This Python Machine Learning online instructor led course is an excellent
introduction to popular machine learning algorithms.
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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.
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REFUND POLICY
No Refunds