1-Day Machine Learning with Scikit Learn for Python (LIVE Stream)
An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning
Scikit Learn: Machine Learning Using Python
Scikit-learn is a free machine learning library for Python. It is also the de facto Machine Learning package for Python. It consists of classification, regression, clustering, dimension reduction, model selection, and many data preprocessing functionalities. You can do many supervised and unsupervised machine learning with Scikit Learn. It also features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports Python numerical and scientific libraries like NumPy and SciPy.
Learn & Add this In-Demand Skills to Your Skills Set
This Machine Learning with Scikit Learn for Python training aims to equip you with fundamental machine learning knowledge using such as classification algorithms and classification metrics, ensemble methods, regression and regularization, K-Means and Hierarchical Clustering and feature reduction with PCA. By using concrete examples and minimal theory, you will gain an intuitive understanding of the concepts and tools for building intelligent systems and machine learning processes.
- Explore the machine learning landscape, particularly neural nets
- Use scikit-learn to track an example machine-learning project end-to-end
- Supervised Learning vs Unsupervised Learning
- Analysing Classification Models with F1 Score and AUC
- Multivariate Linear regression
- Ridge and Lasso Regularization to reduce overfitting
- Silhouette Analysis and Dendrogram for Clustering
- Apply practical code examples without acquiring excessive machine learning theory or algorithm details
Who Should Attend?
- Suited for Marketers, Engineers, Finance, Sales, Service, Business Development and Marketing Analysis seeking to gain deeper understanding of using Python for data analysis.
- Anyone should be able to learn the essentials of Python programming language and all participants should be greatly benefit from it.
- Existing programmers, data scientists and engineers with development experience in another high-level programming or scripting language such as C/C++, Perl, PHP etc. will also benefit by learning the Python Programming Language.
- No prior background of programming knowledge and experience
Note: Participant is required to bring their own laptop with access to internet (WiFi network will be provided)
Data Scientist: Dr Sudipta Samanta, Senior Research Engineer at Temasek Life Sciences Lab
Dr. Sudipta Samanta has over 15 years of research and teaching experience. Dr Sudipta received his Ph.D. for his research in the field of computational Biophysics and has over 10 years of research experience as a Research Scientist. His current interests include Machine Learning, Deep Learning, Health Care Data analysis and computer simulation. Prior to joining Temasek, Dr Sudipta was Visiting Scientist of Internationally renowned MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), USA from 2011 to 2018 and a Research Scientist at Singapore-MIT Alliance for Research & Technology.
Topic 1 Overview of Machine Learning and Scikit Learn
• Introduction to Machine Learning
• Supervised vs Unsupervised Learnings
• Machine Learning Applications and Case Studies
• What is Scikit Learn?
• Installing Scikit-Learn
Topic 2 Classification
• What is Classification?
• Different Machine Learning Algorithms for Classification.
• KNN Classifier
• Classification Workflow
• Decision Trees
• Support Vector Machine
• Fully connected Neural Network for classification
• Confusion Matrix
Topic 3 Regression
• What is Regression?
• Regression Workflow
• Simple Linear Regression
• Regression exercise
Topic 4 Clustering
• What is Clustering?
• Unsupervised learning with Clustering
• K-Means Clustering
• Dendrogram and Hierarchical Clustering
Topic 5 Principal Component Analysis
• Curse of Dimensionality!
• What is Principal Component Analysis (PCA)
• Dimension Reduction with PCA
Topic 6 Build Machine Learning based model with Diabetic dataset