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.