Machine Learning

10000 Learners

Course Objective:

Prerequisite for 1 month: No expertise is required; however basic knowledge High School Mathematics and Statistics will be required.

Course Content:


Introduction to Machine Learning
  • What is 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
  • Uses of Machine learning in real life example
Introduction to Python
  • 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 in python
  • Functions in python
  • Positional argument, keyword argument, default argument
  • Lambda function
  • Module of Python
  • Build in and user defined module
  • Import a module, call a function of a module
Object oriented Python
  • Introduction to Object oriented language
  • Class, object
  • Create class and object in python
  • Data hiding, method overloading
  • Inheritance, method overriding
String manipulation and collections in python
  • Creating and accessing strings
  • Basic functions on strings
  • Indexing and slicing on string
  • Strings methods
  • List and its methods
  • Accessing lists
  • Tuple, set, dictionary and their methods
  • Indexing, slicing on list, tuple, set , dictionary
  • List comprehension and its uses
Introduction Numeric and Data Analysis Modules of Python
  • Introduction to Numpy
  • Creating 1D and 2D Numpy array
  • Slicing and indexing of Numpy Array
  • Operations on Numpy array
  • Introduction to Pandas
  • Series and DataFrame
  • Operations on series and dataframe
  • Different functions on dataframe
  • Pandas plotting functions
  • Read external dataset using Pandas
Handling Data – Data Wrangling
  • What is Data Wrangling
  • Prepare data for use
  • Introduction to sklearn module of python
  • Find for missing value and impute
  • Fillna() and Imputer() of sklearn
  • Concepts of categorical variable and its problem
  • Solution for categorical problem using dummy variable, LableEncoding
  • Feature Scaling and its solution using StandardScalar
Visualization library of Python
  • Using pandas plotting functions
  • Introduction to Matplotlib and Seaborn
  • Difference between the two
  • Scatter plot using matplotlib
  • Draw histogram, barchart, pie chart to any data
  • Create pairplot using seaborn
  • Concept of Box plot in seaborn
  • Drawing of 3D graphs
Supervised Machine learning – Regression & Classification
  • Explain supervised
  • Difference between classification and regression
  • Single and Multiple Linear Regression
  • Mathematics behind linear regression
  • Simple linear regression with example
  • Multiple linear regression with Boston dataset
  • R2 score and RMSE score
  • Logistic regression to solve classification problem
  • Apply logistic regression on titanic dataset
  • Classification report and confusion matrix
  • House price prediction
  • Big mart sales prediction
  • Stock market analysis
  • Loan prediction

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: 1 Month
  • Regular Batches: Suitable For: 3rd Yr / 4th Yr B.Tech. / Diploma / MCA / BCA students

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