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Machine Learning From beginner to advanced

Track :

Programming

Lessons no : 107

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What will you learn in this course?
  • Develop foundational understanding of machine learning algorithms and concepts for practical AI applications
  • Implement supervised, unsupervised, and reinforcement learning techniques for real-world data analysis
  • Apply data preprocessing, feature engineering, and model selection to improve machine learning model accuracy
  • Evaluate machine learning models using metrics like accuracy, precision, recall, and F1 score for optimal performance
  • Utilize Python libraries such as scikit-learn, TensorFlow, and Keras for building machine learning models
  • Design and deploy machine learning solutions for business problems in finance, healthcare, and marketing
  • Analyze large datasets to identify patterns, trends, and insights using advanced machine learning methods
  • Optimize hyperparameters and tune models to enhance predictive accuracy and robustness
  • Integrate machine learning models into applications and systems for automation and decision-making
  • Understand ethical considerations and biases in machine learning to ensure fair and responsible AI deployment
  • Develop deep learning models, including neural networks, for complex pattern recognition tasks
  • Implement natural language processing (NLP) techniques for text analysis and language understanding
  • Apply unsupervised learning methods like clustering and dimensionality reduction for data segmentation
  • Create predictive models for forecasting and trend analysis in various industries
  • Utilize transfer learning and model fine-tuning to accelerate machine learning projects
  • Assess the scalability and deployment strategies for machine learning models in production environments

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Lessons | 107
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Artificial intelligence is a technology that enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly .