MACHINE LEARNING OVERVIEW

Machine Learning Course is designed to prepare you for entry & advance level job roles in its Professional fields. The Course is the right blend of theory and practice ensuring student-teacher engagement throughout the course. It is a kind of artificial intelligence (AI) that allows software applications to improve their prediction accuracy without being expressly designed to do so. To predict future output values, its algorithms use historical data as input. It is useful because it allows businesses to see the new trends in customer behaviour as well as the trends in market. It also helps in development of business. It is one in all the quickest growing fields within the sector of engineering and data Technology. Nowadays, each student desires to boost their ML skills, which is proving to be massively helpful in increasing the probabilities of their placements.

The average salary of a ML engineer is around 8 lakhs for a fresher.

Duration: 60 hours

Course Content:

1. Introduction to Machine Learning (ML)

• Meaning of ML
• Need of ML
• History of ML
• Applications of ML
• Libraries used in ML
• Types of ML Algorithm

2. Supervised ML Algorithms

Supervised ML Algorithms

3. Logistic Regression Algorithm

• Logistic Regression Algorithm Concept
• Logistic Regression Algorithm Program Implementation

4. KNN Algorithm

• KNN Algorithm Concept
• KNN Algorithm Program Implementation

5. Naive Bayes Algorithm

• Naive Bayes Algorithm Concept
• Naive Bayes Algorithm Implementation

6. Decision Tree Algorithm

• Decision Tree Algorithm Concept
• Decision Tree Algorithm Program Implementation

7. Random Forest Algorithm

• Random Forest Algorithm Concept
• Random Forest Algorithm Program Implementation

8. Support Vector Machine Algorithm

• Support Vector Algorithm Concept
• Support Vector Algorithm Program Implementation

9. Unsupervised ML Algorithms

• K-Means Algorithm
• K-Means Algorithm Concept
• K-Means Algorithm Program Implementation

10. Advance Concept

• ANN Algorithm
• ANN Algorithm Concept
• ANN Algorithm Program Implementation

11. Project Work

• Covering all the Concepts.
• Project Works

12. Interview Preparation

• Resume Preparation
• Interview Question Paper

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