Machine Learning using Python

3536 Learners

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.

Course Content:

Introduction to Machine Learning
  • 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
Python for Machine Learning
  • 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
Data Structures of Python
  • 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
Data Analysis and Data Visualization
  • 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
Data Wrangling
  • 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)
Supervised Regression
  • What is regression
  • Simple Linear Regression and Multiple Linear Regression
  • Regression example by Decision Tree Regressor
  • Regression using Random Forest Regressor
Supervised Learning-Classification
  • Choosing a classification algorithm
  • First steps with scikit-learn
  • Modelling 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
Unsupervised Learning
  • Unsupervised learning and clustering
  • Different types of unsupervised learning
  • K-means clustering in python
  • Hierarchical clustering
  • Difference between hierarchical and K means clustering
Introduction to Data Science and NLP
  • 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

Key Features

  • Gain skills and competencies required in Industry by Experts.
  • Work on Real-time Projects depending upon the course you select.
  • Students work in a professional corporate environment.
  • Get a globally recognized Certificate form WebTek with our partner logos.
  • Global Brand recognition for Placements.


  • Course Duration: 4 – 6 Weeks
  • Regular Batches: Online / Offline
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