BECOME AN EXPERT IN MACHINE LEARNING IN
12 WEEKS

190K

190K jobs by 2018, Mckinsey Global Report

$51 Billion

Analytics industry to grow to $51bn by 2016.

50%

Annual pay hikes for Analytics professionals in India is on an average 50% more than other IT professionals.

34+ REVIEWS

2498+ LEARNERS

"Once a new technology rolls over you, if you`re not part of the steamroller, you`re part of the road." - Stewart Brand

21,999

OUR MENTOR-DRIVEN PROGRAM

Learn the fundamentals

Learn intermediate concepts

Learn advanced concepts

Getting Started

Know history and mission of BGAcademy.
Get a walkthrough on the Course plan.
Setup and get familiar with R Studio which we will be using as our Integrated Development Environment (IDE)

Start with Basics

Understand the need for Machine Learning.
Get overview of the different machine learning algorithms

COURSE SYLLABUS

Introduction to machine learning

What is machine learning?
Learning system model
Training and testing
Performance
Algorithms
Machine learning structure
What are we seeking?
Learning techniques

Nearest neighbor classification

Instance based classifiers
Nearest-Neighbor classifiers
Lazy vs. Eager learning
k-NN variations
How to determine the good value for k
When to consider nearest neighbors
Condensing
Nearest neighbour issues

Naive Bayes classification

Naive Bayes learning
Conditional probability
Bayesian theorem: basics
The Bayes classifier
Model parameters
Naive Bayes training
Types of errors
Sensitivity and specificity
ROC curve
Holdout estimation
Cross-validation

Decision Trees - Part I

Key requirements
Decision tree as a rule set
How to create a decision tree
Choosing attributes
ID3 heuristic
Entropy
Pruning trees - Pre and post
Subtree Replacement
Raising

Decision Trees - Part II

Tree induction
Splitting based on ordinal attributes
How to determine the best split
Measure of impurity: GINI
Splitting based on GINI
Attributes binay
Categorical -GINI
Strengths and weakness of decision trees

Ensemble Approaches

Ensemble approaches
Bagging model
Boosting
The AdaBoost algorithm
Gradient boosting
Random forests
RIF
RIC
Advantages
Disadvantages

Artificial Neural Network

Background of brain and neuron
Neural networks
Neurons diagram
Neuron models- step function
Ramp func etc
Perceptrons
Network architectures
Single-layer feed-forward

Artificial Neural Network continued

Multi layer feed-forward NN (FFNN)
Back propagation
NN design issues
Recurrent network architecture
Supervised learning NN
Self organizing map
Network structure
SOM algorithm

Project I

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

Project I cont...

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

Support Vector Machine Classifiers

Support vector machines for classification
Linear discrimination
Nonlinear discrimination
SVM mathematically
Extensions
Application in drug design
Data classification
Kernel functions

Linear Models in R

Introduction to regression
Why do regression analysis
Types of regression analysis
OLS regression
Dependent and independent variable(s)
Steps to implement a regression model
Simple linear regression
Understanding terminology of each of the output of linear regression

Correlation and Regression

Correlation
Strength of linear association
Least-squares or regression line
Linear regression model
Correlation coefficient R
Multiple regression
Regression diagnostics

Assumptions in Regression Analysis

The assumptions
Assumption 1 and explanation- residuals and non normality
Assumption 2 and explanation- heteroscedasticity
Assumption 3 and explanation- additivity
Assumption 4 and explanation- linearity ; Independence assumption;Residual plots

Model Selection in R

Fitting the model
Diagnostic plots
Comparing models
Cross validation
Variable selection
Relative importance
AIC
Dummy variable
Box cox transformations

Creating the model

Residuels vs fitted
Residuels vs regression
Diagnostic plots

Logistic Regression

Binary response regression model
Linear regression output of proposed model
Problems with linear probability model
Logistic function
Logistic regression & its interpretation
Odds ratio
Goodness of fit measures
Confusion matrix
What is cluster analysis?

Introduction to Cluster Analysis

Types of data in cluster analysis
A categorization of major clustering methods
Partitioning methods
Hierarchical methods
Density-based methods
Grid-based methods
Model-based clustering methods
Supervised classification

Principal Component Analysis (PCA)

Curse of dimensionality
Dimension reduction
Why factor or component analysis?
Principal component analysis
PCs variance and least-squares
Eigenvectors of a correlation matrix
Factor analysis
PCA process steps

Forecasting Principles

Basic time series and it's components
Moving averages (simple & exponential)
R'Â’s inbuilt function ts()
Plotting of time series
Business forecasting using moving average methods
The ARIMA model
Application of ARIMA model in business

Project II

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

Project II cont...

