## Machine Learning Zero to Hero

### #MLZeroToHero

### Venue:

Flat No #3, V R K H Building, Vivekananda Layout, Opposite to Home town, Beside Biriyani Zone, **Marathahalli, Bangalore,**Karnataka– 560037

## 9 AM to 5 PM

## Who are Eligible to join #MLZeroToHero Bootcamp ?

#### IT Professionals,

Data Analysts, Business Analysts,

Functional Managers,

Graduates, Post Graduates

Also, anyone having interest in Data Science is free to attend our Bootcamp.

## Benefits of attending our #MLZeroToHero Bootcamp ?

**✓ Salary Hike: **40% to 80% higher than regular Software Engineer.

**✓ Growth Rate: **290% increase in Machine Learning – Artificial Intelligence Jobs in India

**✓** 100% Practical Sessions

**✓** Live Projects to Work

**✓ **Certificate of recommendation after successful projects

### Enrol now to Get Started

## Topics (Curriculum)

**INTRODUCTION TO STATISTICS**

**Basic business statistics**

- How businesses use statistics
- The basic vocabulary of statistics
- The types of data used in business

**Organizing and Visualizing Data**

- The sources of data used in business
- To construct tables and charts for numerical data
- To construct tables and charts for categorical data
- The principles of properly presenting graphs

**Numerical Descriptive Measures**

- To describe the properties of central tendency, variation, and shape in numerical data
- To construct and interpret a boxplot
- To compute descriptive summary measures for a population
- To compute the covariance and the coefficient of correlation

**Basic Probability and Terms**

- Basic probability concepts
- Conditional probability
- To use Bayes’ Theorem to revise probabilities
- Various counting rules

**Probability Distributions**

- Types of Distributions like Discrete and Continuous
- Functions of Random Variables
- Probability Distribution Graphs

**Sampling and Sampling Distributions**

- To distinguish between different sampling methods
- The concept of the sampling distribution
- To compute probabilities related to the sample mean and the sample proportion
- The importance of the Central Limit Theorem

**Confidence Interval Estimation**

- To construct and interpret confidence interval estimates for the mean and the proportion
- How to determine the sample size necessary to develop a confidence interval estimate for the mean or proportion
- How to use confidence interval estimates in auditing

**Fundamentals of Hypothesis Testing**

- Null and Alternate hypothesis
- One sample Tests
- Test statistic and critical values
- Possible errors in testing
- p- value approach | t and z-tests | Testing on proportions

**DATA TRANSFORMATION AND VISUALIZATION**

**Data Transformations**

Merge, Rollup, Transpose and Append | Smoothing |

Aggregation | Normalization | Attribute construction

**Feature Engineering**

Missing value Imputation | Outlier Analysis and Treatment |

Binning | Creating dummy variables | feature scaling |

Extracting Date | Log Transformation | Feature split | Label

Encoding | One-Hot Encoding

**Exploratory Data Analysis**

Summarizing and Visualizing the Important Characteristics

of Data | Visualization techniques – Box plot | Histogram |

Multi-variate chart | Pareto chart | Scatter plot | Stem-

and-leaf plot.

**PYTHON PROGRAMMING**

**Introduction to Python**

Installation | Python Basics | Spyder IDE | Jupyter Notebook

| Floats and Strings Simple Input & Output | Variables |

Operators | Single and Multiline Comments |

Taking input from user

**Data Structures**

List | Strings | Tuple | Dictionary | Sets and their examples

**Conditional Statements**

If | if-else | if-elif-else | nested if else

**Loops**

For | while | nested loops| loop control statements

**Functions**

Creating user defined Functions | Function arguments like-

Required, Keyword, Default and variable-length | Scope of

variables in creating functions | Anonymous Functions –

Lambda

**Exception and File handling**

Exception Handling | Raising Exceptions | Assertions | Files

I/O

### INTRODUCTION TO MACHINE LEARNING

**Introduction**

What is Machine Learning? | End-to-end Process of

Investigating Data Through a Machine Learning Lens |

Evolution and Trends | Application of Machine Learning |

Best Practices of Machine Learning

**Machine Learning Methods**

Supervised | Unsupervised

**Machine Learning Algorithms**

Classification | Regression | Time Series | Collaborative

Filtering | Clustering | Principal Component Analysis

### MACHINE LEARNING WITH PYTHON

**Numpy module**

Basics of numpy | creating arrays | Numpy functions

**Pandas**

Introduction to Pandas | IO Tools | Pandas – Series and Dataframe and their functions

