Data Science with Python Online Training

Data Science with Python Online Training

machine-learning-with-python-online-training-nareshit

About Data Science with Python Online Training

Python is powerful programming language used for harvesting from the huge volume. Python is an open source language. It is developed with Data Science tool which is used to simplify storing of data. Manipulating of data and also, helps in analysis of data. By using Python it creates a wonderful visualization. By using Python it helps to access to high-quality content. This course will also introduce data manipulation and cleaning of data using Python. Also it introduces the abstraction of the data frame as a central data structure.

Course Objectives

What are the Course Objectives?

After completion of the Data Science with Python Course at Naresh I technologies, you will be expertise and eligible for:

  • Complete knowledge of Data Science
  • Able to analysis Bigdata
  • Able to work in Data Mining
  • Able to work on statistic
  • Learn how to use different tool like Tableau, Map Reduce
  • Creation of decision tree
  • Explore Big data concept

Who should go for this course

  • Any IT experienced Professional who are interested to build their career in development/ data scientist.
  • Any B.E/ B.Tech/ BSC/ MCA/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
  • Fresh Graduates.

Pre-requisites

The course can learn by any IT professional having basic knowledge of:

  • Mathematics
  • Statistics
  • Any Programming Language

Course Curriculum

  • 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?
  • 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
  • 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
  • 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
  • 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
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

Generalized 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 Python

  • Need for dimensionality reduction
  • Principal Component Analysis (PCA)
  • Difference between PCAs and Latent Factors
  • Factor Analysis
  • Hierarchical, K-means & DBSCAN Clustering

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

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

Version Control using Git and Interactive Data Products

  • Need and Importance of Version Control
  • Setting 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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e-Learning Sessions

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