What you'll learn:
- Leverage ChatGPT for generating exact python code required for each tasks of data analysis and data science workflow and even for machine learning.
- Acquire the skills to clean raw data effectively, covering techniques for handling missing values, addressing different data types, and managing outliers etc.
- Master data manipulation by learning essential techniques such as sorting, filtering, merging, concatenating, and others using Python's pandas library.
- Learn exploratory data analysis techniques include frequencies, percentages, group-by operations, pivot tables, crosstabulation, and variable relationships.
- Dive into the world of data preprocessing with hands-on experience in feature engineering, selection, and scaling to prepare datasets for ML models.
- Apply your knowledge through a series of practical projects, reinforcing your understanding of each step in the data science workflow.
- Develop expertise in building and evaluating supervised regression models, including linear regression, random forest, decision tree, xgboost, and more.
- Gain practical skills in deploying supervised classification models, covering algorithms such as logistic regression, random forest, KNN, and lightgbm.
- Explore unsupervised learning by understanding and implementing clustering models like KMeans for uncovering hidden patterns in data.
- Learn Python's syntax, data types, variables, and operators to construct simple programs and execute basic functions.
- Become proficient in using essential Python libraries for data science, including pandas, numpy, seaborn, matplotlib, scikit-learn, and scipy.
- Test your knowledge and reinforce your learning through a series of seven-layered quizzes that cover various aspects of the data science workflow.
- Learn to regulate program flow, use loops and conditional statements like if, elif, and else.
- Experience the integration of ChatGPT to rise your understanding of data science applications through interactive conversations and real-world problem-solving.
- Acquire skills to use Python lists, dictionaries, tuples, and sets.
Embark on a comprehensive journey through the fascinating realm of data science and machine learning with our course, "Data Science and Machine Learning with Python and GPT 3.5." This course is meticulously designed to equip learners with the essential skills required to excel in the dynamic fields of data science and machine learning.
Throughout this immersive learning experience, you will delve deep into the core concepts of data science and machine learning, leveraging the power of Python programming alongside the cutting-edge capabilities of ChatGPT 3.5. Our course empowers you to seamlessly navigate the entire data science workflow, from data acquisition and cleaning to exploratory data analysis and model deployment.
You will master the art of cleaning raw data effectively, employing techniques tailored to handle missing values, diverse data types, and outliers, thus ensuring the integrity and quality of your datasets. Through hands-on exercises, you will become proficient in data manipulation using Python's pandas library, mastering essential techniques such as sorting, filtering, merging, and concatenating.
Exploratory data analysis techniques will be thoroughly explored, empowering you to uncover valuable insights through frequencies, percentages, group-by operations, pivot tables, crosstabulation, and variable relationships. Additionally, you will gain practical experience in data preprocessing, honing your skills in feature engineering, selection, and scaling to optimize datasets for machine learning models.
The course curriculum features a series of engaging projects designed to reinforce your understanding of key data science and machine learning concepts. You will develop expertise in building and evaluating supervised regression and classification models, utilizing a diverse array of algorithms including linear regression, random forest, decision tree, xgboost, logistic regression, KNN, lightgbm, and more.
Unsupervised learning techniques will also be explored, enabling you to uncover hidden patterns within data through the implementation of clustering models like KMeans and DBSCAN. Throughout the course, you will familiarize yourself with Python syntax, data types, variables, and operators, empowering you to construct robust programs and execute fundamental functions seamlessly.
Essential Python libraries for data science, including pandas, numpy, seaborn, matplotlib, scikit-learn, and scipy, will be extensively utilized, enabling you to tackle real-world challenges with confidence. Interactive quizzes, integrated seamlessly with ChatGPT, will test your knowledge and reinforce your learning across various aspects of the data science workflow.
By the conclusion of this transformative course, you will possess the requisite skills to communicate your findings effectively, translating complex data science results into clear and actionable insights for stakeholders. Join us on this exhilarating journey and unlock the boundless potential of data science and machine learning today!
