Enroll Course: https://www.udemy.com/course/makine-ogrenmesi/
Are you fascinated by the world of machine learning, or perhaps you work extensively with data and are curious about how machines learn and operate? If so, I have a fantastic course to recommend on Udemy: ‘Python ile Makine Öğrenmesi’ (Machine Learning with Python), expertly crafted by Doç. Dr. Şadi Evren ŞEKER.
Dr. ŞEKER is a highly qualified instructor, holding degrees in Computer Engineering and specializing in Artificial Intelligence during his postgraduate studies. His post-doctoral research in data science has taken him across the globe, and he has an impressive portfolio of academic papers, books, patents, and algorithms in machine learning, big data, data science, and AI. With nearly two decades of experience providing training, consultancy, and software services to leading companies in banking, telecommunications, insurance, transportation, construction, tourism, and finance, his practical insights are invaluable.
This course aims to take absolute beginners and transform them into proficient machine learning practitioners. It offers a step-by-step journey into the field, equipping learners with diverse skills and real-world applications in machine learning and data science. The curriculum even touches upon trending topics like deep learning and reinforcement learning, demonstrating their usage and applications through practical examples.
The course is structured to be both engaging and exciting, covering a comprehensive range of topics:
* **Part 1: Data Preprocessing**
* **Part 2: Prediction and Regression** (Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression)
* **Part 3: Classification** (Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification)
* **Part 4: Clustering** (K-Means, Hierarchical Clustering)
* **Part 5: Association Rule Learning** (Apriori, Eclat)
* **Part 6: Reinforcement Learning** (Upper Confidence Bound, Thompson Sampling)
* **Part 7: Natural Language Processing** (Bag-of-words model and algorithms for NLP)
* **Part 8: Deep Learning** (Artificial Neural Networks, Convolutional Neural Networks)
* **Part 9: Transformation and Dimensionality Reduction** (PCA, LDA, Kernel PCA)
* **Part 10: Model Selection & Boosting** (k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost)
What truly sets this course apart is its practical approach. You’ll work with real-life examples and learn how to build your own machine learning models. Crucially, the course provides Python code templates that you can readily adapt and use for your own projects. This hands-on element ensures you’re not just learning theory but also gaining practical coding skills.
If you’re looking to dive into machine learning with a solid foundation and practical application, ‘Python ile Makine Öğrenmesi’ by Doç. Dr. Şadi Evren ŞEKER is an excellent choice. It’s an investment in your data science journey that promises significant returns.
Enroll Course: https://www.udemy.com/course/makine-ogrenmesi/