ML-EN Machine Learning for Web App Calculators using AI

7 Days

·

Cohort-based Course

Machine Learning models are useless if left in the notebook. Why not leverage them to calculate predictions in a web app?

Students from

Banco Bilbao Vizcaya Argentaria
Endesa
Vodafone
Santander Argentina
Pepsi

Course overview

Practice, practice, and practice!

During the course, you'll work on practical cases to develop different Machine Learning models on datasets of your interests.


After the course, you'll be more than competent in programming any machine learning model on any dataset.


All the practical knowledge will be installed in your brain so that you can focus on creating business value with data.

Course for practical people who want to apply knowledge to real-world problems.

01

You're curious about Machine Learning but haven't coded any model in Python.


Or you have, but you waste a lot of time choosing the best.

02

The Master/Bootcamp program you took has left you with a lot of unorganized theory.


You want to be more competent at writing ML models.

03

You work with technical people who are proficient at developing Machine Learning models.


Yet, you don't understand their jargon...

What you’ll get out of this course

Solutions of practical cases

All the solutions from the exercises will be provided during the course.

Practice with datasets of your interest

I've curated a list of datasets in sectors like Energy, Finance, and Supply Chain, where some of my clients work.


The course is strategically designed to apply the lessons to any new dataset of your interest.


Forget getting bored during theoretical courses!

Monitored learning

You don't know what you don't know.


Quite often, you assume you've understood a lesson.


Then, you are assigned to solve an exercise, and you don't know where to start because you didn't understand it well enough.


I'll monitor your practice to note the lessons you don't know.

Lesson, practice, doubts, repeat

I've taught in many different contexts, including individuals, small (2-5), and big (5-40) groups.


This process is the result of trying many different teaching methodologies.


During doubts,

Resolution of doubts and getting deeper

For each exercise you practice in the course, later, we'll have an assigned time for Q&A to explore deeper details.


Based on my notes from monitored learning, I'll solve the most important ones and clarify any important lessons you still need to understand in order to continue.

Self-development approach by searching on Google or ChatGPT

I am not interested in teaching you 100s of concepts that you'll need to revisit after the course.


Instead, I'll teach you (when required) how to effectively search for solutions in Google/ChatGPT and adapt them to your problems step by step to build deep knowledge.

This course includes

4 interactive live sessions

Lifetime access to course materials

28 in-depth lessons

Direct access to instructor

Projects to apply learnings

Guided feedback & reflection

Private community of peers

Course certificate upon completion

Maven Satisfaction Guarantee

This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.

Course syllabus

Expand all modules
  • Week 1

    May 21—May 26

    Week dates are set to instructor's time zone

    Events

    • May

      21

      Session 1

      Tue, May 21, 2:00 PM - 5:00 PM UTC

    • May

      22

      Session 2

      Wed, May 22, 2:00 PM - 5:00 PM UTC

    • May

      23

      Session 3

      Thu, May 23, 2:00 PM - 5:00 PM UTC

    Modules

    • Modelling Process with Scikit-Learn

    • Machine Learning Algorithms

    • Model Selection

    • Data Preprocessing

    • Deep Learning

    • LSTM for Time Series

    • Shap Values for Model Explainability

  • Week 2

    May 27

    Week dates are set to instructor's time zone

    Events

    • May

      27

      Optional: Q&A

      Mon, May 27, 2:00 PM - 3:00 PM UTC

  • Bonus

    Modules

    • Web Calculators

Jesús López

Jesús López

Instructor @LinkedIn Learning // Statistics, Data & Programming Consultant @datons

Jesus' curriculum showcases a diverse range of experiences and expertise.


His more than 8,000 training hours come from various industries, such as energy, finance, telecommunications, and healthcare. Specifically, some of his notable clients work in Santander, PEPSI, Vodafone, University of Oxford, Hospital Ramón y Cajal, Bankinter, BBVA, Banco de España, IGNIS, Cogen, Telefonica, Galp, and REE.


He has trained on data and programming skills more 800 clients 1:1. For whom he developed the analysis of their data projects (more than 200):


  • Machine Learning models to predict Bladder Cancer and Alzheimer's Disease
  • Simulation of investment strategies in the energy industry
  • Trading Algorithms to invest in the Stock Market
  • Causal statistics to analyze Psychological Factors
  • Dashboards for Final Thesis Projects using Shiny, Dash, Streamlit, Tableau, and Power BI


Based on his vast and intense experience, he has created an educational program that solves most of the problems students face in learning programming for data.


His students consider their methodology one of the best and assure that you will always leave his sessions with practical skills acquired and ready to be applied to your problems.

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