Data Science Course Information
This course will familiarize students with a broad cross-section of models and algorithms for machine learning and prepare them for research or industry application of machine learning techniques. On the successful completion of this course
Duration: 10 days (60 Hours)
- Week day Batches: ( Mon to Fri 10am to 4pm ) 10 Days
- Weekends Batches: ( Sat & Sun 10am to 4pm ) 5 weekends
This course will familiarize students with a broad cross-section of models and algorithms for machine learning and prepare them for research or industry application of machine learning techniques. On the successful completion of this course, students will be able to:
- Source and access data from a variety of databases;
- Select and apply appropriate tools for data visualization;
- Select and apply descriptive data analytics methods;
- Select and apply predictive data analytics methods;
- Fit statistical models;
- Use the results to produce business intelligence in a variety of settings;
- Discover trends in analytical data stores using the data mining techniques of clustering, segmentation, association, and decision trees; and
- Present data visually for clear communication to a managerial audience
Setup a basic Python environment, create virtual environments and learn how to install Python packages.
Learn the fundamentals about a Linux environment – how to navigate through the filesystem and modify files.
Learn to keep track of changes and iterations using git source control from your terminal.
Use the Python and NumPy to navigate through data sources
Intro. to data Visualization
Learn how to plot and visualize data by using Python plotting libraries such as matplotlib and seaborn
Learn about some of the most common data formats, like CSV, XML and JSON files and how to handle them.
Use Pandas to read, clean, parse, and plot data using functions such as Boolean, indexing, math series, joins, and others
Learn to scrape website data using popular scraping tools
Use scikit learn and stats models to run linear and logistic regression models and learn to evaluate model fit
Dive into the math and theory of how gradient descent helps to optimize loss function for regression models
Use feature selection to deepen your knowledge of study design and model evaluation.
Feature engineering & pre-processing
Learn about different the possible ways of encoding the data features and their advantages and disadvantages.
Learn about the fundamentals of Artificial Neural Networks, – how do they work, their advantages and how to train them.
Backpropagation and Optimizers
Learn how the backpropagation algorithm works and is able to train neural networks. See what different optimizers are available and their advantages in the training process.
Learn how to apply regularization in order to avoid problems in the training such as overfitting.
Introduction to TensorFlow
Build simple artificial neural network models with TensorFlow, a differentiable programming framework
Introduction to the Keras framework for Python and how to use it to build neural network models
Learn how to use clustering algorithms in order to visualize and interpret classification tasks.
Dive into the world of image classification problems by using Deep Convolutional Neural Networks. (D-CNNs)
Understand how data augmentation can be used to help models to learn better, and to artificially increase the size of the original dataset.
Learn how adversarial examples can be used to trick a neural network into outputting wrong results and how to deal with this problem.
Learn the basic concepts and tasks of natural language processing (NLP)
Recurrent Neural Networks (RNNs)
Use RNNs in order to model sequences of data
Long Short-Term Memory (LSTM)
Learn how LSTMs solve the common problems of RNNs and how can they used to perform machine learning tasks on large sequences
Time series analysis
Analyze and model time series data using LSTM networks.
Learn how to create a simple REST API to deploy your machine learning models
Use Docker to create isolated containers with all the required dependencies and
Learn the basics of the Amazon cloud infrastructure and how it can be used to train and deploy machine learning models.
- Instructor Led – Face2Face / class room training
- More interaction with student to faculty and student to student.
- Detailed presentations. Soft copy of Material to refer any time.
- Practical oriented / Job oriented Training. Practice on Software Tools & Real Time project scenarios.
- Mock interviews / group discussions / interview related questions.
- Test Lab is in Cloud Technology – to practice on software tools if needed.
- We discuss about the real time project domains.
- The teaching methods / tools / topics we chosen are based on the current competitive job market.
Also on this course we offer the following
- Hands on Experience
- Real Time project work
- Interview based Training
- Previous Educational Background in IT or experience in support of networking.
Expected Salary/ Pay Package
- Expected Salaries are as follows:
- For Contractors £400 to £500 per day
- Permanent Positions £50 to £120k per annum all depends on experience and skills set