Model deployment is the process of integrating a trained Machine Learning model into a real-world application so it can make predictions on new data.
About Course
Learn Machine Learning with real-world tools, hands-on projects, and industry insights to build models, automate predictions, and make intelligent, data-driven business solutions confidently.
Course Content
Introduction to Machine Learning
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What is Machine Learning?
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Types of Machine Learning
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Real-world Applications
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Business Use Cases
Python for Machine Learning
Mathematics for ML
Data Preprocessing
Exploratory Data Analysis (EDA)
Supervised Learning Overview
Linear Regression
Logistic Regression
Decision Trees
Random Forest
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Naive Bayes
Unsupervised Learning Overview
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
Model Evaluation Techniques
Hyperparameter Tuning
Introduction to Deep Learning
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Model Deployment
Capstone Project
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