Machine Learning (ML) is a different approach where the computer learns the rules of solving complex problems without being explicitly programmed. Machine Learning algorithms are at the core and important pieces of data science.
Machine Learning Expert course is designed in accordance with IABAC™ (International Association of Business Analytics Certifications) to provide a theory and application of popular Machine Learning algorithms in Supervised, Unsupervised, and Deep Learning. Major algorithms are discussed in more detail about how they work and apply this algorithm with real-world data.
Finally, we create ML models with the Python Scikit-Learn package in solving business problems and case studies. This will give you a complete knowledge of Machine Learning algorithms, how they work, how to optimize, advantages and disadvantages of each algorithm along with the practical application.
Certified Machine Learning Expert Course Modules
Machine Learning essentials
- Data preprocessing
- Types of ML algorithms: Supervised and Unsupervised.
- Overview of Classification, Regression, and Clustering algorithms
Machine Learning Algorithms Concepts
- Supervised Machine Learning algorithms
- K-Nearest Neighbors (KNN) concept and application
- Naive Bayes concept and application
- Logistic Regression concept and application
- Classification Trees concept and application
- Unsupervised Machine Learning algorithms
- Clustering with K-means concept and application
- Hierarchical Clustering concept and application
Data Processing for Machine Learning
- Advanced-Data Mugging
- Outlier Analysis
- Treating for missing values
- Normalization vs Standardization of data
Machine Learning Hands-on Project
- Creating an application for Hiring Employee for MNC
- Setting up the project with ML workflow.
- Data Preprocessing and statistical exploration
- Building, Training, and evaluation of Machine Learning Model
- Packaging and deploying