About Machine Learning workshop
Participants will learn the end-to-end process of investigating data through machine learning techniques. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.
Two days Machine Learning workshop that focuses on key modules such as Python, Algorithms, Statistics & Probability, Supervised & Unsupervised Learning, Decision Trees, Random Forests, Linear & Logistic regression, etc. With these key concepts, attendees will be well prepared for the role of Data scientists.
Two days Machine Learning workshop provides a detailed overview of Machine Learning topics such as: Using real-time data, creating algorithms using different ML techniques, Regression, Classification, and Time Series Modelling. This Machine Learning workshop covers the most popular and widely used Deep Learning technologies and their applications, as well as Natural Language Processing, thus, paving the way for a solid foundation of Machine Learning.
Machine Learning Course Outlines
- Introduction to Machine Learning
- Find out where Machine Learning is applied in Technology and Science.
- Introduction to supervised learning.
- types of supervised learning
- introduction to regression, simple linear regression, multiple linear regression
- Introduction to classification.
- Introduction to tree-based classification.
- Implementing a decision tree from scratch in Python
- Introduction to probabilistic classifiers & understanding Naïve Bayes.
- Types of unsupervised learning
- Identify the difference between Unsupervised Learning and Supervised Learning.
- Introduction to Deep Learning with neural networks.
Machine Learning Projects
- Customer Churn Classification.
- Insurance Cost Prediction.
- Diabetes Classification.
- How machine learning uses computer algorithms to search for patterns in data
- How to uncover hidden themes in large collections of documents using topic modeling.
- How to use data patterns to make decisions and predictions with real-world examples
- How to prepare data, deal with missing data and create custom data analysis solutions for different industries.
Introduction to Machine Learning
Supervised Learning and Linear Regression
Introduction to classification
Support Vector Machines
Introduction of Decision Trees & Random Forest
Introduction of Clustering