The course included the following key topics:
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Introduction to Machine Learning:
- Overview of machine learning and its applications.
- Understanding different types of machine learning: supervised, unsupervised, and reinforcement learning.
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Data Preprocessing:
- Techniques for data cleaning, normalization, and transformation.
- Handling missing data and categorical variables.
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Supervised Learning Algorithms:
- Linear and logistic regression.
- Decision trees and random forests.
- Support Vector Machines (SVM).
- Neural networks and deep learning basics.
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Unsupervised Learning Algorithms:
- Clustering techniques like K-Means and Hierarchical Clustering.
- Dimensionality reduction techniques.
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Model Evaluation and Tuning:
- Cross-validation techniques.
- Hyperparameter tuning using grid search and random search.
- Data Analysis and Preprocessing: Proficient in preparing and cleaning data for machine learning models.
- Algorithm Implementation: Hands-on experience with implementing various machine learning algorithms.
- Model Evaluation: Ability to evaluate model performance and fine-tune parameters for optimal results.
- Practical Application: Applied machine learning techniques to solve real-world problems and business scenarios.
After completing this course, I worked on several projects that allowed me to apply my knowledge practically:
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Predictive Analytics Project:
- Developed a predictive model to forecast sales using historical data.
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Customer Segmentation:
- Implemented clustering algorithms to segment customers based on purchasing behavior.
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Sentiment Analysis:
- Built a sentiment analysis model to classify customer reviews as positive or negative.