What you'll learn

Understand the data analysis process: Gain a deep understanding of the data analysis workflow, including data preprocessing, visualization.

Learn feature engineering. Learn how to extract meaningful insights from complex datasets and make data-driven decisions.

Master predictive modeling techniques: Develop expertise in building predictive models using machine learning algorithms.

Explore classification and regression models, understand their underlying principles, and learn how to apply them to solve real-world problems.

Acquire practical skills in machine learning: Gain hands-on experience in implementing machine learning techniques and algorithms.

Learn how to train and evaluate models, perform feature selection, handle imbalanced datasets, and optimize model performance.

Showcase skills through real-world projects: Work on five comprehensive projects covering a range of machine learning applications.

Including customer churn prediction, image classification, fraud detection, and housing price prediction.

Demonstrate your ability to apply machine learning concepts to solve practical problems and create impactful solutions.

Excel in data science interviews: Gain the confidence and knowledge to excel in data science interviews.

Learn how to effectively communicate your machine learning projects, explain your methodologies, and discuss the results.

Develop a strong portfolio of projects that can impress potential employers and demonstrate your proficiency in machine learning.

By achieving these learning objectives, learners will be equipped with the necessary skills and knowledge to tackle real-world machine learning problems.

Enhance your career prospects in data science, and confidently showcase your expertise during interviews.

Requirements

Python programming basics: Familiarity with the fundamentals of Python programming is recommended. Learners should have a basic understanding of variables, data types, loops, conditional statements, and functions. If you are new to Python, there are numerous online resources and tutorials available to help you get started.

Machine learning concepts: It is beneficial to have a foundational understanding of machine learning concepts. Familiarity with concepts such as supervised learning, unsupervised learning, classification, regression, and evaluation metrics will provide a solid foundation for the course. If you are new to machine learning, consider taking an introductory course or reviewing online tutorials to grasp the fundamental concepts.

Python libraries: Prior experience with Python libraries commonly used in machine learning, such as NumPy, Pandas, and scikit-learn, is advantageous. These libraries are extensively used throughout the course for data manipulation, analysis, and model implementation. If you are unfamiliar with these libraries, it is recommended to familiarize yourself with their basic usage and functionalities.

Jupyter Notebook: Familiarity with Jupyter Notebook, an interactive coding environment, is beneficial as it is used extensively in the course for code execution, data exploration, and project development. If you have not used Jupyter Notebook before, there are online tutorials and resources available to help you get started.

While these prerequisites are recommended, the course is designed to cater to learners with varying levels of experience. If you are a beginner in Python or machine learning, don't worry! The course provides step-by-step explanations, code walkthroughs, and resources to help you grasp the concepts and build your skills from the ground up.