Python Machine Learning Course, 2-Days, Webinar, Online Attendance

Python Machine Learning Course, 2-Days, Webinar, Online Attendance

Actions and Detail Panel


This excellent online instructor led course is an introduction to Machine Learning Concepts. Also code using the most popular models.

About this event

Python Machine Learning 2-day Course

Prerequisites Basic knowledge of Python coding is a pre-requisite. Bring your own device, or arrange to use ours.

Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn.

Course Outline:

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 a short selected but representative list of available 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  
  • 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.

Share with friends