Machine Learning Online Internship

Transformative Design Driven Learning

Internship Description

Over the past decade, the field of data science has grown exponentially — both in the breadth of applications it encompasses, as well as in the depth at which aspiring data scientists are expected to understand its core concepts. In this Internship, we’ve selected the important lessons that provide a useful roadmap to break into data science, as well as inspiring anecdotes that describe how successful data scientists first entered the field and built their careers.


Program Highlights:

30 Hours

Time to complete

Python Basics

Pre-requisites

15+

Assignments

Students of Our Summer Internships


Batch Date:

1st, 21st June


₹10,000.00/- ₹22,500.00/-

( ₹8,500.00 + 18% GST)


Offer Valid Till:

19th June'20 11:59 PM



Batch Dates:

1st & 20th July 2020


₹10,000.00/- ₹22,500.00/-

( ₹8,500.00 + 18% GST)


Offer Valid Till:

30th July'20 11:59 PM



Batch Date:

1st Aug


₹10,000.00/- ₹22,500.00/-

( ₹8,500.00 + 18% GST)


Offer Valid Till:

30th July'20 11:59 PM




You'll work on:

Module 1: Machine Learning Basics

Lets get going: Syntax Basics!

You'll learn:

Introduction to Malchine Learning

  • Anaconda Installation
  • What is Data Science
  • What is Machine Learning
  • What is Artificial Intelligence
  • What is Deep Learning
  • Role of Data Scientist
  • Applications of Data Science
  • Data and its sources
  • Overview of Data Science Life Cycle
  • Downloading and installing Anaconda
  • Starting Jupyter Notebook

  • Jupyter Notebook

  • UI elements of Notebook
  • Kernel and types of cells - Code and Markdown
  • Modes - Edit and Command
  • Magic functions - Line and Cell functions
  • Keyboard shortcuts - Command mode and Edit mode shortcuts
  • Saving and loading of notebook
  • Using Jupyter Lab
  • Module 2: Stats, NumPy and Pandas

    You'll learn

    Statistics

  • Mean, Median, Mode and Range
  • Variance and Standard Deviation
  • Quartiles and IQR
  • Scatter Plot, Bar Graph, Histogram, Pie, Box plot
  • Measuring Skewness
  • Probability
  • Regression Analysis
  • Using statistics and scipy.stats libraries to apply Linear Regression

  • NumPy

  • Creating single and multi-dimensional arrays
  • Using fancy indexing and slicing
  • Array operations, methods of ndarray and universal functions
  • View vs. Copy of array
  • Reshaping arrays
  • Stacking and splitting arrays
  • Applying Linear Algebra
  • Image processing with Arrays

  • Pandas

  • Working with Series
  • Applying methods on Series
  • Working with DataFrame
  • Reading data into DataFrame and writing DataFrame to other formats
  • Selecting rows and columns in DataFrame
  • Adding and deleting rows and columns in DataFrame
  • Working with apply() and applymap() functions
  • Working with str attribute for string manipulations
  • Joining, Merging and Concatenating DataFrames
  • Grouping data on one or more columns
  • Data Wrangling - Binning, Encoding etc.
  • Handling null values
  • Module 3:Matplotlib, DataScience and Machine Learning Workflow

    You'll learn:

    Matplotlib Seaborn

  • Anatomy of a figure
  • Working with Module API and Object API
  • Working with different plots - Histogram, Bar, Stacked Bar, Pie, Scatter, Line
  • Creating multiple axes in single figure
  • Customizing plots
  • Figure-level vs. axes level plots
  • Categorical, Relational, Distribution, Regression and Matrix Plots

  • DataScience Workflow

  • What is the problem to solve
  • Data Acquisition
  • Preparing data - cleaning and organizing data
  • Exploratory Data Analysis (EDA)
  • Data Munging/Data Wrangling
  • Feature Engineering
  • Data Visualization
  • Module 4: Machinelearning flow, Regression, Classification and Unsupervised Machine Learning

    You'll learn:

    Machine Learning Workflow Working with Classificaiton Case Study

  • Understanding pre-processing concepts like Standardization, Encoding etc
  • Understanding Regularization - Lasso and Ridge
  • Using different algorithms like Logistic Regression, Naive Bayes, Decision Tree etc. using Scikit-learn
  • Understanding Gradient Decent and XGBoost
  • Training

