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Python Machine Learning 2-Day Course

Course summary


Prerequisites
Basic knowledge of Python coding is a pre-requisite.
Bring your own device.
Who Should Attend?
This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn.
This course is also suitable for programmers who may have knowledge of general Python Coding.

Course Outline:

Python Machine Learning Course, 2 Days

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage this to gain insights and ultimately improve business. Using Python scikit-learn, attendees will practice how to use Python Machine Learning algorithms to perform predictions on their data.

Learn how to implement Python functions for machine learning and code and implement algorithms to predict future data.

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 example code to implement the algorithms

    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:

  • Reinforcement learning Algorithms: Q-Learning
  • Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting
  • 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.

    Feature Engineering: Injecting domain knowledge in the process, attributes are extracted from the data and engineered into Machine Learning algorithms.




What is included in this Python Machine Learning:


Python Machine Learning Certificate on completion (assessment based)
Python Machine Learning notes
Practical Python Machine Learning exercises, Python Machine Learning Homework / Python Machine Learning Revision work
Tea, coffees, but no lunch
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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