Prerequisites: Basic knowledge of Python coding is a pre-requisite.
Who Should Attend? Data people who already know the basics of Python.
Bring your own device.
2 consecutive days, the first of which shows as the booking date.
Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of 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.
In this course we learn how to implement Python functions for machine learning.
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.
Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine
Regression Algorithms: Linear, Polynomial
Clustering Algorithms: K-means clustering, Hierarchical Clustering
Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)
Association Machine Learning Algorithms: Apriori, Euclat
Reinforcement learning Algorithms: Q-Learning
Stacking, bagging, boosting.
Random Forest, Gradient Boosting
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.
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