Practical Data Science: Machine Learning, AI, Cloud Computing, and Data Analysis in Python and Azure ML

What you'll learn
Hands On Learning of Data Analysis and Manipulation in Python
Understand and Apply Key Statistical Concepts
Visualize Data to Extract Insights using Matplotlib and Seaborn
Develop and Evaluate Machine Learning Models with Python and Azure Machine Learning Studio
Experience with Cloud Computing and Natural Language Processing

Requirements
There are no prerequisites for this course - it's designed for beginners
All you need is a computer, an internet connection, and a willingness to learn

Description
"Data Science for Beginners - Python & Azure ML with Projects" is a hands-on course that introduces the essential skills needed to work in data science. Designed for beginners, this course covers Python programming, data analysis, statistics, machine learning, and cloud computing with Azure. Each topic is taught through practical examples, real-world datasets, and step-by-step guidance, making it accessible and engaging for anyone starting out in data science.What You Will LearnPython Programming Essentials: Start with a foundation in Python, covering essential programming concepts such as variables, data types, functions, and control flow. Python is a versatile language widely used in data science, and mastering these basics will help you perform data analysis and build machine learning models confidently.Data Cleaning and Analysis with Pandas: Get started with data manipulation and cleaning using Pandas, a powerful data science library. You'll learn techniques for importing, exploring, and transforming data, enabling you to analyze data effectively and prepare it for modeling.Statistics for Data Science: Build your knowledge of key statistical concepts used in data science. Topics include measures of central tendency (mean, median, mode), measures of variability (standard deviation, variance), and hypothesis testing. These concepts will help you understand and interpret data insights accurately.Data Visualization: Gain hands-on experience creating visualizations with Matplotlib and Seaborn. You'll learn to make line plots, scatter plots, bar charts, heatmaps, and more, enabling you to communicate data insights clearly and effectively.Practical, Real-World ProjectsThis course emphasizes learning by doing, with two in-depth projects that simulate real-world data science tasks:California Housing Data Analysis: In this project, you'll work with California housing data to perform data cleaning, feature engineering, and analysis. You'll build a regression model to predict housing prices and evaluate its performance using metrics like R-squared and Mean Squared Error (MSE). This project provides a full-cycle experience in working with data, from exploration to model evaluation.Loan Approval Model in Azure ML: In the second project, you'll learn how to create, deploy, and test a machine learning model on the cloud using Azure Machine Learning. You'll build a classification model to predict loan approval outcomes, mastering concepts like data splitting, accuracy, and model evaluation with metrics such as precision, recall, and F1-score. This project will familiarize you with Azure ML, a powerful tool used in industry for cloud-based machine learning.Customer Churn Analysis and Prediction: In this project, you will analyze customer data to identify patterns and factors contributing to churn in a banking environment. You'll clean and prepare the dataset, then build a predictive model to classify customers who are likely to leave the bank. By learning techniques such as feature engineering, model training, and evaluation, you will utilize metrics like accuracy, precision, recall, and F1-score to assess your model's performance. This project will provide you with practical experience in data analysis and machine learning, giving you the skills to tackle real-world challenges in customer retentionMachine Learning and Cloud ComputingMachine Learning Techniques: This course covers the foundational machine learning techniques used in data science. You'll learn to build and apply models like linear regression and random forests, which are among the most widely used models in data science for regression and classification tasks. Each model is explained step-by-step, with practical examples to reinforce your understanding.Cloud Computing with Azure ML: Get introduced to the world of cloud computing and learn how Azure Machine Learning (Azure ML) can simplify model building, deployment, and scaling. You'll explore how to set up an environment, work with data assets, and run machine learning experiments in Azure. Learning Azure ML will prepare you for a cloud-based data science career and give you skills relevant to modern data science workflows.Additional FeaturesUsing ChatGPT as a Data Science Assistant: Discover how to leverage AI in your data science journey by using ChatGPT. You'll learn techniques for enhancing productivity, drafting data queries, and brainstorming ideas with AI, making it a valuable assistant for your future projects.Testing and Practice: Each section includes quizzes and practice exercises to reinforce your learning. You'll have the opportunity to test your understanding of Python, data analysis, and machine learning concepts through hands-on questions and real coding challenges.By the end of this course, you'll have completed practical projects, gained a strong foundation in Python, and developed skills in data science workflows that are essential in today's data-driven world. Whether you're looking to start a career in data science, upskill, or explore a new field, this course offers the knowledge and hands-on experience you need to get started.

Who this course is for
This course is perfect for beginners who are curious about data science and want a hands-on introduction to this exciting field.
It's ideal for students, career changers, and professionals from non-technical backgrounds who are looking to build a solid foundation in data science skills, including Python programming, data analysis, statistics, machine learning and cloud computing.

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