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37 - ITC - Information Technology - Miscellaneous
ITC 132 - Machine Learning by Python
| Code | Start Date | Duration | Venue | |
|---|---|---|---|---|
| ITC 132 | 28 October 2024 | 5 Days | Istanbul | Registration Form Link |
| ITC 132 | 02 December 2024 | 5 Days | Istanbul | Registration Form Link |
Course Description
Learning about the purpose of Machine Learning and where it applies to the real world, participants will get a general overview of topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms, all of which are implemented using Python programming language.
They are expected to gain new skills to add to their resumes, such as regression, classification, clustering, sci-kit learn and SciPy. New projects that can be added to their portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. They will come out with comprehensive knowledge in machine learning to prove their competency, and share it anywhere they like online or offline,
Course Objectives
- Understand the difference between the two main types of machine learning methods: supervised and unsupervised
- Discussing supervised learning algorithms, including classification and regression
- Learning unsupervised learning algorithms, including Clustering and Dimensionality Reduction
- Knowing how statistical modeling relates to machine learning and how to compare them
- Examining real-life examples of the different ways machine learning affects society
- Having knowledge of basic philosophical and ethical issues related to the development and application of ML / AI /DL
Who Should Attend?
- Professionals with experience in a technical area such as computer science, statistics, physics, or electrical engineering
- Anyone whose work interfaces with data analysis who wants to learn key concepts, formulations, algorithms, and practical examples of what is possible in machine learning and artificial intelligence, deep learning,
- Managers who need the vision and understanding of the many opportunities, costs, and likely performance hurdles in predictive modeling, especially as they pertain to large amounts of textual (or similar) data
- Professionals looking for a deeper understanding and hands-on experience with ML/AI/DL
Course Details/Schedule
Day 1
- Introduction to Fundamental Knowledge in Data Science
- Principles of Data Science (mining, extracting features, modeling,..)
- Fundamental knowledge of Linear Algebra, Probability & STAT, Algorithms, and Modeling
- WHAT buzzwords, AI, ML, DL really are?
- Intro to basics in Python
Day 2
- Introduction to Advanced Skills in Python
- Generators & Comprehension Expressions in Python
- File and Directory Handling
- OOP in Python, Concept, and Implementation
- Intro to SQLite3 Python
- Essential intro to Numpy, Pandas, & Matplotlib
Day 3
- Intro and Implementation of Scikit Learn
- Implementation of Classification algorithms,
- Implementation of Regression algorithms,
- Implementation of Clustering algorithms,
- Implementation of Dimensionality and Reduction
Day 4
- Introduction to AI Conceptual Review
- Intro to TensorFlow
- Application of TensorFlow, Project implementation
- The building of Recommendation System
Day 5
- Introduction to Artificial Neural Network
- Essential Functions and Algo of NN & Deep Learning
- Transfer Learning Conceptual Review
- Implementation of CNN
- Implementation of RNN
- Final Intuation
