Science and Machine Learning with Python
12 Weeks
2 Sessions / Week
3 Hours / Session
8,499 AED
Data science and machine learning are among the most sought-after skills in the tech industry, and this course provides a comprehensive introduction to these fields. It is designed for individuals who want to learn how to analyze data, build predictive models, and apply machine learning algorithms using Python.
The course begins with an overview of data science and its applications in various industries. Participants will learn how to manipulate and clean data using Python libraries like Pandas and NumPy. Data visualization is also covered, with tools like Matplotlib and Seaborn.
Machine learning is introduced as a way to build predictive models. Participants will learn about supervised and unsupervised learning algorithms, including regression, classification, and clustering. Model evaluation and tuning are also covered to ensure that participants can build accurate and reliable models.
The course includes a section on deep learning, where participants will learn the basics of neural networks and how to use TensorFlow. A capstone project is included, where participants will apply their knowledge to solve a real-world problem.
By the end of the course, participants will have the skills to work on data science and machine learning projects. This course is ideal for professionals, students, and developers who want to transition into data science or machine learning.
- Introduction to Data Science: Overview of data science and its applications.
- Data Manipulation: Using Pandas and NumPy for data cleaning and transformation.
- Data Visualization: Creating charts and graphs with Matplotlib and Seaborn.
- Machine Learning Basics: Supervised and unsupervised learning algorithms.
- Model Evaluation: Techniques for evaluating and improving model performance.
- Deep Learning Introduction: Basics of neural networks and TensorFlow.
- Capstone Project: Solving a real-world problem using data science techniques.
- Gain hands-on experience with data analysis and visualization.
- Learn to build machine learning models using Python libraries like Scikit-learn and TensorFlow.
- Understand the basics of artificial intelligence and its applications.
- Work on real-world projects to apply your knowledge.
- Professionals and students interested in data science and AI.
- Developers looking to transition into machine learning.
- Organizations seeking to upskill employees in data analysis and machine learning.