About this course
Machine Learning Internship Program: 6-Week Structured Learning and Experience
This machine learning internship program provides a structured approach to understanding key ML concepts and hands-on experience over six weeks. Interns will gain insights into data preprocessing, model building, and deployment.
Program Highlights
Week 1: Introduction to Python & Data Preprocessing
· Write a Python script demonstrating the use of NumPy, Pandas, and Matplotlib for basic data operations.
· Load a dataset, handle missing values, and normalize numerical features.
Week 2: Exploratory Data Analysis & Feature Engineering
· Perform data visualization on a given dataset using Pandas and Matplotlib.
· Read and process data from CSV, JSON, and an API.
· Select relevant features and create new ones to improve model performance.
Week 3: Regression & Classification Models
· Build and evaluate a simple linear regression model for predicting house prices.
· Implement logistic regression to classify emails as spam or not spam.
· Use a decision tree to classify passengers from the Titanic dataset.
· Implement KNN for handwritten digit recognition using the MNIST dataset.
Week 4: Advanced Machine Learning Models
· Train an SVM model to classify iris flowers based on their features.
· Apply K-Means to segment customers based on their purchasing behavior.
· Perform hierarchical clustering on a dataset and interpret the dendrogram.
· Reduce dimensions of a dataset and visualize the first two principal components.
Week 5: Specialized ML Topics
· Analyze stock prices over time and predict the next 30 days using moving averages.
· Perform sentiment analysis on a dataset of movie reviews.
· Develop a basic book recommendation system using cosine similarity.
· Use techniques like oversampling and undersampling to balance an imbalanced dataset.
Week 6: Model Optimization, Deployment & Final Project
· Optimize the parameters of a classification model using GridSearchCV.
· Develop a simple web app to make predictions using a trained ML model.
· Implement a complete ML pipeline from data collection to model deployment.
Expected Outcomes
By the end of the program, interns will:
· Gain hands-on experience with ML models and techniques.
· Understand data preprocessing and model evaluation.
· Be able to deploy ML models using Flask.
· Develop an end-to-end ML project showcasing their skills.
References
Requirements
Laptop
Internet Connection
Jupyter Notebook
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To understand the fundamental role of Python in machine learning by exploring essential libraries like NumPy, Pandas, and Matplotlib.
To understand how to clean, preprocess, and prepare raw data for machine learning models.
To explore and visualize dataset features to uncover patterns, trends, and outliers.
To learn how to read and process data from multiple sources such as CSV, JSON, and APIs for machine learning.
To understand how to select relevant features and create new features that improve model performance.
To understand and apply linear regression to predict continuous numerical values.
To understand and apply logistic regression for binary classification.
To build a decision tree for classification problems and interpret the results.
To apply the KNN algorithm for pattern recognition tasks such as handwritten digit recognition.
To implement a Support Vector Machine (SVM) classifier for solving a classification problem, such as Iris flower classification.
To apply K-Means clustering for grouping customers based on purchasing behavior.
To apply hierarchical clustering to analyze relationships between data points.
To apply Principal Component Analysis (PCA) for dimensionality reduction in high-dimensional datasets.
To analyze and forecast time-dependent data, such as stock prices or temperature trends.
To classify text data as positive, negative, or neutral sentiment using Natural Language Processing (NLP).
To build a book or movie recommendation system using collaborative filtering.
To learn and apply different techniques to handle imbalanced datasets in classification problems.
To optimize machine learning models by tuning hyperparameters using GridSearchCV and RandomizedSearchCV.
To create a web application that allows users to input data and get ML model predictions using Flask.
To build and present a complete ML project from data collection to deployment.
