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Since March 2024
Instructor since March 2024
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Polytechnician engineering student gives lessons in Mathematics, Physics, Chemistry or English at all levels, (preferably high school level)
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From 35.73 £ /h
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Did you know that a son of Polytechniciens will have on average more than 900% more chance of integrating Polytechnique himself. This statistic is due to unequal access to personalized education.
During a course, I propose to restore equality of opportunity, by transmitting my working method, which allowed me to shine in prep and to join the École Polytechnique. Each course is tailor-made, ensuring deep understanding and practical application.
Extra information
Available in Vernon (Normandy) at my home or at the student's home, and in Paris surroundings Saint-Lazare station at the student's home or at my home in Paris rue Saint Denis
Location
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At student's location :
  • Around Paris, France
About Me
I am an engineer at the École Polytechnique in a double degree with ENSTA having joined my school on the first try, following the post-prep Math sup competition. I specialized in space engineering and have already completed several internships in space companies where I specialized in rocket propulsion. I am currently doing an internship at Arianegroup in Vernon, with the possibility of traveling to Paris Gare Saint-Lazare. I can also give lessons in an apartment in Paris 2nd.
Finally, I am available at the end of March and beginning of April in the Versailles area.
Education
Preparatory school for Grandes Ecoles Physics Chemistry Engineering Sciences (PCSI) then PSI* at Lycée Chaptal in Paris
École Polytechnique / ENSTA engineering school, specializing in space engineering
Experience / Qualifications
Experience as a private Mathematics teacher at home during my high school years to another high school student.
Also a volunteer mathematics support teacher for a class of middle school students.
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
French
English
Availability of a typical week
(GMT -05:00)
New York
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At student's home
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
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Dave
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I am an experienced machine learning teacher with 5 years of expertise in teaching this discipline at all levels. My expertise using Python and R allows me to teach different machine learning algorithms such as neural networks, decision trees and clustering algorithms. I am also experienced in using popular Python and R libraries such as TensorFlow, Keras, Scikit-learn and ggplot2.
In addition to my machine learning skills, I am able to help students read and understand research papers for their presentations, as well as work on projects in Python and R. My commitment to machine learning is passionate and I enjoy sharing my knowledge with my students.
If you are interested in my services as a machine learning teacher for all levels, do not hesitate to contact me.
In addition to my machine learning skills, I am also able to help you with mathematics, statistics and dissertation writing.
I am available to teach the following subjects:
1.Python or R
2. Data exploration
3.Machine learning
3.1. Intro ML
3.2. Linear Model
-> Linear Models for Regression and Classification
3.3. kernel
-> Kernelization
3.4. Model selection
3.5. model set,
-> Bagging / RandomForest, Boosting (XGBoost, LightGBM,...) , Stacking
3.6. Data preprocessing
-> Data pre-processing
-> Pipelines: choose the right preprocessing steps and models in your pipeline
-> Cross validation
3.7. Neural Networks
-> Neural architectures
-> Training neural nets: Forward pass: Tensor operations and Backward pass: Backpropagation
-> Neural network design: Activation functions, weight initialization and Optimizers
-> Neural networks in practice: Model selection, Early stopping, Memorization capacity and information bottleneck, L1/L2 regularization, Dropout, Batch normalization
3.8. Convolutional Neural Networks
-> Convolved Image
-> Convolutional neural networks
->Data increase
-> Model interpretation
-> Using pre-trained networks (transfer learning)
3.9. Neural Networks for text
-> Bag of word representations, Word embeddings, Word2Vec, FastText, GloVe
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Contact Marc
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arrow icon previousarrow icon next
verified badge
Dave
Hello,
I am an experienced machine learning teacher with 5 years of expertise in teaching this discipline at all levels. My expertise using Python and R allows me to teach different machine learning algorithms such as neural networks, decision trees and clustering algorithms. I am also experienced in using popular Python and R libraries such as TensorFlow, Keras, Scikit-learn and ggplot2.
In addition to my machine learning skills, I am able to help students read and understand research papers for their presentations, as well as work on projects in Python and R. My commitment to machine learning is passionate and I enjoy sharing my knowledge with my students.
If you are interested in my services as a machine learning teacher for all levels, do not hesitate to contact me.
In addition to my machine learning skills, I am also able to help you with mathematics, statistics and dissertation writing.
I am available to teach the following subjects:
1.Python or R
2. Data exploration
3.Machine learning
3.1. Intro ML
3.2. Linear Model
-> Linear Models for Regression and Classification
3.3. kernel
-> Kernelization
3.4. Model selection
3.5. model set,
-> Bagging / RandomForest, Boosting (XGBoost, LightGBM,...) , Stacking
3.6. Data preprocessing
-> Data pre-processing
-> Pipelines: choose the right preprocessing steps and models in your pipeline
-> Cross validation
3.7. Neural Networks
-> Neural architectures
-> Training neural nets: Forward pass: Tensor operations and Backward pass: Backpropagation
-> Neural network design: Activation functions, weight initialization and Optimizers
-> Neural networks in practice: Model selection, Early stopping, Memorization capacity and information bottleneck, L1/L2 regularization, Dropout, Batch normalization
3.8. Convolutional Neural Networks
-> Convolved Image
-> Convolutional neural networks
->Data increase
-> Model interpretation
-> Using pre-trained networks (transfer learning)
3.9. Neural Networks for text
-> Bag of word representations, Word embeddings, Word2Vec, FastText, GloVe
Good-fit Instructor Guarantee
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Contact Marc