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Artificial Intelligence
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Maven

AI-ML Projects for Data Professionals

  • up to 5 weeks
  • Advanced

Gain hands-on experience and build a portfolio of industry AI/ML projects. This advanced course is designed to equip mid-senior career professionals to drive impact while building a portfolio of applied ML projects.

  • Machine Learning
  • Data Science Workflow
  • Feature Engineering
  • Model Deployment
  • GitHub

Overview

This course offers a dynamic blend of technical expertise and real-world business challenges. Through a series of interactive sessions, discussions, and hands-on projects, you will learn how to scope machine learning projects effectively, lead discussions with stakeholders, navigate the entire data science workflow from data exploration to model deployment, and communicate project insights and business impact to stakeholders.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Professional Certification
    upon course completion
  • Part-time
    course format
  • Live classes
    delivered online

Who is this course for?

Data Scientists

Data scientists who want to build a compelling portfolio of industry projects to showcase their skills to potential employers.

Software and Data Engineers

Software and data engineers eager to gain expertise in applications of machine learning methodologies to enhance their technical repertoire.

Data and BI Analysts

Data and BI analysts seeking to acquire hands-on experience in leveraging data-driven insights to solve industry challenges.

This course offers a dynamic blend of technical expertise and real-world business challenges. Gain hands-on experience in applying machine learning methodologies to real-world industry scenarios. Develop a structured approach to navigating complexities of scoping ML projects, understanding business problems, gathering requirements, implementing projects, and deploying them on Cloud.

Pre-Requisites

1 / 3

  • Familiarity with R / Python programming language

  • Knowledge of data manipulation using Pandas

  • Understanding of machine learning fundamentals is good-to-have

What will you learn?

Week 1: Learning the Basics
In this week, we will gain a comprehensive understanding of fundamentals of data science and set up the basics for project deployment in the consecutive weeks. Goals include revisiting fundamentals of machine learning, learning Git for version control and collaborative development, understanding Data Science Workflow on Github, and setting up Github repository where we will host our projects.
Week 2: [Case Study 1] Uber ETA Prediction
In this week, we will deep-dive into ML project development and deployment. Goals include putting ML project scoping into action, containerizing ML applications using Docker, intro to deployment using Streamlit, navigating the entire workflow from data exploration to model deployment, and learning coding best practices.
Week 3: [Case Study 2] Demand Forecasting
In this week, we will build an ML model to predict energy demand. Goals include scoping out the demand forecasting project, exploring various forecasting methodologies like Holt Winter and Prophet, building out the demand forecasting code, and deploying it using Streamlit.
Week 4: [Case Study 3] Transformer Based Speech Transcription
In this week, we will get a step further into scoping and building AI projects. Goals include exploring various transformer based open-source models, building an AI project using an open-source API and pre-trained models, containerizing and deploying the project through Streamlit, and structuring the code for production through modularization, logging, and maintainability.
Week 5: Build Your Portfolio
In this week, we will bring all our learnings together to build a portfolio. Goals include wrapping up on all the projects from past weeks, building your website and GitHub portfolio, and showcasing your work through LinkedIn posts, blogs, and newsletters.

What learners say about this course

  • “I have learnt more from Manisha through her courses than I have learnt in my 4-year college degree. I wish I had found her earlier.”

    Abhigna Pebbati

    Analytics & Data Science Manager, Meta

  • “I attended PrepVector's Product Data Science course and it was immensely helpful in developing a thought process for approaching open ended problems. Manisha was very supportive and advised me throughout my upskilling journey. I highly recommend her course.”

    Ketki Sharma

    Data Scientist, Dropbox

  • “I learnt from Manisha about how to think about problems in a structured manner. This helped me not just in my interviews as a candidate but also as an interviewer. The thought process developed in her course now helps me evaluate candidates better.”

    Vedhanarayan Ravi

    Data Scientist, Adobe

Meet your instructor

  • Manisha Arora

    Data Science Lead, Google

    I am a seasoned Data Science professional with 10+ years of experience leading data science teams and driving business growth through data-driven decision making. I am passionate about democratizing data science and enabling others level up in their careers. I found PrepVector to enable aspiring professionals to excel in their data science careers. I have taught 350+ data professionals through my courses at Maven & PrepVector.

Upcoming cohorts

  • Dates

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$1,250