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Intermediate ML Projects

Intermediate projects move beyond basic Scikit-Learn pipelines. At this level, you will deal with unstructured data (text and images) and temporal data, requiring more sophisticated feature engineering and deep learning frameworks.

Project 1: Sentiment Analysis on Movie Reviews (NLP)​

Goal: Classify a text review as positive or negative using natural language processing.

Project Overview​

This project introduces the challenges of turning text into numbers. You will explore word importance and sequence.

  • Dataset: IMDb Movie Reviews.
  • Key Techniques: TF-IDF Vectorization, Word Embeddings, or BERT.
  • Algorithm: XGBoost or a simple RNN/LSTM.

Challenges​

  1. Text Cleaning: Removing HTML tags, emojis, and stopwords.
  2. Sparsity: Managing high-dimensional data created by large vocabularies.
  3. Context: Moving from "Bag of Words" (ignoring order) to "Word Sequences" (preserving context).

Project 2: Digit Recognition (Computer Vision)​

Goal: Correctly identify handwritten digits (0-9) from grayscale images.

Project Overview​

This is the entry point into Deep Learning. You will move from flat feature vectors to spatial data processing.

  • Dataset: MNIST Database.
  • Key Algorithm: Convolutional Neural Networks (CNN).
  • Framework: TensorFlow/Keras or PyTorch.

Implementation Steps​

  1. Reshaping: Convert image arrays into a format compatible with CNNs (Height, Width, Channels).
  2. Normalization: Scale pixel values from [0, 255] to [0, 1].
  3. Architecture: Build a model with Conv2D, MaxPooling, and Dropout layers to prevent overfitting.

Project 3: Stock Price or Weather Forecasting (Time-Series)​

Goal: Predict future values based on historical sequential data.

Project Overview​

Time-series data is unique because the order of data points matters. You will learn to handle "autocorrelation."

  • Dataset: Yahoo Finance (Stock) or NOAA (Weather).
  • Key Algorithm: Prophet (by Meta), ARIMA, or LSTMs.
  • Primary Metric: Root Mean Squared Error (RMSE).

Key Concepts​

  1. Stationarity: Checking if the mean and variance change over time.
  2. Windowing: Creating "Sliding Windows" where the previous NN days are used to predict the next day.
  3. Seasonality: Identifying repeating patterns (e.g., higher sales during holidays).

Intermediate Project Workflow​

At this stage, your workflow includes an "Feature Engineering" and "Architecture Design" phase.

  • Frameworks: PyTorch or TensorFlow.
  • Boosting: XGBoost, LightGBM, or CatBoost.
  • NLP Tools: Hugging Face Transformers, Spacy.
  • Hardware: Access to GPUs (Google Colab or Kaggle Kernels).

References​

Intermediate projects transition you from a "user" of libraries to a "builder" of architectures. Are you ready to dive into the cutting edge of AI?