2025 Machine Learning & Data Science for Beginners in Python

It seems like you've provided a comprehensive overview of the topics that will be covered in the Python for Data Science course. This course aims to guide learners from the basics of Python programming to advanced data science techniques, including machine learning and deep learning models. Here's a summary of what you've outlined:

Python Programming Fundamentals:

• Basics of Python: Variables, data types, control flow, functions, and error handling.
• Libraries and tools for data analysis: Pandas for data manipulation, NumPy for numerical computation, Matplotlib and Seaborn for data visualization.

Data Cleaning and Preparation:

• Handling missing data and outliers.
• Data normalization and transformation techniques.

Statistical Foundations:

• Statistical tests to analyze datasets.
• Probability distributions and hypothesis testing.

Supervised Learning Models:

• Linear Regression, Logistic Regression, Decision Trees, Random Forest, AdaBoost, XGBoost, and CatBoost.

Unsupervised Learning Models:

• K-Means, DBSCAN, Hierarchical Clustering, and Spectral Clustering.
• Dimensionality reduction techniques like Principal Component Analysis (PCA).

Advanced Deep Learning Models:

• Introduction to Deep Learning with a focus on Multi-Layer Perceptrons (MLP), which can be used for classification and regression tasks.
• Natural Language Processing (NLP) using term frequency-inverse document frequency (tf-idf).

Course Requirements:

• Basic knowledge of Python programming.
• A computer with either Mac, Windows, or Linux operating system.
• Enthusiasm and willingness to learn.

Target Audience:

• Beginners in python programming and data science.
• Students pursuing degrees in data science or machine learning.
• Individuals interested in Python for data analysis, visualization, or machine learning tasks.
• Developers who want to shift towards analytics and visualization projects.
• Those curious about the field of data science.

Support:

• Access to experienced instructors for guidance.
• A community of fellow learners for additional support.

Learning Approach:

• Self-paced learning, allowing you to go through materials at your own speed.

The course is designed to be comprehensive, covering a wide range of topics from the fundamentals of Python programming to advanced machine learning techniques. It aims to equip learners with practical skills in data analysis, modeling, and visualization, which are essential for a career in data science. With hands-on projects and real-world applications, learners will be able to apply their knowledge effectively.