bt_bb_section_bottom_section_coverage_image
Scikit-Learn Training

Scikit-Learn Training

Welcome to the 5-day Scikit-Learn Training program by KONCPT AI. This Corporate training is designed to equip participants with the skills and knowledge needed to effectively use Scikit-Learn, one of the most powerful and versatile machine learning libraries in Python. The course combines lectures with hands-on exercises to provide a comprehensive learning experience, covering both fundamental and advanced aspects of machine learning.

Description

Welcome to the 5-day Scikit-Learn Training program by KONCPT AI. This Corporate training is designed to equip participants with the skills and knowledge needed to effectively use Scikit-Learn, one of the most powerful and versatile machine learning libraries in Python. The course combines lectures with hands-on exercises to provide a comprehensive learning experience, covering both fundamental and advanced aspects of machine learning.

Benefits of Attending This Training

  1. Hands-On Experience: Practical exercises and projects to apply what you learn.
  2. Expert Instructors: Guidance from experienced machine learning professionals.
  3. Comprehensive Curriculum: Thorough coverage of essential and advanced topics.
  4. Career Advancement: Improve your job prospects in data science and AI fields.
  5. Networking Opportunities: Connect with peers and experts in the field.
  6. Certification: Receive a certificate of completion to showcase your skills.

Reviews

There are no reviews yet.

Be the first to review “Scikit-Learn Training”

Your email address will not be published. Required fields are marked *

Course Content


Day 1: Introduction to Machine Learning and Scikit-Learn

  1. Overview of machine learning concepts
  2. Introduction to Scikit-Learn and its ecosystem
  3. Installing and setting up Scikit-Learn
  4. Understanding the Scikit-Learn API
  5. Supervised vs. unsupervised learning
  6. Hands-on exercises: Basic data manipulation with Scikit-Learn
  7. Loading and preparing datasets
  8. Splitting data into training and testing sets
  9. Feature scaling and normalization
  10. Evaluating machine learning models

 Supervised Learning Algorithms

  1. Linear regression
  2. Logistic regression
  3. Decision trees
  4. Random forests
  5. Support vector machines (SVM)
  6. Hands-on exercises: Implementing supervised learning algorithms
  7. Model evaluation metrics: Accuracy, precision, recall, F1 score
  8. Cross-validation techniques
  9. Hyperparameter tuning with GridSearchCV and RandomizedSearchCV
  10. Case studies and practical applications

 Unsupervised Learning Algorithms

  1. K-means clustering
  2. Hierarchical clustering
  3. Principal Component Analysis (PCA)
  4. Independent Component Analysis (ICA)
  5. t-Distributed Stochastic Neighbor Embedding (t-SNE)
  6. Hands-on exercises: Implementing unsupervised learning algorithms
  7. Evaluating clustering performance
  8. Dimensionality reduction techniques
  9. Anomaly detection methods
  10. Case studies and practical applications

Model Selection and Validation

  1. Model selection techniques
  2. Bias-variance tradeoff
  3. Ensemble methods: Bagging and boosting
  4. Hands-on exercises: Implementing ensemble methods
  5. Validation curves and learning curves
  6. Feature selection techniques
  7. Pipelines and workflow automation
  8. Hands-on exercises: Building and optimizing machine learning pipelines
  9. Best practices for model selection and validation
  10. Case studies and practical applications

Advanced Topics and Real-World Applications

  1. Natural Language Processing (NLP) with Scikit-Learn
  2. Time series analysis and forecasting
  3. Working with imbalanced datasets
  4. Handling missing data and outliers
  5. Hands-on exercises: Implementing advanced machine learning techniques
  6. Model deployment and integration
  7. Performance optimization and scaling
  8. Monitoring and maintaining deployed models
  9. Future trends in machine learning with Scikit-Learn
  10. Building a complete machine learning project from start to finish


Contact us

By the end of this 5-day training program, participants will have a strong foundation in using Scikit-Learn for various machine learning tasks, from basic data preprocessing to advanced model deployment. Join us at KONCPT AI to enhance your machine learning skills and take your career to the next level!

Related products