1 / 13

5 Steps To ML Made Easy

The basic phases of machine learning workflows include data collection, data pre-processing, building datasets, model training and refinement, evaluation, and deployment to production.

milesed
Download Presentation

5 Steps To ML Made Easy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 5 Steps to ML Made Easy

  2. Machine Learning is never complete with one coding language. It works best with other advantages combined from different coding language

  3. The basic phases of machine learning workflows includes data collection, data pre-processing, building datasets, model training and refinement, evaluation, anddeployment to production

  4. Therefore to operate an ML workflow, involves lot of data-science concepts to base the entire flow and this asks to strengthen basic mathematics to start with

  5. Step - 1 Topics to brush up are- • Basic Algebra • Probability • Statistics • Linear Algebra • Linear Regression Never let mathematicsoverwhelm you, cause experts say you will only require 10% of it to apply on ML workflow

  6. To get started with Machine Learning you have to be thorough with algorithms, how they are categorized and applied to get varied end results

  7. Step - 2 These are the three basic models based on which Machine Learning predictions are made from the given dataset

  8. Step - 3 There are around 10 languages which are considered best fit for ML use and for beginners, Python is preferred

  9. Data analysis and processing which involves collection, cleaning or wrangling, storage, analysis, and also visualization is a critical process for ML, here require trained data engineers to perform the tasks

  10. Step - 4 These are the toolsused for the data wrangling anḍ analytics process.

  11. Many new learners find it difficult to transition to an ML job role and the underlying reason is due to lack of confidence and practice with real time data set.

  12. Step - 5 Let’s get ready for the final run!These are the three ways you can start practicing... • Targeted Practice • Working Out the Entire ML Workflow • Practice on Real Datasets

  13. It's time to #BeAIReady! The only PG Certification program in Applied AI, that develops skills in building applications with Deep Learning, Computer Vision, NLP, Image & Speech Processing Offered by-IIT Mandi and Wiley Official Channel PartnerMiles Education

More Related