from48.00EUR/ h
Python and R course by a Machine Learning and Actuarial Researcher
Translated by Google. Click to view original.
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!
Location
Online via webcam
At teacher's location: Place de Clichy, Paris, France
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Use ⊞+wheel to zoom!
General info
Skills:
Age: | Children (7-12 years old) Teenagers (13-17 years old) Adults (18-64 years old) Seniors (65+ years old) |
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Student level: | Beginner Intermediate Advanced |
Duration: | 60 minutes |
The class is taught in: | French, English |
About Me
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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.
Education
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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.
Experience / Qualifications
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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.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
Ask a question
Send a message explaining your needs and Dave will reply soon:
The more detail, the better.
Ex. "Hi, when are you available to meet for a lesson?"
Ex. "Hi, when are you available to meet for a lesson?"
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from 48€At teacher's location
from 48€Online via webcam
100% Satisfaction Guarantee
Good-fit Instructor Guarantee
If you are not satisfied after your first lesson, Apprentus will find you another instructor or will refund your first lesson.
Online reputation
- Instructor since April 2023
- Phone number verified
- Google connected
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from 48€At teacher's location
from 48€Online via webcam
Good-fit Instructor Guarantee
If you are not satisfied after your first lesson, Apprentus will find you another instructor or will refund your first lesson.
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Duration :
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Educational objectives
At the end of the course, participants will be able to:
1. Understand the basics of programming, including the concepts of variables, data types, conditional structures, and loops.
2. Write and run simple Python scripts.
3. Manipulate inputs and outputs to interact with the user.
4. Solve practical problems with short, functional programs.
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Course syllabus
1. Introduction (10-15 min)
- Presentation of Python: Why this language?
- Installation and configuration of a programming environment (IDLE, Visual Studio Code, or Jupyter Notebook).
- First program: *"Hello, World!"*.
2. Python Basics (20-30 min)
- Variables and data types (integers, strings, lists).
- Mathematical operations and data manipulation.
- Conditional structures (if/else) and loops (for/while).
3. Practical exercises (20-35 min)
- Write a program to calculate the sum of two numbers provided by the user.
- Create a simple application, like a currency converter or password generator.
- Bonus for 90 min format: Fixed a more complex issue involving lists or loops.
4. Q&A and conclusion (5-10 min)
- Summary of the concepts covered.
- Suggestions for resources to continue learning.
- Feedback on the course.
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Teaching methodology
- Interactive and engaging: the course combines theoretical explanations with practical exercises.
- Personalized approach: Sessions are tailored to the needs of participants. Students are encouraged to ask questions at any time.
- Learning by doing: examples and exercises are designed to help you assimilate concepts quickly.
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Target audience
This course is ideal for:
- Complete beginners in programming.
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Benefits of this online course
- Teaching by an experienced and passionate trainer.
- Interactive sessions adapted to your learning pace.
- Access to educational materials and exercises to continue practicing after the course.
Join us now to discover programming and its applications using Python!
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