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Workshop detalis

Machine Learning

Workshop: 2/3days
Degree:Any branch
Enrolled: 60 students
(30 Reviews)

Machine Learning (ML) is a branch of Artificial Intelligence that enables computer systems to learn from data and improve their performance without being explicitly programmed. Instead of following fixed rules, machine learning algorithms analyze patterns in historical data and make predictions or decisions based on new inputs.

Machine learning is widely used in applications such as recommendation systems, image and speech recognition, fraud detection, predictive analytics, healthcare diagnosis, and autonomous systems. It helps organizations automate decision-making processes and extract valuable insights from large volumes of data.

Course description

This course provides a strong foundation in Machine Learning concepts, techniques, and real-world applications. It introduces learners to how machines learn from data and make intelligent decisions using algorithms and statistical models. The course focuses on building practical skills using Python and popular Machine Learning libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.

What you'll learn from this course
  • Understanding the fundamentals of Machine Learning and its real-world applications.
  • Learning Python basics for Machine Learning, including NumPy, Pandas, and Matplotlib.
  • Gaining knowledge of data collection, cleaning, and preprocessing techniques.
  • Understanding different types of Machine Learning such as supervised, unsupervised, and reinforcement learning.
  • Implementing common Machine Learning algorithms like Linear Regression, Decision Trees, K-Means, and Random Forest.
Certification

At TechIn IT, we proudly assure that every Trainee who successfully completes our program will be awarded a certificate. We are officially associated with APSCHE, AICTE, MSME, Skill India, IAF, and NASSCOM. The certification will reflect the Trainees dedication and skill development, recognized under national-level standards .

  • Introduction to Machine Learning

    Overview of Machine Learning and Artificial Intelligence.

    Applications of Machine Learning in real life.

    Types of Machine Learning (Supervised, Unsupervised, Reinforcement).

  • Data Handling and Preprocessing

    Understanding datasets and data types.

    Data collection and data cleaning.

    Handling missing values and outliers.

  • Machine Learning Algorithms

    Introduction to Supervised Learning.

    Linear Regression and Logistic Regression.

    Introduction to classification problems.

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