Machine Learning Projects Using Python

Machine Learning Projects Using Python

This course provides hands-on experience in building machine learning models using Python. Students will learn the core concepts of machine learning, including data preparation, feature engineering, model selection, and model evaluation. They will also explore advanced topics such as natural language processing, computer vision, and deep learning. By the end of the course, students will have a strong foundation in machine learning and the skills necessary to apply machine learning techniques to real-world problems.

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Machine Learning Projects Using Python

1) Introduction:

• Overview of machine learning concepts and Python libraries for ML.
• 2) Data Preprocessing:
• * Importing and cleaning data.
• * Feature engineering and feature scaling.
• * Train test data split.
• 3) Supervised Learning:
• * Linear regression for continuous target variables.
• * Logistic regression for binary classification.
• * Decision trees for both regression and classification.
• * Support vector machines for classification.
• 4) Unsupervised Learning:
• * K means clustering for data exploration and grouping.
• * Principal component analysis for dimensionality reduction.
• 5) Time Series Forecasting:
• * ARIMA models for time series prediction.
• * LSTM neural networks for sequential data analysis.
• 6) Natural Language Processing:
• * Text preprocessing and feature extraction.
• * Bag of words model and TF IDF for text representation.
• * Naive Bayes and SVM for text classification.
• 7) Image Recognition:
• * Loading and preprocessing images.
• * Convolutional neural networks for image classification and object detection.
• 8) Object Detection:
• * Using pre trained models like YOLO or Faster R CNN.
• * Training custom object detection models.
• 9) Natural Language Generation:
• * Using Transformers and GPT 3 for text generation.
• * Creating chatbots and dialogue systems.
• 10) Model Evaluation:
• * Evaluating model performance using accuracy, precision, recall, and F1 score.
• * Hyperparameter tuning for model optimization.
• Training Program for Students:
• The training program should cover the following:
• * Hands on implementation of all the projects listed above.
• * Explanation of the underlying theory and algorithms.
• * Guidance on project design and implementation.
• * Real world case studies and applications.
• * Mentorship and support from experienced machine learning practitioners.

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This information is sourced from JustAcademy

Contact Info:

Roshan Chaturvedi

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