MACHINE LEARNING with PYTHON

Learn how to use Data Science with Python from beginner level to advanced techniques which are taught by experienced working professionals. With our Data Science with Python Training in Chennai, you'll learn concepts at an expert level in a practical manner. Get enrolled for the most demanding skill in the world.

MACHINE LEARNING with PYTHON Exams & Certification

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SYLLABUS

  • What is data science & explain its importance?
  • Applications of data science
  • Various data science tools
  • Data Science project methodology
  • Tool of choice-Python: what & why?
  • Case study
  • Installation of Python framework and its packages like Anaconda & pip
  • Writing/Running python programs using Spyder Command Prompt
  • Working with Jupyter notebooks
  • Creating Python variables
  • Numeric , string and logical operations
  • Data containers including Lists , Dictionaries, Tuples & sets
  • Practice assignment
  • Writing for loops in Python
  • While loops and conditional blocks
  • List/Dictionary comprehensions with loop
  • Writing your own functions in Python
  • Writing your own classes and functions
  • Practice assignment
  • Need for data summary & visualization
  • Summarising numeric data in pandas
  • Summarising categorical data
  • Group wise summary of mixed data
  • Basics of visualisation with ggplot & Seaborn
  • Inferential visualisation with Seaborn
  • Visual summary of different data combinations
  • Practice assignment
  • Introduction to NumPy arrays, functions & properties
  • Introduction to Pandas & data frames
  • Importing and exporting external data in Python
  • Feature engineering using Python
  • Linear Regression
  • Regularisation of Generalised Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Threshold determination methods & performance measures for classification score models
  • Case Study
  • 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
  • Case Study & Assignment
  • Concept of weak learners
  • Introduction to boosting algorithms
  • Adaptive Boosting
  • Extreme Gradient Boosting (XGBoost)
  • Case Study & assignment
  • Converting business problems to data problems
  • Understanding supervised and unsupervised learning with examples
  • Understanding biases which associated with any machine learning algorithm
  • Ways of reducing bias & increasing with 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
  • 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
  • Case Study
  • Need for dimensionality reduction
  • Principal Component Analysis (PCA)
  • Difference between PCAs and Latent Factors
  • Factor Analysis
  • Hierarchical, K-means & DBSCAN Clustering
  • Case study
  • Text Mining in Python
  • Gathering text data by using web scraping with urllib
  • Processing raw web data with BeautifulSoup
  • Interacting with Google search by using urllib with custom user agent
  • Collecting twitter data with Twitter API
  • Naive Bayes Algorithm
  • Feature Engineering with text data
  • Sentiment analysis
  • Case study
  • Need and Importance of Version Control
  • Set up git and github accounts on local machine
  • Creating and uploading GitHub Repos
  • Push and pull requests with GitHub App
  • Merging and forking projects
  • Introduction to Bokeh charts and plotting
  • Examples of static and interactive data products

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Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.