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Master the Art of Intelligence :
Daisy Minds's Machine Learning in AI

Unleash the power of data and algorithms with Daisy Minds's Machine Learning in AI domain. Dive into the heart of artificial intelligence, where innovation meets data science. Our meticulously crafted courses empower you to navigate the complexities of machine learning, from algorithms to predictive modeling. Elevate your skills and be at the forefront of the AI revolution. Explore the endless possibilities, and shape the future with Daisy Minds. Your journey to AI mastery starts here.

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Training Phase

1. Comprehensive training covering essential topics.
2. Mini projects assigned after each topic for practical application.
3. Emphasis on thorough understanding and skill development.

Continued Training and Projects

1. Ongoing training with in-depth exploration of advanced concepts.
2. Mini projects to reinforce learning and practical expertise.
3. Interactive sessions to address queries and challenges.

Capstone Projects and Internship Preparation

1. Engaging in 2 capstone projects to demonstrate proficiency.
2. Transition towards practical application and real-world scenarios.
3. Preparing for corporate internships through hands-on experiences

Course Curriculum

Certification in AI-ML

1 .Introduction to Statistics

•Descriptive statistics
• Inferential statistics
• Introduction to Machine Learning
• What is Machine Learning?
• Supervised Learning
• Unsupervised Learning
• NaN

2 .Linear Regression

• Machine Learning Honor Code
• What is Machine Learning?
• Multiple Features
• Model evaluation metrics
• Features and Polynomial Regression • Logistic Regression
• Classification model evaluation metrics

3 .Designing Security Controls

• Understand the controls that help in detecting security threats.
• Develop a deeper understanding of SIEM and its purpose.
• Familiarize yourself with the reviewing, interpreting
• Understanding Shell Scripting.

4 .Decision Tree & Support Vector

• Decision Tree intro and Explanation.
• Decision Tree Code
• Optimization Objective
• Large Margin Intuition
• Using An SVM

5 .Ensemble Learning

• Intro to Ensemble Learning
• Bais And Varience
• Different Algorithms and camparison
• Bagging and Boosting
• Random Forests and Decision Trees
• Hyperparameter Tuning
• In details of Ensemble Learning

6 . Random Forest & Regularization

• Random Forest intro and Explanation
• Random Forest Code
• Regularization Explained
• Lasso, Ridge and ElesticNet Regression
• ElesticNet Regression code

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FEE STRUCTURE

Self Paced

5700 ₹

  • • Recorded Sessions ✅
  • • Hands-on-projects✅
  • • Certifications ✅
  • • Live Sessions❌
  • • Doubt clearing sessions❌
  • • Mentor Guidance❌
  • • Placement Assistance❌
  • • Guaranteed Internship❌

Mentor Led

8500 ₹

  • • Recorded Sessions✅
  • • Hands-on-projects✅
  • • Certifications ✅
  • • Live Sessions✅
  • • Doubt clearing sessions✅
  • • Mentor Guidance✅
  • • Placement Assistance✅
  • • Guaranteed Internship❌

Professional

14300 ₹

  • • Recorded Sessions✅
  • • Hands-on-projects✅
  • • Certifications ✅
  • • Live Sessions✅
  • • Doubt clearing sessions✅
  • • Mentor Guidance✅
  • • Placement Assistance✅
  • • Guaranteed Internship✅

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