Machine Learning Fundamentals

This three-day training is designed to provide IT professionals and developers with some experience in Python with a basic knowledge and experience on how to do machine learning. The training will cover the fundamentals of machine learning, including data preparation, Python libraries, regression algorithms, classification algorithms, clustering algorithms, neural networks, Natural Language Processing (NLP) and deployment of machine learning models. The training will be a mix of lectures, hands-on exercises, and case studies to ensure that participants gain practical experience and can apply their knowledge in real-world scenarios.

Audience and Prerequisites

This training is ideal for IT professionals, developers and others that want to learn the basics of machine learning. Participants should have a basic understanding of Python programming.

The training is suitable for:

• Developers and IT professionals who want to learn how to develop machine learning models
• Anyone who wants to learn the basics of machine learning and its applications in IT

What you will learn

At the end of the training, participants will have a good understanding of the concepts and techniques involved in machine learning, and they will be able to apply their knowledge in real-world scenarios.

Training Schedule

 

Session 1: Introduction to Machine Learning

• Definition of Machine Learnin
• Types of Machine Learning
• Applications of Machine Learning
• Importance of Machine Learning in IT

Session 2: Introduction to Python Libraries for Machine Learning

• Introduction to Numpy
• Introduction to Pandas
• Introduction to Matplotlib
• Introduction to Scikit-learn

Session 3: Data Preparation for Machine Learning

• Data Collection
• Data Pre-processing
• Data Cleaning
• Data Transformation

Session 4: Regression algorithms

• Introduction to Regression algorithms
• Linear Regression
• Decision tree
• Other types of Regression algorithms
• Model Evaluation Methods

Session 5: Classification Algorithms

• Introduction to Classification
• Logistic Regression
• Decision Trees
• Random Forest
• Model Evaluation Methods

Session 6: Clustering Algorithms

• Introduction to Clustering
• K-Means Clustering
• Hierarchical Clustering
• Model Evaluation Methods

Session 7: Neural Networks

• Introduction to Neural Networks
• Artificial Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Model Evaluation Methods

Session 8: Introduction to Natural Language Processing (NLP)

• Introduction to NLP.
• Basic concepts of NLP.
• Text preprocessing techniques.
• Some examples and use cases for NLP.

Session 9: Deployment of Machine Learning Models

• Introduction to Deployment.
• Model Deployment on Cloud.

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