How to Study Data Science and Generative AI

TL;DR

Start with mathematics, then choose either the data analyst boot camp or the machine learning and NLP boot camp based on your career goal. Complete the machine learning path before taking the generative AI course, because it supplies prerequisites spanning Python, machine learning, NLP, deep learning, transformers, MLOps, deployment, Docker, Git, and end-to-end projects.

Transcript

hello all my name is krishak and welcome to my YouTube channel so guys from past couple of months I have uploaded somewhere around four to five very affordable udmi courses for every one of you out there and trust me the kind of feedback the the kind of uh questions that I've actually got and I'm really really overwhelmed by all your support it's q... Read More

Key Insights

• The mathematics course is the recommended starting point for learners from technical or nontechnical backgrounds because it establishes the prerequisites needed for data science, machine learning, and generative AI. It contains more than 23 hours of instruction ranging from basic concepts to advanced applications.
• Linear algebra is taught through practical data science applications rather than only definitions and problem solving. The material connects vectors, matrices, matrix multiplication, linear transformations, and eigenvalues with model training, dimensionality reduction, neural networks, computer graphics, principal component analysis, and feature engineering.
• Statistics and differential calculus are included alongside linear algebra to prepare students for later machine learning work. Derivatives are presented through multiple examples and mathematical notation, while the course also demonstrates how mathematics supports dimensionality reduction, deep learning, normalization, standardization, and visualization.
• The data analyst path follows the mathematics foundation and uses the Complete Data Analyst Bootcamp. Its current 52.5 hours include Python, statistics, exploratory data analysis, feature engineering, Power BI, and SQL Server, with planned additions covering Excel, ETL pipelines, and Tableau.
• The machine learning path follows mathematics and uses an approximately 92-hour boot camp. It covers Python, machine learning, NLP, deep learning through transformers, MLOps, Docker, Git, deployment, and end-to-end projects, providing the preparation recommended before moving into generative AI.
• The generative AI course should be taken after completing the machine learning and NLP boot camp. Its approximately 53.5 hours cover prerequisites and generative AI topics involving LangChain and Hugging Face, with material extending through LangGraph.
• The courses are presented as affordable options priced at 399 rupees, and the description says they are also available through Udemy Business. The instructor states that the 399-rupee price is intended to remain accessible to subscribers rather than being increased.
• Additional course plans include big data engineering and big data with AWS. The proposed big data engineering course would involve collaboration with an MNC-based mentor, divide recordings equally between the instructor and mentor, contain about 50 to 60 hours, and take roughly one month to produce.

Questions & Answers

Q: What order should I follow for these data science courses?

Begin with Mathematics Basics to Advanced for Data Science and Generative AI. After building that foundation, choose the Complete Data Analyst Bootcamp if your goal is data analysis, or the Complete Machine Learning and NLP Bootcamp if you want machine learning. Students on the machine learning path should finish that boot camp before starting the Complete Generative AI Course with LangChain and Hugging Face.

Q: Why should I study mathematics before machine learning?

Mathematics provides the prerequisites needed to understand the first machine learning algorithms more easily. The mathematics course connects linear algebra, statistics, and differential calculus with practical uses such as model training, dimensionality reduction, neural networks, principal component analysis, feature engineering, normalization, standardization, and visualization. This application-focused preparation is intended to make later machine learning material easier to follow.

Q: What mathematics topics are covered for data science?

The mathematics course contains more than 23 hours and covers linear algebra, statistics, differential calculus, functions and transformations, inverse functions or transformations, eigenvalues, vectors, matrices, matrix multiplication, and derivatives. It also shows how these subjects apply to data science, including deep learning, dimensionality reduction, model training, feature engineering, principal component analysis, normalization, standardization, and visualization.

Q: Which course should an aspiring data analyst take?

An aspiring data analyst should first complete the mathematics foundation and then take the Complete Data Analyst Bootcamp from Basics to Advanced. The boot camp currently contains about 52.5 hours and covers Python, statistics, exploratory data analysis, feature engineering, Power BI, and SQL Server. Excel, ETL pipelines, and Tableau are also planned additions to the same course.

Q: What does the machine learning and NLP boot camp cover?

The approximately 92-hour machine learning and NLP boot camp covers Python and the material required for machine learning and natural language processing. Its subjects include machine learning, NLP, deep learning through transformers, MLOps, Docker, Git, deployments, and end-to-end projects. Completing this broad curriculum is the recommended preparation before progressing to the generative AI course.

Q: When should I start the generative AI course?

Start the generative AI course after completing the machine learning and NLP boot camp. That sequence ensures exposure to Python, machine learning, NLP, deep learning, transformers, MLOps, deployment, Docker, Git, and end-to-end projects before moving forward. The generative AI course then provides about 53.5 hours of content spanning prerequisites, LangChain, Hugging Face, and LangGraph.

Q: How much do the courses cost and where are they available?

The instructor states that the courses are available for 399 rupees and that this price is intended to remain affordable for subscribers. The description also says that all the courses are available through Udemy Business, allowing people with a company Udemy account to access the content for free. Individual course links are provided in the description.

Q: What additional courses and live sessions are planned?

Additional free live classes are planned for people who have taken the courses, with the announcement placing their start in the following month. A big data engineering course is also planned through collaboration with a mentor working in an MNC, with each instructor handling half of the recordings. Big data with AWS is identified as a later target.

Summary & Key Takeaways

• The recommended learning path begins with Mathematics Basics to Advanced for Data Science and Generative AI. Its more than 23 hours cover linear algebra, statistics, differential calculus, transformations, eigenvalues, and applications in data science. Examples connect mathematical concepts with model training, dimensionality reduction, neural networks, feature engineering, normalization, standardization, and visualization.

• After mathematics, students should select a path based on their intended role. Aspiring data analysts can take the Complete Data Analyst Bootcamp, which currently contains about 52.5 hours covering Python, statistics, exploratory data analysis, feature engineering, Power BI, and SQL Server, with Excel, ETL pipelines, and Tableau also planned.

• Students interested in machine learning should complete the approximately 92-hour machine learning and NLP boot camp before advancing to the roughly 53.5-hour generative AI course. The boot camp covers machine learning, NLP, deep learning, transformers, MLOps, Docker, Git, deployments, and projects, while the generative AI course progresses through LangChain, Hugging Face, and LangGraph.