Machine Learning – Part I
Certification by GRADSKEY
- Foundation
- Medium
- Certificate
Machine Learning – Part I
Syllabus
65 hours of intractive online class.
Module 1: Introduction to Machine Learning |
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Basics of Artificial Intelligence, Machine Learning, and Deep Learning |
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Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning |
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Applications of Machine Learning in Real-world Industries |
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Machine Learning Workflow and Project Lifecycle |
Module 2: Python for Machine Learning |
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Python Fundamentals for Data Analysis |
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Working with NumPy Arrays and Mathematical Operations |
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Data Manipulation using Pandas |
Module 3: Data Collection and Preprocessing |
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Data Collection Techniques and Data Sources |
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Data Cleaning and Handling Missing Values |
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Feature Selection and Feature Engineering |
Module 4: Exploratory Data Analysis (EDA) |
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Understanding Data Distributions and Patterns |
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Statistical Analysis and Summary Measures |
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Correlation Analysis and Feature Relationships |
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Visualizing Data for Insights and Decision Making |
Module 5: Supervised Learning Fundamentals |
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Introduction to Classification and Regression |
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Training and Testing Datasets |
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Model Evaluation Metrics and Validation Techniques |
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Overfitting and Underfitting Concepts |
Module 6: Regression Algorithms |
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Simple and Multiple Linear Regression |
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Polynomial Regression Techniques |
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Model Building and Performance Evaluation |
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Real-world Regression Applications |
Module 7: Classification Algorithms |
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Logistic Regression for Classification Problems |
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K-Nearest Neighbors (KNN) Algorithm |
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Decision Tree Classification |
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Naive Bayes Classification Technique |
Module 8: Model Evaluation and Optimization |
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Confusion Matrix and Classification Metrics |
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Cross Validation Techniques |
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Hyperparameter Tuning Basics |
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Performance Improvement Strategies for ML Models |
Instructor
Corporate Instructors
From Premier Companies.
“Instructors are from Corporate – Software Engineers from premium companies, working on the cutting-edge technologies and Java on a day to day basis..”
What Our Learners Say
