroductionExploratory Data Analysis

    Basic ConceptsDistribution of Variable

    Anyone interested in Machine learning can take the course.

    Here in the course, we are going to use Python as a Programming Language.

    Who this course is for:

    Anyone interested to learn Machine Learning with Python

    Machine Learning with  Python

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    Author: mitsumi 11-11-2019, 19:24 Comments: 0 Download: 216
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