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Machine Learning

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Machine Learning

ML

Duration
45 Hours

Course Description

              In simpler terms, machine learning enables computers to learn from data and make decisions or predictions without being explicitly programmed to do so. At its core, machine learning is all about creating and implementing algorithms that facilitate these decisions and predictions.

Course Outline For Machine Learning

1. Introduction to machine learning

  • Defining ML and its relationship to Artificial Intelligence (AI): Understanding ML as a subfield of AI focused on enabling systems to learn from data.
  • Machine learning applications across industries: Exploring how ML is used in various fields like healthcare, finance, transportation, manufacturing, education, and agriculture.
  • Types of machine learning: Delving into supervised, unsupervised, semi-supervised, and reinforcement learning, understanding the differences and when to apply each type.
  • Introduction to the machine learning workflow: Familiarizing with the stages involved in building and deploying ML models, from data collection to deployment and monitoring. 

2. Mathematical and statistical foundations

  • Linear algebra: Essential for understanding concepts like vectors, matrices, and their operations, critical for understanding neural networks.
  • Calculus: Understanding differentiation and optimization techniques, particularly for algorithms like gradient descent.
  • Probability and Statistics: Fundamentals for understanding data distributions, hypothesis testing, and model uncertainty. 

3. Data handling and preprocessing

  • Data collection methods: Gathering data from various sources like APIs, databases, and web scraping.
  • Data cleaning and preprocessing: Techniques for handling missing values, dealing with outliers, and formatting data for ML models.
  • Feature selection and engineering: Identifying and extracting relevant features from the data to improve model performance.
  • Data visualization: Tools and techniques for interpreting trends and patterns in data (e.g., Matplotlib, Seaborn, Tableau). 

4. Machine learning algorithms and models

Supervised learning algorithms:

  • Regression: Predicting continuous output values (e.g., linear regression, multiple linear regression, logistic regression).
  • Classification: Predicting categorical output values (e.g., logistic regression, decision trees, support vector machines, naive Bayes).

Unsupervised learning algorithms:

  • Clustering: Grouping similar data points together (e.g., K-means clustering, hierarchical clustering, DBSCAN).
  • Dimensionality Reduction: Reducing the number of features in a dataset while retaining essential information (e.g., Principal Component Analysis).
  • Reinforcement learning: Developing agents that learn through interaction with an environment, maximizing rewards through trial and error.
  • Neural networks: Building blocks of deep learning, inspired by the structure and function of the human brain. 

5. Model evaluation and selection

  • Evaluation metrics: Understanding and applying various metrics to assess model performance (e.g., accuracy, precision, recall, F1-score, confusion matrix for classification; MSE, RMSE, R-squared for regression).
  • Cross-validation: Techniques for validating model performance, minimizing bias and overfitting.
  • Bias-variance tradeoff: Understanding the balance between bias and variance to optimize model performance. 

6. Advanced topics (depending on course level)

  • Deep learning and neural networks: Advanced architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for image and sequential data processing.
  • Natural Language Processing (NLP): Techniques for processing and analyzing human language (e.g., sentiment analysis, chatbots, language models like BERT, GPT).
  • Computer Vision: Enabling computers to interpret and understand visual information (e.g., image classification, object detection).
  • Time Series Analysis: Methods for analyzing and forecasting data points collected over time.
  • Deployment and scalability: Strategies for deploying ML models into production and working with large-scale datasets and cloud platforms (e.g., Apache Spark, Hadoop, AWS, Google Cloud).
  • MLOps: Managing the complete lifecycle of ML systems, including deployment, monitoring, and automation. 

7. Practical applications and projects

  • Real-world case studies: Analyzing how machine learning is applied in various industries to solve practical challenges.

Hands-on projects: Developing and implementing ML models using programming languages like Python and libraries/frameworks such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. 

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