Description:
Description
Data Science is a multidisciplinary field that deals with the study of data. Data scientists have the ability to take data, understand it, process it, and extract information from it, visualize the information and communicate it. Data scientists are well-versed in multiple disciplines including mathematics, statistics, economics, business, and computer science, as well as the unique ability to ask interesting and challenging data questions based on formal or informal theory to spawn valuable and meticulous insights. This course introduces students to this rapidly growing field and equips them with its most fundamental principles, tools, and mindset.
Students will learn the theories, techniques, and tools they need to deal with various datasets. We will start with Regression, one of the basic models, and progress as we evaluate and assessing different models. We will start from the initial stages of data science and advance to higher levels where students can write their own algorithm from scratch to build a model. We will see end to end and work with practical datasets at the end of each module. Students will be issued with tutorials and explanation of all the exercises to help you learn faster and enable you to link theory using hands on exercises.
This course teaches advanced theory including some mathematics with practical exercises to promote deeper understanding.
Learning Outcomes
At the end of the course the students will:
Have an in-depth understanding of the concepts of Machine Learning
Be able to grasp, understand, and write machine learning code from scratch
Use Builtin Libraries available to build machine learning models
Be able to analyze, build, and assess models on any dataset
Be able to interpret and understand the black box behind model
Understand the applications of data science by exhibiting the ability to work on different datasets and interpreting them.
What is the working system of this course?
Strong concepts and theory linked to practical at the end of each module
Easy Lectures for those starting from scratch
Illustration and examples
Hands-on exercises with tutorials
Detailed explanations of how models work
What does this course cover?
Introduction to machine learning: Overview of supervised and unsupervised learning
Regression from scratch – Gradient Descent, Cost Function , Modelling
Using Machine learning builtin library
Feature Scaling
Multivariate Regression
Polynomial Regression
Over-fitting, Under-fitting and Generalization
Bias Variance Tradeoff
Cross Validation Strategy and Hyper-parameter tuning
Grid Search
Learning Curves
Decision Trees and introduction to other algorithms including neural network
Exercises after each module
After completing the course, you will have enough knowledge and confidence to code machine learning algorithms from scratch and to use built-in library. This cou[/b]rse is for all interested in learning data science and machine learning, there is no such pre req. This course is different from other courses in a manner that it teaches to code algorithms and also exposes you to the mathematics behind machine learning, this even includes tutorials at the end of each module so that students can do side by side practice with the instructor. It exposes you to practical real world datasets to work on and get started with new problems.
[b]Who this course is for:
Curious about Data Science
People wishing to learn Machine Learning from scratch
People of different domains – Business Analyst, Marketing, etc
Seeking job in the areas of machine learning
Requirements
No Such Pre-req, its good to have some basic math concepts
Last updated 3/2019 |
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