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This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms. Python Machine Learning 2-day Course Prerequisites: Basic knowledge of Python coding is a pre-requisite. Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn. Practical: We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms. Course Outline: Supervised Machine Learning: Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine Regression Algorithms: Linear, Polynomial Unsupervised Machine Learning: Clustering Algorithms: K-means clustering, Hierarchical Clustering Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) Association Machine Learning Algorithms: Apriori, Euclat Other machine learning Algorithms: Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting Reinforcement learning Algorithms: Q-Learning Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN) Data Exploration and Preprocessing: The first part of a Machine Learning project understands the data and the problem at hand. Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. What is included in this Python Machine Learning: Python Machine Learning Certificate on completion Python Machine Learning notes Practical Python Machine Learning exercises and code examples After the course, 1 free, online session for questions or revision Python Machine Learning. Max group size on this Python Machine Learning is 4. Refund Policy No Refunds

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