Beginner's Guide to Artificial Intelligence I

With our beginner's guide training programme, you can get started with Artificial Intelligence. Discover the core ideas of artificial intelligence, such as machine learning, neural networks, and natural language processing.

Live Course

Live Class: Saturday, 09 Mar

Duration: 27 Hours

Enrolled: 8

Offered by: infyni


Live Course

About Course

A course in Artificial Intelligence and Machine Learning course makes you an innovator and teaches you to build useful apps, entertaining games and create other innovations using Artificial Intelligence. The course also familiarizes the student with career options in AI and at the end of the course, demonstrate AI in action with a mini project.

The course is broken down into eight modules. Students who complete the course will learn what AI and ML is, understand its applications, and how it is transforming lives. You will explore basic AI concepts including machine learning, as well as use cases and applications of AI.

Skills You Will Gain

Artificial Intelligence Machine Learning Business problems Python Data Visualization NumPy SciPy Matplotlib Pandas Slkearn Supervised Learning Classification Regression

Course Offerings

  • Instructor-led interactive classes
  • Clarify your doubts during class
  • Access recordings of the class
  • Attend on mobile or tablet
  • Live projects to practice
  • Case studies to learn from
  • Lifetime mentorship support
  • Industry specific curriculum
  • Certificate of completion
  • Employability opportunity
  • Topics
  • Instructor (1)
  • Reviews
  • Introduction to Data Science, ML, DL & AI - why is it so important?
  • Applications of Data science across industries
  • Business problems – Analytics scenarios
  • Data Scientist Toolbox, Tool of choice- Python: what & why?
  • Introduction to Python
  • Installation of Python framework and packages: Anaconda and pip
  • Working with Jupyter Notebooks
  • Creating Python variables: Numeric, string and logical operations
  • Practice Assignments
  • Operations and Functions in Python
  • Writing for loops in Python
  • List & Dictionary Comprehension
  • Numerical Summary of Data
  • Writing for loops in Python
  • Need for visual summary
  • Introduction to Seaborn
  • Supervised Learning
  • Unsupervised Learning
  • Data Analysis Packages
  • NumPy
  • SciPy
  • Matplotlib
  • Pandas
  • Slkearn
  • Regression
  • Classification
  • Generalization, Overfitting, and Underfitting
  • Classification
  • Understand how continuous supervised learning is different from discrete learning
  • Code a Linear Regression in Python with scikit-learn
  • Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions


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