Python for Data Science & Machine Learning: Zero to Hero – Online Course

The Python for Data Science & Machine Learning: Zero to Hero online course is a complete training program designed for learners who want to master Python and its applications in data science and machine learning. This course covers everything from foundational Python programming to advanced machine learning algorithms, ensuring learners gain both theoretical knowledge and hands-on experience. Participants will explore Python libraries, data cleaning techniques, exploratory data analysis, visualisation tools, and various machine learning models, preparing them for real-world data-driven projects.

Through the Python for Data Science & Machine Learning: Zero to Hero course, learners will develop skills in handling large datasets, performing time-series analysis, building predictive models, and creating recommendation systems. The curriculum emphasizes practical implementation, empowering learners to confidently apply Python in data science and machine learning workflows. By completing this training, participants will acquire the technical expertise needed to transition from beginners to proficient data science professionals.

Learning Outcomes

By the end of this course, learners will be able to:

1. Master Python programming for data science and machine learning tasks.
2. Utilize essential Python libraries for data manipulation and analysis.
3. Clean, transform, and visualize datasets for better insights.
4. Apply regression, classification, and clustering algorithms effectively.
5. Build predictive models and recommendation systems using Python.
6. Solve real-world data science problems with practical implementation skills.

Course Link : Python for Data Science & Machine Learning: Zero to Hero – Online Course

Course Curriculum

Module 01: Introduction

• Overview of Python for data science and machine learning
• Understanding the course structure and learning objectives

Module 02: The Must-Have Python Data Science Libraries

• Introduction to NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn
• Using libraries for efficient data processing and analysis

Module 03: NumPy Mastery: Everything you need to know about NumPy

• Arrays, vectorization, and advanced NumPy operations
• Practical exercises for data manipulation and calculations

Module 04: DataFrames and Series in Python's Pandas

• Creating, accessing, and modifying DataFrames and Series
• Handling missing values and performing data transformations

Module 05: Data Cleaning Techniques for Better Data

• Removing duplicates, handling missing values, and normalizing data
• Improving data quality for more accurate analyses

Module 06: Exploratory Data Analysis in Python

• Visualizing data distributions and relationships between variables
• Using statistical methods to summarize and understand datasets

Module 07: Python for Time-Series Analysis: A Primer

• Working with date-time data and time-series datasets
• Performing trend analysis, forecasting, and seasonal decomposition

Module 08: Python for Data Visualisation: Library Resources, and Sample Graphs

• Using Matplotlib, Seaborn, and Plotly for effective visualizations
• Creating bar charts, line graphs, scatter plots, and heatmaps

Module 09: The Basics of Machine Learning

• Understanding supervised, unsupervised, and reinforcement learning
• Preparing datasets for machine learning applications

Module 10: Simple Linear Regression with Python

• Implementing regression models to predict continuous variables
• Evaluating model performance using metrics and visualizations

Module 11: Multiple Linear Regression with Python

• Building models with multiple independent variables
• Handling multicollinearity and interpreting coefficients

Module 12: Classification Algorithms: K-Nearest Neighbors

• Using KNN for classification problems and predictions
• Tuning hyperparameters for improved accuracy

Module 13: Classification Algorithms: Decision Tree

• Building decision trees for classification tasks
• Understanding tree splitting, pruning, and overfitting prevention

Module 14: Classification Algorithms: Logistic Regression

• Applying logistic regression for binary classification problems
• Interpreting probabilities and evaluating model performance

Module 15: Clustering

• Introduction to unsupervised learning and clustering algorithms
• Implementing K-Means and hierarchical clustering techniques

Module 16: Recommender System

• Building recommendation engines using Python and machine learning
• Applying collaborative and content-based filtering methods

Module 17: Conclusion

• Summarizing key takeaways from Python and machine learning modules
• Planning next steps for real-world applications and projects

Disclaimer:

This is an online course with pre-recorded lessons. You will get access to the course within 48 hours after your enrolment.