Full Stack Data Science & AI Courese

Full Stack Data Science & AI Courese

  • Teacher
    Teacher
    Mr. Sriram
  • Review
Courses
Course Summary

The Online Full Stack Data Science & AI Training course offers a thorough and immersive program meticulously crafted to empower participants with the essential knowledge and competencies crucial for success in the dynamic realm of data science and artificial intelligence (AI). Developed with precision, this course spans across a spectrum of critical subjects, encompassing data collection methodologies, preprocessing techniques, advanced analysis methodologies, machine learning principles, deep learning advancements, and practical applications of AI. Delivered in an online training format, participants have the flexibility to engage with course materials from anywhere, at any time, allowing for seamless integration into their schedules and lifestyles. Through a blend of interactive modules, virtual labs, and instructor-led sessions, participants will delve into the intricacies of handling diverse real-world datasets and honing their proficiency in implementing cutting-edge AI algorithms. Whether pursuing a career shift, seeking professional advancement, or simply expanding one's expertise, this online training course provides a comprehensive platform for individuals to embark on their journey towards mastery in data science and AI.

Learn software skills with real experts, either in live classes with videos or without videos, whichever suits you best.

 

 

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Requirements

  • Basic understanding of Python programming.
  • Familiarity with statistics and mathematics.
  • Knowledge of data manipulation libraries (e.g., NumPy, Pandas).
  • Understanding of data visualization techniques.
  • Basic knowledge of machine learning concepts.
  • Familiarity with SQL and databases.

Instructor
Mr. Sriram
Teacher

The Full Stack Data Science & AI course begins with an introduction to data science and AI, providing an overview of their applications and importance in various industries. Participants will learn about data collection methods, data preprocessing techniques, and exploratory data analysis (EDA). The course covers topics such as statistical analysis, machine learning algorithms, deep learning models, and AI applications. Practical exercises, projects, and case studies will be used to reinforce theoretical concepts and provide hands-on experience.

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