Python Machine Learning : Learn Handson™ Free Download

Description

Free Download Python Machine Learning : Learn Handson™, with this course you will have the option to Naive Bayes Classifier, Decision tree, PCA, kNN classifier, linear regression, logistic regression,SVM classifier. It contains 63 recordings. Remember that numerous understudies are tried out this course Python Machine Learning : Learn Handson™, so don't hold on to download it presently, it's totally free. You can start learning Python Machine Learning : Learn Handson™ by clicking the download link below.

Before you can start learning from this course explained in English (US) you need to have Knowledge of computer, Basic knowledge in math and statistics.

Python Machine Learning : Learn Handson™ is targeting for people that have interset in Anyone interested in Machine Learning, Any students in college who want to start a career in Data Science.

Finally you will learn how to Linear Regression, SVR, Decision Tree Regression, Random Forest Regression, Machine Learning, Deep Learning, AI and Data Science Basic Concepts, Python package “Numpy” for numerical computation, Python package “Matplotlib” for visualization and plotting, Python package “pandas” for data analysis, Polynomial Regression, Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification, Clustering: K-Means, Hierarchical Clustering, Data Visualization in Python with MatPlotLib and Seaborn, Dimensionality Reduction: PCA, PCA sklearn, Supervised Learning & Unsupervised Learning, Support Vector Machine, Curse of Dimensionality, Neural Networks, Applications of ML/AI/DS and Job prospects, 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), Linear Algebra Review: Eigen value decomposition, Multi-layered Perceptron (MLP) and its architecture, Learning Rule : Back-Propagation, High dimensionality in data set and its problems, Environment Setup : Anaconda and Jupyter Notebook, Using in-built Python libraries for solving linear regression problem, Python implementation of Gradient Descent update rule for logistic regression.

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