Machine Learning with Python

Last Updated: 31 03 2025

Machine learning with Python course is designed to equip learners with the essential knowledge and hands-on skills required to master machine learning. This training covers everything from basic concepts to advanced techniques, ensuring a well-rounded understanding of the field. 
 
Participants will detect large mathematical foundations such as linear algebra, probability and data, which are crucial for developing strong machine learning models. The curriculum also emphasizes functional technique, so that learners can limit data for better model performance. In addition, topics such as data Preprocessing, Exploratory Data Analysis and performance matrix are covered to enhance the skills of the real world. 
 
The curriculum flows into various machine learning types, including monitored learning (regression and classification) and Unsupervised learning (clustering and association rules mining). Learners will gain practical experience in using Python and industry standard equipment such as Scikit-Learn and ensure that they can confidently implement the model. 
 
Towards the end of the program, participants will be well prepared for the Python Machine Learning certification, making them very competitive in the job market. Whether you’re an aspiring data scientist or a professional looking to upskills, this machine learning python training provides the expertise needed to excel in the ever-evolving AI landscape. 

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Learning Options for You

  • Live Training (Duration : 32 Hours)
  • Per Participant

Fee: OnRequest

09:00 - 13:00 (IST)

(4 Hours/Day)

09:00 - 17:00 (IST)

(8 Hours/Day)

Course Prerequisites

  • Basic programming knowledge in Python (variables, loops, functions)
  • Understanding of fundamental mathematics (linear algebra, probability, statistics)
  • Familiarity with data structures and basic data handling using Pandas and NumPy (optional but beneficial)
     

Learning Objectives

  • Understand the fundamentals of machine learning and its real-world applications
  • Work with data preprocessing, feature engineering, and model evaluation techniques
  • Implement supervised learning algorithms such as linear regression, decision trees, and support vector machines
  • Explore unsupervised learning techniques like clustering and dimensionality reduction
  • Understand neural networks and deep learning basics using TensorFlow and Keras
  • Perform hyperparameter tuning and model optimization
  • Develop end-to-end machine learning projects from data preparation to model deployment
     

Target Audience

  • Data science and AI enthusiasts who want to learn machine learning with Python
  • Software developers, analysts, and engineers looking to integrate machine learning into their projects
  • Students and researchers interested in predictive analytics and data-driven decision-making
  • Professionals seeking career advancement in data science, AI, and machine learning

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