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Cybertec Introduction To Machine Learning

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Details

Artificial Intelligence may be our generation's most exciting pursuit, and, in order for a machine to be intelligent, it must be able to learn. Machine learning is the process by which computer systems access data, run experiments, and learn from experience, much the same way we do. In this course, you will learn how to set up these systems and how they might benefit your business.

This dynamic course covers both theoretical explanations and practical activities, which comprise over 60% of the course. The main goal is that participants face “real” problems, experience consequential challenges and learn to come up with a solution – guided by the instructor at all stages of the process.

Delivery Methods

Leading Training is focusing on providing virtual training courses for the foreseeable future and will only consider in-person and classroom training on request, with a required minimum group size of four delegates. We remain committed to offering training that is fast, focused and effective.

Audience

This course is intended for technical analysts or mid-level managers who are willing to do their first steps in Machine Learning.

Pre-Requisites

Analysts/technical managers with at least 1 year of programming experience (Ideally, also experience in Python)

Course Outline / Curriculum

Day 1:

Machine Learning: Introduction and explanation of main concepts.

About Python/Jupyter

Overview of main Python libraries to be used

Exploratory Data Analysis: in theory

Exploratory Data Analysis: in practice

 

Day 2:

Supervised Learning: Introduction and explanation of main concepts.

  • Explanation of Training Process: Training/Validation/Testing, Cross-validation
  • Recap: Regression vs Classification
  • Cost/Loss functions
  • What is done in training? Minimization of Loss Function. Example Algorithms
  • Performance Evaluation
  • Steps for successful Model building

Regression

 

Day 3:

Classification

  • Classification Algorithms
  • Ensemble Algorithms
  • Performance Evaluation

 

Day 4:

Unsupervised Learning

  • Unsupervised Algorithms
  • Ensemble Algorithms
  • Performance Evaluation

From lab to production: challenges and common problems

The importance of distributed computing in Machine Learning

 

Day 5:

Introduction to neural networks

  • Definition
  • Main Concepts
  • Tensorflow Playground demo
  • Activation Functions
  • NN training process
  • Multi-class Problems and Softmax
  • Convolutional NN
  • Keras Demo and Explanation of Keras Library
  • Transfer Learning
  • Hyperparameter Tuning

Deep Learning: What is it and where can it be applied?

Machine Learning in my organization: How can I implement ML considering the current problems we face?

Final summary, questions, suggestions, etc.