Machine Learning

Dive into Comprehensive Machine Learning Course Tailored to Analyze Patterns, Predict Outcomes, and Drive Innovation and explore the power of data.

Objectives

Machine learning aims to develop algorithms that enable computers to learn from data and improve their performance over time without explicit programming. The primary objective is to create models that can generalize patterns from the input data, make predictions, and adapt to new information, enhancing decision-making and problem-solving across various domains.

This interdisciplinary field combines statistics, computer science, and domain expertise to automate tasks, discover insights, and drive advancements in artificial intelligence, fostering innovation in areas like healthcare, finance, and autonomous systems.

Eligibility

Eligibility for machine learning roles typically requires a strong foundation in mathematics, statistics, and programming. A bachelor’s degree in a related field such as computer science or engineering is common, while advanced positions may require a master’s or Ph.D. Relevant experience and proficiency in machine learning frameworks are also crucial.

Course outline

The introduction and installation process sets the stage for Python programming, paving the way for writing your first program. Understanding input/output (I/O) operations in Python completes the foundational skills needed for effective coding.

Exploring data types such as numbers and strings in Python, understanding the various operations that can be performed on them, and engaging in hands-on implementation provide a comprehensive approach to mastering Python programming.

Understanding indentation and scopes is fundamental for Python code structure. If-else and elif blocks provide conditional branching, while an introduction to loops, including for and while loops, enhances iterative processes. Break and continue statements within loops offer control and customization, contributing to efficient program flow.

Exploring lists, tulips, sets, and dictionaries allows for versatile data structures in Python, where CRUD operations (Create, Read, Update, Delete) play a crucial role in effective data manipulation. Building a Rock Paper Scissors game with console input/output provides a practical application to reinforce understanding and implementation of these data structures.

An introduction to functions, including an understanding of positional and keyword arguments, is essential for efficient code structure. Return statements facilitate the output of desired results, while incorporating try, catch, and finally blocks ensures robust error handling and graceful program execution.

File handling, involving Input/Output (I/O) operations, plays a crucial role in manipulating and managing data. Numpy arrays enhance efficient data handling in Python, enabling advanced numerical operations.

Our Pricings

Prices that are budget-friendly and suitable for students.

Self-Paced

5500
  • 30+ Recorded sessions
  • 6 month of LMS access
  • 1 Minor & 1 Major project
  • Doubt clearning session
  • Course completion certificate
  • Internship completion certificate

Mentor Led

7000
  • 16-24 Hrs live interactive session
  • 30+ Hrs recorded session
  • 6 month of LMS access
  • 1 Minor & 1 Major project
  • Resume building live session
  • Mock interviews
  • Add on MNC certificate
  • Doubt clearning sessions
  • Internship completion certificate
  • Course completion certificate
  • Excellent performance certificate
Popular

Advanced

8000
  • 16-24 Hrs live interactive session
  • 30+ Hrs recorded session
  • 6 month of LMS access
  • 2 Minor & 2 Major project
  • Resume building live session
  • Mock interviews
  • Add on MNC certificate
  • Doubt clearning sessions
  • Internship completion certificate
  • Course completion certificate
  • Excellent performance certificate
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