Practical Python for Machine Learning
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?
Practical Python for Machine Learning
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
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.
Practical Python for Machine Learning
Course syllabus
Week 1
May
21
Session 1
May
22
Session 2
May
23
Session 3
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
Optional: Q&A
Bonus
Web Calculators
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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