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.
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.
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.
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
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
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
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
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.