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Machine Learning using Python

Course Objective:

On completion of the training, participants will be able to do data analysis using Python. Audience will be able to identify between classification and regression problem in real life data. Participants will be able to do some real life prediction like email spam filtering, stock market price prediction, loan prediction, crime data analysis etc using some machine learning tools in python.


No experience is required; however basic understanding of Python and some Basic Mathematics is highly required.

Course Content:

Lesson: 1
  • The three different types of machine learning
  • Supervised, unsupervised, reinforcement learning
  • An introduction to the basic terminology and notations
  • A roadmap for building machine learning systems
  • Using Python for machine learning
Lesson: 2
  • Introduction to Python language
  • Environmental setup for Python, Anaconda, Jupyter, Spider etc
  • Data types of Python, numbers, string
  • Run a python program in jupyter/spider/ipython
  • if, elif, loops and function in python
  • Module, exception of Python
Lesson: 3
  • Different types of Data structure list, tuple, dictionary, set etc
  • What is List comprehension
  • Different functions of List, Set, Dictionary, Tuple
  • push and pop on Set
  • Difference between different data structures
Lesson: 4
  • Introduction to Numpy and Panda
  • Creating Numpy array and its different operations
  • Slicing and indexing of Numpy Array
  • Introduction to Panda and Data frame
  • Programs using Panda Data frame
  • Data visualization using Matplotlib
Lesson: 5
  • Dealing with missing data
  • Handling categorical data
  • Partitioning a dataset in training and test sets
  • Selecting meaningful features
  • Reduce dimensionality using PCA (Principal Component Analysis)
Lesson: 6
  • What is regression
  • Simple Linear Regression and Multiple Linear Regression
  • Regression example by Decision Tree Regressor
  • Regression using Random Forest Regressor
Lesson: 7
  • Choosing a classification algorithm
  • First steps with scikit-learn
  • Modeling class probabilities via logistic regression
  • Solving nonlinear problems using a kernel SVM
  • Decision Tree and Random Forest Classifier
  • K-Nearest Neighbors – a lazy learning algorithm
  • Text based classification using Naïve Bayes algorithm
Lesson: 8
  • Unsupervised learning and clustering
  • Different types of unsupervised learning
  • K-means clustering in python
  • Hierarchical clustering
  • Difference between hierarchical and K means clustering
Lesson: 9
  • Data Science overview
  • Data Analytics overview
  • Statistical Analysis and Business Application
  • Introduction to Natural language processing using Scikit learn
  • Uses of Tensor Flow and Open CV in data science problem


  • Course Duration: 4 – 6 Weeks
  • Suitable For: 3rd Yr / 4th Yr B.Tech. / Diploma / MCA / BCA students
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