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Data Science with MATLAB MasterClass
Data Analysis and Machine Learning, Fintech
Data Science with MATLAB MasterClass (NEW)
Learn how to use MATLAB® and Statistics & Machine Learning Toolbox™ to perform statistical analysis with distribution fitting, regression, and hypothesis testing.
Complimentary Services: Post training email support & 1-hr consultation session within 1 month after the course completion!
Course
Highlights
Do you find yourself in an industry or field that increasingly uses data to answer questions? Are you working with an overwhelming amount of data and need to make sense of it? Do you want to avoid becoming a full-time software developer or statistician to do meaningful tasks with your data?
Data science uses scientific methods to gain useful information from data and apply knowledge from data over wide range of applications. In recent years, there are vast amount of data from many sources. The data needs to be processed to process to gain meaningful insight to build predictive model using complex machine learning algorithms.
Attend our 3-day Data Science with MATLAB MasterClass to learn the essential MATLAB hands-on skills you need to achieve practical results in Data Science quickly. Being able to visualize, analyze, and model data are some of the most in-demand career skills from fields ranging from healthcare to the auto industry, to tech startups.
This three-day course provides hands-on experience to perform statistical data analysis, machine learning techniques in MATLAB®, and practical deep learning using MATLAB®. The course examples and exercises demonstrate the use of appropriate MATLAB and Statistics and Machine Learning toolbox™ functionality throughout the analysis process; from importing and organizing data, to exploratory analysis, to confirmatory analysis and simulation. It demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. The introduction to practical deep learning using Deep Learning toolbox™ will be covered to provide attendees with additional selection of method to work with data. Topics include:
- Managing data
- Calculating summary statistics
- Visualizing data
- Fitting distributions
- Reducing data sets
- Organizing and preprocessing data
- Clustering data
- Creating classification and regression models
- Importing image and sequence data
- Transfer learning for image classification
- Interpreting deep network behavior/li>
Who Should
Attend
The course is intended for data scientists, engineers, researchers who take raw data and putting it in a form that can be used, examine its patterns, ranges, and biases to determine the usefulness of the information in predictive analysis using MATLAB.
Course
Prerequisites
MATLAB Fundamentals or equivalent experience using MATLAB®; and knowledge of basic statistics
Course
Benefits
Upon the completion of the course, the participants will be able to:
- Import, organize, and preprocess data from fil
- calculate summary statistics
- Create visualizations of data
- fit model data
- Reduce the dimensionality of large data sets
- Use unsupervised learning techniques to discover natural groupings within a data set
- Use supervised learning techniques to predict a continuous or categorical response from many variables
- Import image and sequence data for deep network
- Perform image classification using convolutional neural networks
- Interpret deep network behavior
- Use pre-trained deep network and create custom deep network
Partners
Upcoming Program
- Please keep me posted on the next schedule
- Please contact me to arrange customized/ in-house training
Course Outline
Importing and Organizing Data
Objective: Bring data into MATLAB and organize it for analysis. Perform common tasks, such as merging data and dealing with missing data.
- Importing data
- Data types
- Tables of data
- Merging data
- Categorical data
- Missing data
Exploring Data
Objective: Perform basic statistical investigation of a data set, including visualization and calculation of summary statistics.
- Plotting
- Central tendency
- Spread
- Shape
- Correlations
- Grouped data
Distributions
Objective: Investigate different probability distributions and fit distributions to a data set.
- Probability distributions
- Distribution parameters
- Comparing and fitting distributions
- Nonparametric fitting
Working with Multiple Dimensions
Objective: Simplify high-dimensional data sets by reducing the dimensionality.
- Feature transformation
- Feature selection
Finding Natural Patterns in Data
Objective: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.
- Unsupervised learning
- Clustering methods
- Cluster evaluation and interpretation
Building Classification Models
Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.
- Supervised learning
- Training and validation
- Classification methods
Building Regression Models
Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables.
- Parametric regression methods
- Nonparametric regression methods
- Evaluation of regression models
Transfer Learning for Image Classification
Objective: Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.
- Pretrained networks
- Image datastores
- Transfer learning
- Network evaluation
Interpreting Network Behavior
Objective: Gain insight into how a network is operating by visualizing image data as it passes through the network. Apply this technique to different kinds of images.
- Activations
- Feature extraction for machine learning
Creating Networks
Objective: Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.
- Training from scratch
- Neural networks
- Convolution layers and filters
