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Machine Learning In Python

Python features a dynamic type system and automatic memory management. It supports multiple programming paradigms, including object-oriented, imperative, functional and procedural. It also has a comprehensive standard library. Python interpreters are available for many operating systems. CPython, the reference implementation of Python, is open source software and has a community-based development model, as do nearly all of Python's other implementations. Python and CPython are managed by the non-profit Python Software Foundation.

Download python Machine Learning syllabus   Machine Learning Basics
  • Converting business problems to data problems
  • Understanding supervised and unsupervised learning with examples
  • Understanding biases associated with any machine learning algorithm
  • Ways of reducing bias and increasing generalisation capabilites
  • Drivers of machine learning algorithms
  • Cost functions
  • Brief introduction to gradient descent
  • Importance of model validation
  • Methods of model validation
  • Cross validation & average error
  Generalised Linear Models in Python
  • Linear Regression
  • Regularisation of Generalised Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Methods of threshold determination and performance measures for classification score models
  Tree Models using Python
  • Introduction to decision trees
  • Tuning tree size with cross validation
  • Introduction to bagging algorithm
  • Random Forests
  • Grid search and randomized grid search
  • ExtraTrees (Extremely Randomised Trees)
  • Partial dependence plots
  Boosting Algorithms using Python
  • Concept of weak learners
  • Introduction to boosting algorithms
  • Adaptive Boosting
  • Extreme Gradient Boosting (XGBoost)
  Support Vector Machines (SVM) & kNN in Python
  • Introduction to idea of observation based learning
  • Distances and similarities
  • k Nearest Neighbours (kNN) for classification
  • Brief mathematical background on SVM/li>
  • Regression with kNN & SVM
  Unsupervised learning in PythonS
  • Need for dimensionality reduction
  • Principal Component Analysis (PCA)
  • Difference between PCAs and Latent Factors
  • Factor Analysis
  • Hierarchical, K-means & DBSCAN Clustering
  Text Mining in Python
  • Gathering text data using web scraping with urllib
  • Processing raw web data with BeautifulSoup
  • Interacting with Google search using urllib with custom user agent
  • Collecting twitter data with Twitter API
  • Naive Bayes Algorithm
  • Feature Engineering with text data
  • Sentiment analysis
  Open CV
  • Basic of Computer Vision & Open CV
  • Images Manipulations
  • Image Segmentation
  • Object Detection
  • Face, People and Car Detection
  • Face Analysis and Fulters
  • Machine Learning in Computer Vision
  • Motion Analysis & Object Tracking
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