"One percent of genius is inspiration,
ninety-nine percent is sweat."
Execution of projects developed with Java and PHP programming languages, solving existing problems in projects, carrying out maintenance, security operations and debugging of projects, working with API and REST services, process management and monitoring (Zabbix, Confluence, Jira).
In my undergraduate studies, I primarily learned the fundamentals of programming and algorithm design in the C programming language, algorithms, data structures, Object Oriented Programming and Visual Programming (GUI) in Java, Database development concepts and programming in the SQL programming (query) language, computer and computer hardware basics, and fundamental computer science concepts like basic mathematics, statistics, software engineering fundamentals, and computer networks. Outside of school, UML, Python, PHP, etc. I made an effort to develop myself in a variety of ways.
I got the opportunity to take part in trainings, workshops, and networking programs on data science and artificial intelligence that Boaziçi University had planned. Through these opportunities, I was able to discover how these fields are employed in business and in practice, as well as from whom and how.
Taking Part Workshops:
Completed Courses and Trainings
- Java Basics
- Java Required
- Game: Catch the Canny Java
- Java OOP
- RecyclerView: Landmarks Java App
- Database: Art Book Java App
- Maps: Travel Book Java App
- Instagram Clone Java App: Firebase
- Developing Game: Survivor Bird
- Retrofit: CryptoCoin Java App
- Work Manager
- Fruit Ninja Clone Java App
- Design Basics
- Design Practices and Logo Making
- Kotlin Basics
- Kotlin Required
- Game: Catch the Canny Kotlin App
- Kotlin OOP
- RecyclerView: Landmarks Kotlin App
- Database: Art Book Kotlin App
- Maps: Travel Book Kotlin App
- Instagram Clone Kotlin App: Firebase
- Kotlin Fragment & Navigation
- Retrofit: CrpytoCoin Kotlin App
- Kotlin Coroutines
- Kotlin Work Manager
- Jetpack Compose Basics
- Jetpack Compose State Management
- Parse (Legacy)
It is great that the topics are reinforced with applications and most importantly that the course is constantly up to date. The course is instructive in every sense. The learning process becomes more efficient not only with the applications in the course, but also with the efforts and research to be done.
- Docker Engine
- Image ve Container
- Docker CLI
- Docker Volume
- Bind Mounts
- Docker Plugin
- Docker Network
- Docker Logging
- Docker CPU ve Memory Limitleri
- Docker Environment Variables
- Docker Registry
- Multi-stage Build
- Build ARG
- Docker Commit, Save-Load
- Docker Registry
- Docker Compose
- Container Orchestration
- Docker Swarm
- Docker Service
- Overlay Network
- Docker Secret
- Docker Stack
The instructor is well-versed in the subject and is well prepared as a presentation to the lectures. The education was enriched by the scenarios of real examples with hearty visuals and a fluent narration.
In education; Comparisons were made between conventional systems and containers. In this way, we have provided a quicker understanding of the concepts in the Docker world by overlapping them with the concepts you know. Even if you are entering the IT sector for the first time, you will get the theoretical knowledge of conventional systems in this training.
- Sentiment Analysis with Logistic Regression
- Sentiment Analysis with Naive Bayes
- Vector Space Models
- Machine Translation and Document Search
A really great course in NLP. They do a really good job balancing beginner and intermediate skill levels. This is a good introduction to NLP and machine learning in general. Really fun course!
- Recurrent Neural Networks
- Natural Language Processing & Word Embeddings
- Sequence models & Attention mechanism
Great hands on instruction on how RNNs work and how they are used to solve real problems. It was particularly useful to use Conv1D, Bidirectional and Attention layers into RNNs and see how they work.
- Exploring a Larger Dataset
- Augmentation: A technique to avoid overfitting
- Transfer Learning
- Multiclass Classifications
Very brief and precisely taught implementing various techniques in Convolution Neural Networks by using Tensorflow. Quite time saving and a good one to boost your skills.
