- Understanding the basics of machine learning
- Differentiating between supervised and unsupervised learning
- Overview of popular machine learning algorithms and their applications

- Introduction to Python programming language
- Data types, variables, and control flow
- Essential Python libraries for machine learning (NumPy, Pandas, Matplotlib)

- Handling missing values and outliers
- Feature scaling and normalization
- Exploratory data analysis and visualization

- Linear regression for continuous target variables
- Logistic regression for binary classification
- Decision trees and random forests
- Support vector machines (SVM)
- Naive Bayes classifiers

- Clustering algorithms (K-means, hierarchical clustering)
- Dimensionality reduction techniques (Principal Component Analysis, t-SNE)
- Association rule learning (Apriori algorithm)

- Model evaluation metrics (accuracy, precision, recall, F1 score)
- Cross-validation techniques
- Overfitting and underfitting

- Techniques for selecting relevant features
- Creating new features from existing data
- Handling categorical variables

- Introduction to artificial neural networks
- Building and training deep neural networks
- Convolutional Neural Networks (CNN) for image classification
- Recurrent Neural Networks (RNN) for sequence data

- Techniques for optimizing model performance
- Grid search and randomized search for hyperparameter tuning
- Model selection and ensemble methods

- Processing and analyzing textual data
- Text classification and sentiment analysis
- Named Entity Recognition (NER) and text summarization

- Exporting machine learning models for deployment
- Building RESTful APIs for model serving
- Deploying models on cloud platforms (AWS, GCP)

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