  • Working with Regression case study

  • Evaluating result of the model using metrics - classification report, confusion matrix
  • Understanding cross validation and how to use it
  • Using Grid Search to select right hyper parameters
  • Presenting the model - Deployment

  • Unsupervised Machine Learning

  • What is clustering
  • How k-Means clustering works
  • How hierarchical clustering works
  • Recommender systems - Collaborative filtering and Content-based filtering
  • CASE STUDIES

    Graduate Admission Analysis

    Objective

    400 applicants have been surveyed as potential students for a university. The university weighs certain aspects of a student's education to determine their acceptance. The objective is to explore what kind of data is provided, determine the most important factors that contribute to a student's chance of admission, and select the most accurate model to predict the probability of admission.


    Loan Approval Prediction

    Objective

    Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.


    Car Price Prediction

    Objective

    You are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market.



    PAYMENT OPTION

    Find a plan that works the most for you:


    Option 1: Self Paced

    ₹ 5,000, month

    • Pre-recorded videos
    • Course Completion Certificate
    • 1 major project
    • Internship Certificate on successful project completion
    • 2 hours of project briefing(via Telegram)
    • 8+ hours of live mentorship (via Telegram)
    • Live project exposure
    • Real time training
    • Pre placement opportunities

    Option 2: Mentor Led

    ₹ 10,000, month

    • Course Completion Certificate
    • 1 major + 1 minor project
    • Internship Certificate on successful project completion
    • 60+ hours of live mentorship (via Telegram)
    • Live project exposure
    • Real time training
    • Pre placement opportunities

    Option 3: Complete ML

    ₹ 30,000, month

    • Course Completion Certificate
    • 2 major + 2 minor project
    • Internship Certificate on successful project completion
    • 90+ hours of live mentorship (via Skype)
    • Live project exposure
    • 1-on-1 Real time training
    • Pre placement opportunities


    CAREER PROSPECTS

    Careers in Data Science

    Data science is one of the hottest fields in Information technology in present scenario. A career in data science requires a thorough understanding of mathematics and statistics. Some of the most commonly asked for skills in data scientist are — R, Python, Hadoop, Apache Spark, Scala, machine learning among others. Data scientists are also skilled in knowledge of different data mining techniques such as regression, clustering, decision trees and support vector machines.

    Companies using Data Science



    We are right here!!

    Need more information?? Fill up our form super quick or shoot us a message on whatsapp and we'll get back to you!

    ADMISSION & PROGRAM DETAILS

    Make the Jump

    When we say we build a community, we genuinely do. We dont just select an individual student but rather cultivate a group of diverse and unique people with passion for technology.

    Admission Process:


    • 1. Register yourself HERE

    • 2. Complete Technical Application and pass a Code Assessment.
    • 3. Deposit your fee and get going with the online internship!!

    Methodology:

    Online pre-recorded classes with 24*7 guidance by mentors with subject expertise

    Project based (Pre-Req: Personal Laptop to work on)


    Frequently Asked Questions


    We offer this Internship in “Live Instructor-Led Online Training” mode. Through this way you won’t mess up anything in your real-life schedule. Live meeting access link will be shared before your session starts. Online training is live and the instructor's screen will be visible and voice will be audible. Your screen will also be visible to the instructor and you can ask queries during the live session.
    Participants will be provided "Machine-learning"-specific study material, our public GitHub repository and the study material will also be shared with the participants.
    This is a 2 week Internship, total of 30 hours wherein each week will cover 10 hours.
    Our Subject matter experts (SMEs) have more than fourteen years of industry experience. This ensures that the learning program is a 360-degree holistic knowledge and learning experience. The Internship program has been designed in close collaboration with the experts.
    We have TA's who are available in different time slots to resolve all your doubts. You can also interact with faculty through Skype
    Previous experience with programming, preferably using an object-oriented language like Java, Python, is very helpful. The Internship does cover a few basic programming concepts to make sure everyone has the same level of background knowledge but individuals who have never programmed may find it a bit too fast- paced. So, it is advisable to do some reading before you start.