- A New Programming Paradigm
- Introduction to Computer Vision
- Enhancing Vision with Convolutional Neural Networks
- Using Real-world Images
Very good and concise introduction to Tensorflow. Important parameters to methods are explained so they are understood and no longer mysterious. The course starts with a very simple “Hello World” type model and builds to a more complex CNN. During this evolution, the code becomes necessarily more complex but the additional methods are explained as they are introduced.
- Foundations of Convolutional Neural Networks
- Deep convolutional models: case studies
- Object detection
- Special applications: Face recognition & Neural style transfer
Great course for kickoff into the world of CNN’s. Gives a nice overview of existing architectures and certain applications of CNN’s as well as giving some solid background in how they work internally.
- Machine Learning Strategy
This is a must course in the entire specialization. It covers the step by step procedure to approach and solve a problem. The case studies provided are real world problems which are so much helpful.
- Practical aspects of Deep Learning
- Optimization Algorithms
- Hyperparameter tuning, Batch Normalization and Programming Frameworks
Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course.
- Introduction to Deep Learning
- Neural Network Basics
- Shallow Neural Networks
- Deep Neural Networks
Andrew Ng’s presenting style is excellent. Makes the course easy to follow as it gradually moves from the basics to more advanced topics, building gradually. Very good starter course on deep learning.
- Introduction to Machine Learning
- Recommender Systems
- Final Project
- Fundamentals of Data Manipulation with Python
- Basic Data Processing with Pandas
- More Data Processing with Pandas
- Answering Questions with Messy Data
If you are looking for in-depth theory, you may be looking at the wrong place. The videos skim through some fundamentals, and sometimes give you some valuable hints.
But if you are looking for a challenging experience that emulates the real world, this course is definitely for you. The assignments will throw you to the wolves very early. You will have to research way beyond the videos to finish them in a elegant manner. It also encourages you to code in a “Pandorable” way, which is a valuable skill.
- Introduction to specialization
- What it means to be AI first
- How Google does Machine Learning
- Inclusive ML
- Python notebooks in the cloud
It is very informative about the machine learning and AI usage in Google products and provide deep dive into GCP platform in very intuitive way. I recommend to AI aficionado.
- Linear Regression with One Variable
- Linear Regression with Multiple Variables
- Octave/Matlab Tutorial
- Logistic Regression
- Neural Networks: Representation
- Multi-class Classification and Neural Networks
- Neural Networks: Learning
- Advice for Applying Machine Learning
- Regularized Linear Regression and
- Machine Learning System Design
- Support Vector Machines
- Unsupervised Learning
- Principal Component Analysis
- K-Means Clustering and PCA
- Anomaly Detection
- Recommender Systems
- Anomaly Detection and Recommender
- Large Scale Machine Learning
- Application: Photo OCR
My most beautiful course on Machine learning. To all those thinking of getting in ML, Start you learning with the must-have course.
If you want to take your understanding of machine learning concepts beyond “model.fit(X, Y), model.predict(X)” then this is the course for you.
Python – Machine Learning
- Data Analysis Project Management and Problem Types
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree
- Random Forest
- Logistic Regression
- K-Nearest Neighbors – KNN
- SVM Classification
- Naive Bayes
- Decision Tree Classification
- Random Forest
- K-Means Algorithm
- Hierarchical Segmentation
- Association Rule Mining
- APriori Algorithm
- Eclat Algorithm
- Reinforced Learning
- Upper Confidence Interval – UCB
- Thompson Sampling
- Natural Language Processing Basics
- Deep Learning Basics
- Dimension Reduction
- Model Selection
Introductory machine learning course for practice using the Scikit-Learn library on its topics.
Python Data Science Fundamentals
Python Data Science Course on cleaning the data to be used, data processing methods with Pandas and Numpy on data (EDA, Visual EDA, Tidy and Pivoting Data etc ..)
Step-by-step Python basics course on basic Python data structures (tuple, dictionary, strings, list etc.), Python Object-oriented programming concepts, Python errors and exceptions, Numpy and Pandas basics, visualization basics with Matplotlib
CSS – Bootstrap 4
Basic CSS, Grid system, Components etc. like basic CSS and Bootstrap4 course.