Machine Learning Course

Practical Machine Learning (ML) Course

This practical Machine Learning (ML) course feature into the world of machine learning, covering key algorithms, model training, and applications. I-Tech System is one of the best training institute in IT to offer the Machine Learning course. Whether you’re new to ML or seeking to enhance your skills, this course equips you with the knowledge and experience to excel in the knowledge of machine learning.

Machine Learning Course Details:

Introduction to Machine Learning

  • Understanding Machine Learning
  • The Role of ML in Data Analysis
  • Key Concepts and Terminology
  • Overview of ML Tools and Frameworks

Fundamentals of Machine Learning

  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement
  • Model Training and Evaluation
  • Feature Engineering and Data Preprocessing
  • Practical Machine Learning Examples

Data Preprocessing for ML

  • Data Collection and Cleaning
  • Feature Selection and Extraction
  • Data Scaling and Transformation
  • Handling Missing Data
  • Data Privacy and Ethics in ML

Supervised Learning Algorithms

  • Linear Regression and Logistic Regression
  • Decision Trees and Random Forest
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (k-NN)
  • Model Selection and Hyperparameter Tuning

Unsupervised Learning and Clustering

  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Anomaly Detection
  • Real-world Applications

Natural Language Processing (NLP)

  • Text Preprocessing and Tokenization
  • Sentiment Analysis
  • Named Entity Recognition
  • Language Modeling
  • Real-world NLP Applications

Deep Learning and Neural Networks

  • Introduction to Neural Networks
  • Feedforward Neural Networks (FNN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Training Deep Learning Models

Real-world Machine Learning Projects

  • Implementing ML Solutions
  • Collaborative ML Development
  • Project Management and Deployment
  • Presenting ML Findings

Couse Duration: 2 Months

  • Daily 2 Hrs
  • Week End Extra Practice
  • Career Guidance sessions
  • Week End Batches are also available.

Course Benefits:

  • Hands-on projects and practical exercises
  • Access to real datasets and case studies
  • Quizzes and assessments for progress tracking
  • Support from a community forum
  • Course completion certificate

Prerequisites: No specific prerequisites are required for this course. It is suitable for both beginners and individuals with some prior knowledge of programming and mathematics.

Target Audience:

  • Aspiring machine learning professionals
  • Data analysts looking to deepen their ML skills
  • Tech enthusiasts interested in the practical applications of machine learning

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