Machine Learning

What you will Learn

In machine learning, you’ll grasp algorithms to analyze data, classify information, and make predictions. You’ll utilize tools like scikit-learn and TensorFlow to build models, optimizing them for real-world applications. Ultimately, you’ll leverage these skills to make informed decisions and solve complex problems across diverse industries.

Experience the Future

Join our free Machine Learning Demo and Inquiry Session to explore the world of ML. Reserve your slot now for an informative session on ML applications and techniques.

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Course content

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  1. Overview of machine learning: definition, applications, and challenges.
  2. Types of machine learning: supervised, unsupervised, and reinforcement learning.
  3. Machine learning workflow: data preprocessing, model training, and evaluation.

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  1. Introduction to regression analysis
  2. Simple linear regression Multiple linear regression
  3. Evaluation metrics for regression models
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  1. Logistic regression Decision trees and random forests
  2. Naive Bayes classifier
  3. Evaluation metrics for classification models
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  1. Logistic regression Decision trees and random forests
  2. Naive Bayes classifier
  3. Evaluation metrics for classification models
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  1. Introduction to SVM Linear SVM
  2. Nonlinear SVM using kernel functions
  3. Model evaluation and parameter tuning
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  1. Clustering algorithms: K-means, hierarchical clustering
  2. Dimensionality reduction: Principal Component Analysis (PCA)
  3. Anomaly detection techniques
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  1. Introduction to artificial neural networks
  2. Activation functions and feed forward networks
  3. Back propagation and gradient descent
  4. Deep learning architectures: convolutional neural networks (CNNs), recurrent neural networks (RNNs)
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  1. Cross-validation techniques
  2. Bias-variance tradeoff
  3. Ensemble learning: bagging and boosting Hyperparameter tuning
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  1. Precision, recall, and F1-score Receiver Operating.
  2. Characteristic (ROC) curve Area.
  3. Under the Curve (AUC) Handling imbalanced datasets.
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  1. Text preprocessing techniques Bag-of-words model
  2. Word embeddings: Word2Vec, GloVe
  3. Sentiment analysis and text classification
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  1. Text preprocessing techniques Bag-of-words model
  2. Word embeddings: Word2Vec, GloVe
  3. Sentiment analysis and text classification
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  1. Introduction to time series data
  2. Autoregressive Integrated Moving Average (ARIMA)
  3. Seasonal decomposition of time series
  4. Forecast evaluation and performance metrics
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  1. Reinforcement learning
  2. Deep reinforcement learning
  3. Machine learning in image recognition
  4. Ethical considerations and bias in machine learning

Description

Examine the fundamentals, techniques, and uses of machine learning with an emphasis on data-driven judgement. With its coverage of supervised, unsupervised, and reinforcement learning methodologies, this course gives students the tools they need to evaluate large, complicated datasets and draw insightful conclusions. Students explore real-world applications in a variety of fields, including healthcare and finance, through practical projects. Predictive analytics and optimisation techniques are revolutionising many industries.

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