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Cumali
- Rate R550
- Response 1h
-
Students1
Number of students Cumali has accompanied since joining Superprof
Number of students Cumali has accompanied since joining Superprof
R550/h
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- Python
- Programming Languages
- Java
- Artificial Intelligence
PhD Candidate Expert Tutor in Python, R, Jupyter notebook, Machine Learning, Nature Inspired Optimization. In general terms: Data Science, Data Analysis and Data Visualization.
- Python
- Programming Languages
- Java
- Artificial Intelligence
Lesson location
About Cumali
I am a Computer Scientist, a PhD student and a research assistant which work on Artificial Intelligence. I would like to share my knowledge with passionate people who want to learn programming, Artificial Intelligence and Data Science. Likewise, I hope to make an impact on young people toward AI.
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About the lesson
- Beginner
- Intermediate
- Kids
- +11
levels :
Beginner
Intermediate
Kids
Secondary
Matric/GCSE
AS Level
A Level
BTech
Adult education
Undergraduate
Diploma
Primary
Masters
Advanced
- English
- Turkish
Languages in which the lesson is available :
English
Turkish
Python & R
I'll walk you through the fundamentals of the Python programming world, including lambdas, reading and editing csv files, and the numpy library. I'll show you how to manipulate and clean data with the famous Python pandas data science library, as well as how to abstract the Series and DataFrame as the core data structures. I'll show you how to use the popular Python pandas data science library to perform data manipulation and cleaning, as well as the abstraction of the Series and DataFrame as the core data structures for data analysis, as well as tutorials on how to use functions like groupby, merge, and pivot tables effectively. You will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. (The same steps are valid for R )
Machine Learning
You'll learn about data dimensionality, the process of clustering data and analyzing such clusters, supervised approaches to building predictive models, and how to use the scikit learn predictive modelling techniques while recognizing data generalizability process issues (e.g. cross validation, overfitting). You'll also learn sophisticated techniques like ensemble building and the functional limitations of predictive models. Furthermore, you will be able to distinguish between supervised (classification) and unsupervised (clustering) techniques, determine which technique to use for a specific dataset and requirement, engineer features to satisfy the need, and write Python code to carry out the tasks.
Advance topics: Background of Machine Learning, Reinforcement Learning and Deep Learning techniques.
Text Mining
The fundamentals of text mining and text manipulation will be covered. You'll learn how Python handles text, the structure of text for both machines and humans, and an overview of the nltk text manipulation system. You'll also learn text manipulation techniques such as regular expressions (text searching), text washing, and text preparation for machine learning algorithms. Likewise, you'll be able to apply simple natural language processing techniques to text and see how text classification is done. You'll also be able to create advanced methods for identifying and grouping topics in documents based on their similarity (topic modelling).
Plotting & Charting & Visualization
I'll walk you through the fundamentals of data visualization, with an emphasis on reporting and charting with the matplotlib library. I'll start from a design and information literacy standpoint, discussing what makes a good and poor visualization, as well as how statistical metrics translate into visualizations. Likewise, I'll concentrate on the technologies used in python to create visualizations, such as matplotlib. I'll concentrate on matplotlib, the technology used to create visualizations in Python, and show users how to create simple charts and how to implement design decisions in the system. A tutorial of matplotlib functionality will be provided, as well as demonstrations of a variety of simple statistical charts to assist learners in determining when a specific approach is appropriate for a given situation.
Nature Inspired Optimization
Genetic Algorithms, Evolutionary Algorithms, Multi-Objective optimization
I can help you with those programming language, tools and libraries
- Python
- R
- Jupyter Notebook
- Anaconda
- packages management
- Numpy
- Matplotlib
- SciPy
- Scikit-learn
- Data Analysis
- Data Science
- Data Visualization
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Rates
Rate
- R550
Package rates
- 5h: R2419
- 10h: R4398
online
- R550/h
Details
Only online lessons
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