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What You will Get In

Internship Oriented Training on Machine Learning

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  • Get access of 12 lessons training on GIT
  • Get access of 25 lessons training on Python
  • Get access of 48 lessons training on Machine Learning
  • Assignments
  • Internship (No limit of Projects, You can complete number of projects during your internship)
  • Letter of Recommendation
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Placement Assistance:
  • Placement assistance to all students who successfully completed all modules.
  • Get updates on various companies vacancies, opportunities, placement drives.
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What You will Get In

Why Internship Oriented Trainings?

As per previous experience from companies they found that many students want to join Internship but they didn’t have enough knowledge to work on live projects of company or they lack in some particular fields. This Internship Oriented Training is a bridge of knowledge between companies and students, so that companies get more skilled and qualified students for internship. In this Internship Oriented training we are not only cover particular technology but we also covers from installation of software that needed to all topics needed for internship.

GIT / GitHub

Get access of 12 lessons training on GIT

Python

Get access of 25 lessons training on Python

Machine Learning

Get access of 48 lessons training on Machine Learning

Internship

Get Internship After completion of all Trainings
Get Training Course and Internship

Certificate

Get Certificates for Each Course You Completed and Internship Completion Certificate

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Training Syllabus:
GIT/GITHub
  • Introductions
  • How to create user in Git
  • Git Workflow
  • How to use git stage and commit commands
  • How to use and Manage Git ignore command
  • How To Use Rename and Remove Commands in Git
  • How To Skip staging in GIT
  • How To Unmodifying and Unstaging In GIT
  • Creating Account on GitHub | How to Singup on GitHub
  • How To Cloning Remote Repository in Github
  • How To Create Branch In Git | Git Branching
  • How To Merge Branches In GIT
 
Python
  • How to install python on windows 7/8.1/10
  • How to Install PyCharm IDE on Windows Machine
  • How to install Anaconda in windows 7/8.1/10
  • How to Launch Jupyter Notebook Using Anaconda
  • Introduction of python
  • Python Basic Concepts
  • Variables in Python
  • Python Strings
  • List in Python
  • Python Dictionary Methods
  • Tuple In Python
  • How To Use Set In Python
  • How To Use Conditional Expressions in Python
  • How To Use While Loop in Python
  • How To Use For Loop In Python
  • Try And Except In Python Function
  • Read, Readline and Readlines In Python
  • Python Functions
  • Lambda Functions
  • Join(), Map(), Filter() in Python
  • Python Decorators
  • Enumerate In Python
  • Fstring In Python
  • OOPS Concepts in python

Python Usecases- Creating Python Mini Project

Machine Learning
  • How to install python on windows 7/8.1/10
  • How to install Anaconda in windows 7/8.1/10
  • How to Launch Jupyter Notebook Using Anaconda
  • How To Import Libraries and Dataset for Linear Regression
  • Distribution Plot For Linear Regression
  • How To Use Label Encoder and Heatmap
  • How To Use Train Test and Split For Linear Regression
  • Linear Regression Model Creation and Model Training
  • Linear Regression Model Evaluation
  • How To Import libraries and dataset for Logistic Regression
  • Imputing Null Values
  • Plotting For Logistic Regression
  • How To Do The Pre-Processing For Logistic Regression
  • How To Use Train, Test and Split For Logistic Regression
  • How To Create Logistic Model and Model Evaluation
  • How To Import Libraries and Dataset
  • EDA (Exploratory Data Analysis) and Count Plot
  • How To Do The Pre-Processing
  • How To Use Train, Test and Split
  • How to Create Logistic Model and Logistic Model Evaluation
  • Decision Tree Entropy Model
  • Decision Tree Gini Model
  • Random Forest Model

 

  • How To Import Libraries and Dataset
  • Distribution Plot and EDA for Naive Bayes
  • How To Use Train, Test, Split and Logistic Model
  • Naive Bayes Model
  • How To Import Libraries and Dataset
  • Plotting and EDA For K-Means Cluster
  • Finding K and Elbow method for K-means Cluster
  • How Get Cluster Centers and Plotting
  • How To Import Libraries and Dataset
  • Distribution Plot and EDA For KNN
  • How To Use Train, Test and Split
  • How To Create KNN Model and Model Evaluation
  • How To Find K Optimum for KNN
  • How To Import Libraries, Dataset and Pre-Processing
  • Distribution Plot and EDA For SVM
  • How To Use Train, Test and Split the dataset
  • How to Create Logistic Model and Logistic Model Evaluation
  • Creating a Support Vector Machine and Model Evaluation
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