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Since April 2023
Instructor since April 2023
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Python and R course by a Machine Learning and Actuarial Researcher
From 48 € /h
Hello !
If you're looking for a private Python tutor, you've come to the right place!
I am a Python expert with several years of experience teaching this programming language. I can help you learn the basics of Python, as well as more advanced topics such as objects and classes.
As a private teacher, I can adapt my lessons to your level and your individual needs to help you progress quickly.
I can also show you how to use popular libraries like:
NumPy: a library for scientific computing, which allows working with multidimensional arrays and performing high performance calculations.
Pandas: a library for data analysis, which offers flexible and easy-to-use data structures for manipulating tabular data.
Matplotlib: a library for data visualization, which allows you to create professional-quality graphs and figures.
Scikit-learn: a library for machine learning, which offers supervised and unsupervised learning algorithms for data analysis.
If you are interested, do not hesitate to contact me to discuss your needs and my availability. I look forward to working with you!
If you're looking for a private Python tutor, you've come to the right place!
I am a Python expert with several years of experience teaching this programming language. I can help you learn the basics of Python, as well as more advanced topics such as objects and classes.
As a private teacher, I can adapt my lessons to your level and your individual needs to help you progress quickly.
I can also show you how to use popular libraries like:
NumPy: a library for scientific computing, which allows working with multidimensional arrays and performing high performance calculations.
Pandas: a library for data analysis, which offers flexible and easy-to-use data structures for manipulating tabular data.
Matplotlib: a library for data visualization, which allows you to create professional-quality graphs and figures.
Scikit-learn: a library for machine learning, which offers supervised and unsupervised learning algorithms for data analysis.
If you are interested, do not hesitate to contact me to discuss your needs and my availability. I look forward to working with you!
Location
At teacher's location :
- Place de Clichy, Paris, France
Online from France
About Me
I am passionate about applying mathematics and computer science to solving real-world problems.
If you want to learn more about my professional background and skills. You will discover my professional experiences, achievements, and areas of interest there.
I am passionate about applying mathematics and computer science to solving real-world problems.
If you want to know more about my professional background and my skills. You will be able to discover my professional experiences, my achievements and my centers of interest.
If you want to learn more about my professional background and skills. You will discover my professional experiences, achievements, and areas of interest there.
I am passionate about applying mathematics and computer science to solving real-world problems.
If you want to know more about my professional background and my skills. You will be able to discover my professional experiences, my achievements and my centers of interest.
Education
I am currently pursuing a doctoral degree in applied mathematics at the University of Paris 1. I obtained my two master's degrees in statistics (at Paris 1) and machine learning (at INSA Rouen). My research work focuses on the application of machine learning techniques to statistical problems.
I am currently doing a doctorate in applied mathematics at the University of Paris 1. I obtained my two master's degrees in statistics (at paris 1) and in machine learning (at INSA Rouen). My research work focuses on the application of machine learning techniques to statistical problems.
I am currently doing a doctorate in applied mathematics at the University of Paris 1. I obtained my two master's degrees in statistics (at paris 1) and in machine learning (at INSA Rouen). My research work focuses on the application of machine learning techniques to statistical problems.
Experience / Qualifications
I am a machine learning teacher with 5 years of experience and I am able to teach at all levels. I specialize in using Python and R to teach different machine learning algorithms, including neural networks, decision trees, and clustering algorithms.
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
Skills
Availability of a typical week
(GMT -04:00)
New York
At teacher's location and via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
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
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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