Successfully completed the Machine Learning A-Z: AI, Python & AWS course, gaining both theoretical knowledge and hands-on experience in building, evaluating, deploying, and monitoring Machine Learning models using Python and AWS.
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Data Preprocessing & Feature Engineering
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Exploratory Data Analysis (EDA)
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Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
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Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
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Association Rule Learning
- Apriori
- Eclat
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Reinforcement Learning
- Upper Confidence Bound (UCB)
- Thompson Sampling
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Natural Language Processing (NLP)
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Deep Learning
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
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Dimensionality Reduction
- PCA
- LDA
- Kernel PCA
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Model Selection & Hyperparameter Tuning
- K-Fold Cross Validation
- Grid Search
- XGBoost
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Ensemble Learning
- LightGBM
- CatBoost
- Amazon SageMaker
- AWS Glue
- AWS Glue DataBrew
- SageMaker Data Wrangler
- Amazon S3
- Amazon Comprehend
- Amazon Rekognition
- Amazon Textract
- Amazon Polly
- Amazon Transcribe
- Amazon Translate
- Model Deployment on AWS
- CI/CD Pipelines for Machine Learning
- Model Monitoring & Responsible AI
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
- TensorFlow
- Keras
- PyTorch
- AWS
- Jupyter Notebook
- Git
- GitHub
- Built and evaluated Machine Learning models using real-world datasets.
- Applied data preprocessing and feature engineering techniques.
- Performed model optimization and hyperparameter tuning.
- Learned end-to-end Machine Learning workflows from data preparation to deployment.
- Gained practical experience with cloud-based Machine Learning using AWS.
Yathish Gowda C Computer Science Engineering Student | Machine Learning & AI Enthusiast
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