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

Project II cont...

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

Project II cont...

Mentee can select project from predefined set of BGAcademy projects or they can come up with their own ideas for their projects

INTERESTED IN CORPORATE TRAINING?

12
WEEKS
COURSE DURATION

2
SESSIONS
EVERY WEEK

1.5
HOURS
EACH SESSION

200
HOURS
PROJECTS & ASSIGNMENTS

GREAT

CAREER

COURSE EXTRACT

Level	Intermediate
Pre-Requisites	Prior knowledge in R language
Technologies/Tools	R, RStudio
Projects	2 (Fully Functional)
Batch Size	8 - 10 Students
Job Preparation	Yes
Certification	Yes
*247 support**	Yes

WE HELP YOU GET A GREAT JOB

ADDITIONAL BONUS WEEK

On course completion, you are ready to enter the exciting world of corporates . We show you how to make the right impression at job interviews.

INTENSIVE JOB INTERVIEW PRACTICE

Congratulations, you're now a programmer with awesome skills. In this bonus week, we'll help you prepare for your job interviews so that you can make a great first impression. This includes:

2 in-depth mock technical interviews

Guidance on how to answer real interview questions from top employers

Assistance with creating a resume that stands out

BUILDING AN ONLINE REPUTATION

We can help you build that online presence on sites such as Git, StackOverflow, Quora, and LinkedIn.

PROJECTS WHICH STUDENTS WILL DEVELOP

Classification Model with Algorithms

This project aims at creating a classification models for mushroom data set with different classification algorithms.

Bank Special Service

This project is based on a survey based on the special services offered by Banks in order to compete with their rivals.

Naave Bayes Classification Algorithm

This project aims at creating Naave Bayes classification algorithm to classify the people as republican and democrats.

Clustering Wine data

This project aims at clustering the wine data to determine the quantities of 13 constituents found in each of the three types of wines grown in Italy.

Using MNIST Data

This project aims to classify handwritten digits using the famous MNIST data.

CUSTOMER FEEDBACK

PRICING

21,999

MONEY BACK GUARANTEE

If you are unhappy with the course and opt out in the first week, you get a complete refund.

SCHOLARSHIPS

We subsidize our fees by 10% for military personnel, and college students with exceptional records. To apply for a scholarship, email enquiry@acadgild.com.

GROUP DISCOUNT

More than 3 people in a group or from the same Company can avail special discount. For more information contact us at enquiry@acadgild.com

FAQ'S

What is this course all about?

Machine learning is the science of getting computers to act without being explicitly programmed. In this course, you will learn about the most effective machine learning techniques and gain experience by implementing them and getting them to work for you. More importantly, you'll learn about not only the theoretical part but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI (Artificial Intelligence)

Who should do this course?

Graduates, post graduates and PhD scholars (from statistical background), entry-level software professionals and anyone who is interested in data analytics career can do this course.

Why should I do this course?

This course provides a broad introduction to machine learning, data mining and statistical and pattern recognition. The course will also enable you to implement numerous case studies and applications, so that you'll also learn how to apply learning algorithms. It is the world’s most powerful programming language for statistical computing and graphics making it a must for Data Scientists.

What are the pre-requisites for this course?

The Machine Learning course doesn’t delve deep into R language as it is already covered in the Business Analytics with R course. We assume that the candidate going for Machine Learning course has prior knowledge in R language and knows how to perform data manipulation in R.

What are the job prospects after doing this course? Do I need to do any additional course after doing this?

After doing this course, one will have good knowledge in statistics such as:

Supervised learning (classification algorithms, support vector machines, neural networks).
Unsupervised learning (clustering, recommender systems).
Best practices in machine learning.

Technologies that will be used for this course.

R, RStudio

BECOME AN EXPERT IN MACHINE LEARNING IN
12 WEEKS

INTENSIVE JOB INTERVIEW PRACTICE

BUILDING AN ONLINE REPUTATION

COMPANY

SUPPORT

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FOLLOW US ON

BECOME AN EXPERT IN MACHINE LEARNING IN 12 WEEKS

INTENSIVE JOB INTERVIEW PRACTICE

BUILDING AN ONLINE REPUTATION

COMPANY

SUPPORT

ABOUT

FOLLOW US ON

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BECOME AN EXPERT IN MACHINE LEARNING IN
12 WEEKS