Machine Learning with Python
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.
About this course
Data science in the real world often involves the management of data flows for a specific purpose - the modeling of some hypothesis. Machine learning is the art of training models by using existing data along with a statistical method to create a parametric representation that fits the data. In other words, a machine learning algorithm uses statistical processes to learn from examples and then applies what it has learned to future inputs to predict an outcome. These models can be used in data products as engines to create more data and actionable results.Machine learning can classically be summarized with two methodologies - supervised and unsupervised learning.In supervised learning, the “correct answers” are annotated ahead of time and the algorithm tries to fit a decision space based on those answers.In unsupervised learning, algorithms try to group like examples together, inferring similarities usually via distance metrics.These learning types allow us to explore data and categorize them in a meaningful way, predicting where new data will fit into our models.
This course is designed for individuals and organizations requiring:
- Anyone willing and interested to learn machine learning algorithm with Python
- Any one who has a deep interest in the practical application of machine learning to real world problems
- Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms
- Any intermediate to advanced EXCEL users who is unable to work with large datasets
- Anyone interested to present their findings in a professional and convincing manner
- Anyone who wishes to start or transit into a career as a data scientist
- Anyone who wants to apply machine learning to their domain
- Basic Python programming knowledge is necessary
- Good understanding of linear algebra
This course will cover the following topics:
- An introduction to machine learningLoading datasets
- Building models and model persistence
- Feature extraction from data sets
- Model selection and evaluation
- Building machine learning pipelines
- Set up a Python development environment correctly
- Gain complete machine learning tool sets to tackle most real world problems
- Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
- Combine multiple models with by bagging, boosting or stacking
- Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
- Develop in Jupyter (IPython) notebook, Spyder and various IDE
- Communicate visually and effectively with Matplotlib and Seaborn
- Engineer new features to improve algorithm predictions
- Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data
- Use SVM for handwriting recognition, and classification problems in generalUse decision trees to predict staff attritionApply the association rule to retail shopping datasets
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