Getting started with Machine Learning and Different career paths

I was given a presentation on the "Getting started with Machine Learning, Project end to end steps and different career paths" at Developer student club (DSC VVP) webinar.

Transcript

1. How to get started with machine learning? - uday kiran
2. Step – 1 Having right mindset
3. Step – 2 •Choose the right path •Top-Down •Bottom-UP
4. Step – 3 Choose the right tool
5. Step – 4 Practice and practice
6. Step – 5 Build a portfolio
7. Top - Down approach Learn the high level process of applied machine learning Learn how to use a tool enough to be able to work through problems. Practice on datasets, a lot. Transition intothe details and theory of machine learning algorithms.
8. Top - Down approach Learn all the prerequisites like programming, math Learn Machine Learning in depth Learn how to apply those concepts on the datasets and practice a lot. Build high level systems for end to end process.
9. Math prerequisites Calculs (don’t panic! only differential calculus/chain-rule) Linear algebra (Basics) Probability Statistics (Inferential and descriptive)
10. Why math?
11. Project steps
12. Project steps
13. Project steps
14. Project steps
15. Practice, practice and practice
16. Careers in Machine learning - uday kiran
17. Machine Learning Engineer Machine Learning Engineers are primarily involved with the design and development of ML systems and applications by using ML algorithms and tools. They also conduct and run various ML experiments Mathematics, Statistics, Programming, software architecture, system design, data structures, data modeling and ML algorithms
18. Data Scientist Machine Learning Engineers are primarily involved in gathering data from different touchpoints, analyzing and interpreting it, drawing insights and inferences. These are then used to make business decisions by the company executives. Mathematics, Statistics, Programming, data mining, data modeling, ML algorithms, Big Data platforms and SQL
19. Data Analyst Transform and manipulate large data sets to suit the desired analysis, this role can also include tracking web analytics and analyzing A/B testing. Data analysts also aid in the decision-making process by preparing reports for organizational leaders which effectively communicate trends and insights gleaned from their analysis. Python, R, Tableau, Excel, analytical thinking, data interpretation, Excellent communication skills, A good listener
20. Data Engineers Preprocesses, cleans and organizes big data. Turns data into powerful insights Builds and maintains ETL pipelines. Makes sure big data applications work properly. Database systems, ETL tools, Data APIs, Python, Java, Scala, distributed systems, Data warehousing solutions
21. BI analyst Analyzing business KPIs and performance. Improving company's competitive positioning. Identified issues and best practices. Creates exceptional graphs and dashboards. Python, R, SQL, Excel, Power BI, Tableau, Analytical skills, presentation skills, Communication skills, Team player.
22. Other career roles • Data architect • Database Administrator • Statistician • Data and Analytics Manager • Data Warehouse Architect • Research Scientist • Etc.....
23. THANK YOU -Ask your questions