Sat, Aug 08|
Data Science: Intro to Machine Learning
Gain real-time industrial experience of Data Science and Machine Learning projects under the guidance of experts. Commence your technical career along with strengthening your portfolio.
Time & Location
Aug 08, 2020, 7:30 PM – 9:00 PM GMT+5:30
About the Webinar
Did you know that an average salary of a Data Scientist in today's technical era is approximately $113,436, according to Glassdoor. The increasing demand has driven the organizations to hire people in order to organize the data efficiently and conveniently. A smart business decision would be to hire an employee with a skillset that is beneficial to the company which projects the boom of job requirements for data science and analytics by 364,000 openings to 2,720,000 as per report published in 2020. According to the U.S. Bureau of Labor Statistics, 11.5 million new jobs will be created by the year 2026.
What does Data Science mean?
Data is Facts about something that can be used in calculating, reasoning, or planning, expressed as numbers in a computer. The term Data Science was originally coined in 1998 by statistician Chien-Fu Jeff Wu, at University of Michigan.The Scientific study of data is known as data science which involves the scientific study of the creation, validation and transformation of data to create meaning. It also includes processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured by visualizing them using statistics, data mining, and predictive analytics.
Why Data Science?
- Sexiest job of 21st century as per Harvard Business Review
- Data storage has become relatively cheaper
- Computing capacity has increased
- Data-driven features/products, more personalized user experience
- Learning meaningful insight from data
- Application is enormous, applicable to various field
What does Machine Learning mean?
According to Wikipedia, machine learning is nothing but computers or machines having the ability to learn on its own without being explicitly programmed. It's a subfield of computer science. Computers apply statistical learning techniques to automatically identify patterns in data. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.
Why Machine Learning?
Regaining its charm, machine learning is becoming the essence of real-world technologies. Companies are continuous using these technologies to solve some of the critical problems that have been on the surface for a while. With the abundance of data, more and more patterns could be learned from using sophisticated processing power in a matter of seconds. This is to offer the end users basically people of us with ease of using tech and saving time. Producing reliable, repeatable decisions and results and helping out industries to make data-driven decisions.
Some of the real world use cases-
- Have you ever noticed your email account, how the spam and non-spam emails are automatically segregated?
- Google Page Ranking - search results shown by google; best ones on the top.
- Netflix automated movie recommendations
- Spotify learning about your music preference
- "You may also like", "people like you also bought" features on e-commerce websites
- "People you may know" on facebook
- Self-driving autonomous cars by Tesla, Google and a lot more
- Artificial Intelligence based personal assistant devices like Alexa, Google home, Siri constantly learning about your behavior and preferences
- Various chatbots assist you better while learning from other users at the same time using some natural language processing techniques.
- Fraud detection, banks are using machine learning techniques to flag fraudulent transactions and saves real customers from becoming victim
- All the genetic advancements happening like DNA pattern recognition
- Various data-driven health monitoring systems
- Financial quantitative maths for stocks trading
- Google maps suggesting you alternate path in real-time
- Facial recognition systems
- Targetted ad systems
And a lot more! We are heading towards an age where we will find machine learning in every walk of life. Various devices are going to learn from existing patterns and human interaction. Making our experience with technology more seamless and personalized.
If you are a student, looking to explore the career of a Data Scientist or an established engineer looking to learn a new skill, this workshop is for you.
This workshop will help you create a foundation to work in Data Science & Machine Learning domain. Along with the basic introduction and an in-class project session, we will also mentor you the with future scope of how real-world applications are built right from scratch.
Duration: 1.5 hours with 5 mins break
Prereq: Install Python 3.7 on your desktop/laptop.
You can either use Anaconda IDE with installed jupyter notebook or install Jupyter Notebook (even try it in your browser).
What will you get out of this webinar?
1. A Participation Certificate
2. In-class project using Python
3. Take home Assignment
4. Bonus Projects
What will I learn in this class?
1. Introduction to Data Science
2. Introduction to Machine Learning
3. Project using Python
By the end of this workshop, you will have new Data Science & Machine Learning project under your portfolio.
Stay Tuned for upcoming workshop details !