Machine Learning

Transformative Design Driven Learning

Course 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 course, 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

Core java
Batch Date:

27TH APRIL 2020


₹8000.00/- ₹22,500.00/-

( ₹6,779.00 + 18% GST)


Offer Valid Till:

25th April'20 11:59 PM



Batch Dates:

4TH, 11TH, 18TH & 25TH MAY 2020


₹8000.00/- ₹22,500.00/-

( ₹6,779.00 + 18% GST)


Offer Valid Till:

25th April'20 11:59 PM



Batch Date:

1ST JUNE 2020


₹8000.00/- ₹22,500.00/-

( ₹6,779.00 + 18% GST)


Offer Valid Till:

25th May'20 11:59 PM




Check the syllabus:

Module 1: Machine Learning Basics

Lets get going: Syntax Basics!

Machine-learning algorithms find and apply patterns in data. And they pretty much run the world. But first, understand the difference between all the words that are hot in the market: Data Science, Machine Learning, Artificial Intelligence and Deep Learning, Understand the role of a data scientist and get set ready with the installation.

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

    NumPy is a fundamental Python package to efficiently practice data science. Learn to work with powerful tools in the NumPy array, and get started with data exploration. Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.

    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

    Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease. Integrate spatial data into your Python Data Science workflow and design machine learing workflows in python.

    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

    Learn how to build a logistic regression model with meaningful variables. You will also learn how to use this model to make predictions. Learn to train and assess models performing common machine learning tasks such as classification and clustering. Lastly,be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.

    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.

    CAREER PROSPECTS


    Careers in Data Science

    t’s already been almost nine long years since the famous declaration by the Harvard Business Review on Data Scientist being the “Sexiest Job of the 21st Century”. Since then, the data science field as a whole has matured in rapid ways. Notable among these developments is in the careers, from the rise of data science bootcamps to undergraduate programs 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. At the heart of data science is machine learning, analytics and statistical skills to draw meaningful insights. 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. At its heart, data science is the art of analyzing petabytes and terabytes of data in a short span of time and extracting useful information from huge volumes of data. Over the years, data scientists have successfully created new fields of knowledge such as predictive analytics which is used extensively in manufacturing, retail and healthcare and helps in streamlining operations and bringing down costs significantly.


    Companies using Data Science



    We are right here!!

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    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 our ONLINE CLASS!!

    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 course 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 course, 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 course 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 course 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.