تفاصيل الدورة

Introduction to Data Science
  • What is data science and why is it so important?
  • Applications of data science
  • Various data science tools
  • Data Science project methodology
  • Tool of choice-Python: what & why?
  • Case study
  • Introduction to Python
  • Installation of Python framework and packages: Anaconda & pip
  • Writing/Running python programs using Spyder Command Prompt
  • Working with Jupyter notebooks
  • Creating Python variables
  • Numeric , string and logical operations
  • Data containers : Lists , Dictionaries, Tuples & sets
  • Practice assignment
  • Iterative Operations & Functions in Python
  • Writing for loops in Python
  • While loops and conditional blocks
  • List/Dictionary comprehensions with loops
  • Writing your own functions in Python
  • Writing your own classes and functions
  • Practice assignment
  • Data summary & visualization in Python
  • Need for data summary & visualization
  •  Summarizing numeric data in pandas
  • Summarizing categorical data
  • Group wise summary of mixed data
  • Basics of visualization with ggplot & Sea born
  • Inferential visualization with Sea born
  • Visual summary of different data combinations
  • Practice assignment
  • Data Handling in Python using NumPy & Pandas
  •  Introduction to NumPy arrays, functions & properties
  • Introduction to Pandas & data frames
  • Importing and exporting external data in Python
  • Feature engineering using Python
  • Machine Learning in Python 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 generalization capabilities
  • Drivers of machine learning algorithms
  • Cost functions
  • Brief introduction to gradient descent
  • Importance of model validation
  • Methods of model validation
  • Cross validation & average error
  • Generalized Linear Models in Python
  • Linear Regression
  • Regularization of Generalized Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Methods of threshold determination and performance measures for classification score models
  • Case Study
  • 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 Extra Trees (Extremely Randomized Trees)
  • Partial dependence plots
  • Case Study & Assignment
  • Support Vector Machines (SVM) & kNN in Python
  • Introduction to idea of observation based learning
  • Distances and similarities
  •  k Nearest Neighbors (kNN) for classification
  • Brief mathematical background on SVM/li>
  • Regression with kNN & SVM
  • Case Study
  • Unsupervised learning in Python
  • Need for dimensionality reduction
  • Principal Component Analysis (PCA)
  • Difference between PCAs and Latent Factors
  • Factor Analysis
  • Hierarchical, K-means & DBSCAN Clustering
  • Case study
  • Artificial Neural Networks in Python
  • Introduction to Neural Networks
  • Single layer neural network
  • Multiple layer Neural network
  • Back propagation Algorithm
  • Neural Networks Implementation in Python
  • Case study
تحديث بتاريخ 20 July, 2020

المتطلبات

You should be comfortable with Python, including functions, control flow, lists, and loops.

وظائف مناسبة لهذه الدورة

Machine Learning Engineer , Junior Data Sciëntist , Python Developer

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