
Beschrijving
The two main types of machine learning are supervised learning and unsupervised learning. In supervised learning, the model is trained on labeled data, which means that each data point is associated with a target or outcome variable. The goal is to learn a mapping function that can accurately predict the target variable for new data points. In unsupervised learning, on the other hand, the model is trained on unlabeled data, which means that there is no target variable. Instead, the goal is to discover patterns or structure in the data. Other types of machine learning include reinforcement learning, which involves learning through trial and error, and deep learning, which uses neural networks to learn complex representations of data.
Program
Title: "Introduction to Machine Learning with Python". Duration: 2 weeks (14 days). Location: Paris, . . Week 1:. - Day 1: Introduction to machine learning and Python. - Overview of machine learning concepts and applications. - Setting up a Python development environment. - Day 2: Preprocessing and cleaning data. - Importing and exporting data in Python. - Handling missing and incorrect data. - Feature scaling and normalization. - Day 3: Exploratory data analysis. - Visualizing and summarizing data with Python. - Identifying trends and patterns in data. - Day 4: Supervised learning. - Regression algorithms. - Classification algorithms. - Day 5: Evaluating model performance. - Measuring model accuracy and error. - Choosing an appropriate evaluation metric. . Week 2:. - Day 6: Unsupervised learning. - Clustering algorithms. - Dimensionality reduction techniques. - Day 7: Deep learning. - Introduction to neural networks. - Training deep learning models with TensorFlow. - Day 8: Transfer learning. - Reusing pre-trained models for new tasks. - Fine-tuning pre-trained models. - Day 9: Feature engineering. - Extracting and creating new features from raw data. - Selecting relevant features for model training. - Day 10: Hyperparameter tuning. - Optimizing model performance through hyperparameter tuning. - Day 11: Project work. - Working on a machine learning project in small groups. - Applying the concepts learned in the course to a real-world problem. - Day 12: Project work. - Day 13: Project work. - Day 14: Project presentation and course review. . This training program would be suitable for individuals with some programming experience, but no previous knowledge of machine learning is required. The program would cover the basics of machine learning, including preprocessing and exploring data, supervised and unsupervised learning, and deep learning, as well as practical techniques for improving model performance such as feature engineering and hyperparameter tuning. Participants would also have the opportunity to work on a machine learning project during the course, and present their results at the end.
Prerequisites
No requirements
Who is this course for
For all
Duration
The duration of the course is flexible. Contact our educational managers for more information.
Course Format
Online,Hybrid,Blended
Evaluation procedures/Certification
This training is evaluated through a test scenario and multiple choice questions. To validate the course, it is necessary to validate all the skills. For information, there is no equivalence or bridge to validate this course. To download and take our quizzes for validation of acquired knowledge, please connect to the online storage platform with your login details.
Upcoming dates
December 2025
January 2026
February 2026
How to access the course
To pre-register for this course and receive all the necessary information, please click on the registration button above. A quizz is proposed to allow us to direct you to the right course and instructor.
When to access the course
After registration, you will receive all the details to start the course.
Administrative & Educational Contact
Nduka NZEKA - formations(at)ilaria-academy.com
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"I was new to machine learning and this course really helped me understand the basicsThe instructors were knowledgeable and patient, and I feel much more confident in my ability to implement machine learning techniques in my work."
"This course was an excellent introduction to machine learningThe material was presented in a clear and concise manner, and the hands-on exercises helped me solidify my understanding of the conceptsI would highly recommend this course to anyone interested in getting started with machine learning."
"I have been working in the field of machine learning for a few years now, but I still learned a lot from this courseThe instructors did a great job of explaining complex concepts in an easy-to-understand way, and the real-world examples helped bring the material to lifeI would definitely take another course from this team."
"I was skeptical about taking an online machine learning course, but this one exceeded my expectationsThe interactive format allowed me to ask questions and get immediate feedback, and the instructors were always available to helpI feel much more confident in my ability to apply machine learning to my job thanks to this course."
"I really enjoyed the hands-on nature of this courseThe instructors provided us with a lot of data sets to work with, and I felt like I was able to get a lot of practical experienceI also appreciated the support of the teaching assistants, who were always available to help when I had questions."
"The course materials and lectures were top-notch, and the instructors did a great job of explaining complex concepts in an accessible wayI also appreciated the small class size, which allowed for more personalized attention from the instructorsI would highly recommend this course to anyone interested in machine learning."
"I have taken several machine learning courses in the past, but this one was by far the bestThe instructors were knowledgeable and approachable, and the course materials were well-organized and easy to followI would definitely take another course from this team in the future."
"This was my first time taking an online course, and I was pleasantly surprised by how effective it wasThe instructors were able to create a sense of community among the students, and the interactive format kept me engaged throughout the courseI feel much more confident in my ability to apply machine learning techniques thanks to this experience."
"I was a bit nervous about taking a machine learning course, but the instructors made it a very positive experienceThey were patient and helpful, and the material was presented in a way that was easy to understandI would definitely recommend this course to others."
"I have been working in the field of data science for a while now, but I still learned a lot from this courseThe instructors were knowledgeable and approachable, and the course materials were well-organized and easy to followI would definitely take another course from this team in the future."