Machine Learning
In the Machine Learning internship program, you will explore how computers can learn from data to make predictions or decisions without being explicitly programmed. This internship introduces core ML concepts such as data preprocessing, model training, evaluation metrics, and algorithm
tuning. You will gain hands-on experience with supervised and unsupervised learning techniques using real datasets and industry-standard libraries.
Level 1: Easy Projects
Task 1: Setting Up Machine Learning Environment
Problem Statement:
Set up a Python environment with machine learning libraries and run a simple ML script.
Steps to Complete:
• Install Python, Jupyter Notebook, and libraries like scikit-learn, numpy, pandas
• Load a sample dataset (e.g., Iris dataset)
• Implement a simple classification using k-Nearest Neighbors
• Train the model and evaluate accuracy
Task 2: Data Preprocessing for ML
Problem Statement:
Clean and prepare data for machine learning models by handling missing values and scaling.
Steps to Complete:
• Load a dataset with missing or inconsistent data
• Handle missing values (imputation or removal)
• Encode categorical variables
• Scale features using StandardScaler or MinMaxScaler
Task 3: Implementing Linear Regression
Problem Statement:
Build and evaluate a linear regression model to predict a continuous variable.
Steps to Complete:
• Select a dataset with numeric targets
• Split data into training and testing sets
• Train a linear regression model using scikit-learn
• Evaluate with RMSE and R² metrics
Level 2: Intermediate Projects
Task 4: Classification with Decision Trees
Problem Statement:
Develop a decision tree classifier and analyze its performance.
Steps to Complete:
• Choose a classification dataset (e.g., Titanic)
• Train a decision tree model
• Evaluate using accuracy, precision, recall, and confusion matrix
• Visualize the decision tree
Task 5: Implementing K-Means Clustering
Problem Statement:
Cluster data points into groups using K-Means clustering.
Steps to Complete:
• Select or generate an unlabeled dataset
• Determine optimal clusters using the elbow method
• Apply K-Means clustering
• Visualize the clusters
Task 6: Random Forest Classifier
Problem Statement:
Build a random forest classifier and tune hyperparameters for improved performance.
Steps to Complete:
• Train a random forest model on a classification dataset
• Perform hyperparameter tuning using GridSearchCV or RandomizedSearchCV
• Evaluate model accuracy and feature importance
Level 3: Advanced Projects
Task 7: Support Vector Machine (SVM) Implementation
Problem Statement:
Build an SVM classifier and optimize it for a given dataset.
Steps to Complete:
• Train an SVM on a classification dataset
• Experiment with kernel functions (linear, polynomial, RBF)
• Tune hyperparameters like C and gamma
• Evaluate and compare results
Task 8: Neural Network with TensorFlow or Keras
Problem Statement:
Build and train a simple feedforward neural network for classification tasks.
Steps to Complete:
• Set up TensorFlow/Keras environment
• Prepare the dataset (e.g., MNIST digits)
• Define a neural network architecture
• Train and evaluate the model accuracy
Task 9: Implementing Principal Component Analysis (PCA)
Problem Statement:
Reduce dimensionality of data using PCA and analyze the impact on model performance.
Steps to Complete:
• Load a high-dimensional dataset
• Apply PCA to reduce features
• Train a classifier before and after PCA
• Compare performance metrics and visualize results