**Matplotlib and Seaborn**

Graphical representation of data using various plots

**Scikit learn**

Introduction to SciKit Learn | Load Data into Scikit Learn |

Run Machine Learning Algorithms Both for Unsupervised and

Supervised Data | Supervised Methods: Classification &

Regression | Unsupervised Methods: Clustering, Gaussian

Mixture Models | Decide What’s the Best Model for Every

Scenario

**MACHINE LEARNING APPLICATIONS**

**Linear Regression**

Implementing Simple & Multiple Linear Regression |

Making Sense of Result Parameters | Model Validation |

Handling Other Issues/Assumptions in Linear Regression:

Handling Outliers, Categorical Variables, Autocorrelation,

Multicollinearity, Heteroskedasticity | Prediction and

Confidence Intervals | Use Cases

**Logistic Regression**

Implementing Logistic Regression| Making Sense of Result

Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-

Square Test | Goodness of Fit Measures | Model

Validation: Cross Validation, ROC Curve, Confusion Matrix |

Use Cases

**Decision Tree**

Implementing Decision Trees | Homogeneity | Entropy

Information Gain | Gini Index | Standard Deviation

Reduction | Vizualizing & Prunning a Tree | Implementing

Random Forests using Python | Random Forest Algorithm

| Important hyper-parameters of Random Forest for tuning

the model | Variable Importance | Out of Bag Errors

**Naïve Bayes**

Bayes Theorem | Gaussian Naïve Bayes & its

implementation | Multinomial Naïve Bayes, Count

vectorizer, TF-IDF Vectorizer and its use cases on Text

Classification

**K-Nearest Neighbors**

Concept of nearest neighbors | Euclidean Distance | Use

Cases of KNN Classifier & Regressor

**Support Vector Machine**

Introduction to support vectors | Concept of hard & soft

margins | slack variable | Lagrangian Primal & Dual | Kernel

Trick | Use Cases

**Time Series**

Introduction | Components of Time Series | Stationarity |

ACF | PACF | ARIMA model for forecasting | Use Cases

**K-Means Clustering**

Clustering concept | Finding optimal number of clusters |

Use Cases

**Hierarchical Clustering**

Agglomerative & Divisive Clustering | Dendrograms |

Linkage Matrix like Single, Complete & Average | Use Cases

**Ensemble Techniques**

Bagging | Boosting | Stacking | Regularization | Different

cross-validation techniques used to treat Over fitting and

Under fitting in machine learning models

**Principal Component Analysis**

Concept of Dimensionality reduction | Eigen values | Eigen

Vectors | Use Cases

**PROJECTS**

**PROJECT 1**

Title: Real Estate Price Prediction using Linear Regression

Industry: Real Estate

Description: The goal of this Use-case is to make property

price predictions using Real Estate data. The dataset

contains the of the price of apartments and various

characteristics of the property. Based on this data, decide

on the price of new properties.

**PROJECT 2**

Title: Loan Prediction using Logistic Regression & Decision

Tree

Industry: Finance

Description: The goal is to build a classification model to

predict if a loan is approved or not. Dataset contains

demographic information like age, income etc. of various

customers. Based on this, predict for a new customer

whether loan will be approved or not

**PROJECT 3**

Title: Student’s Performance Prediction using KNN & Naïve

Bayes

Industry: Education

Description: The goal is to build a classification model to

predict student’s performance from various characteristics

associated with student’s academics

**PROJECT 4**

Title: Handwritten digit recognition using Decision Tree

&Random forest

Description: Dataset contains various pixel values of

handwritten digits between 0 to 9. The goal is to build a

classification model to predict the digit from its pixel

values.

**PROJECT 5**

Title: Amazon fine food review-Sentiment Analysis

Industry: Amazon

Description: The analysis is to study Amazon food review

from customers and try to predict whether a review is

positive or negative. The dataset contains more than 500k

reviews with number of upvotes and total votes to those

comments

**DEEP LEARNING APPLICATIONS**

**Neural Networks**

Understanding Neural Networks | The Biological

Inspiration | Perceptron Learning & Binary Classification |

Backpropagation Learning | Learning Feature Vectors for

Words | Object Recognition

**Keras**

Keras for Classification and Regression in Typical Data

Science Problems | Setting up KERAS | Di erent Layers

in KERAS | Creating a Neural Network Training Models

and Monitoring | Artificial Neural Networks

**Tensorflow**

Introducing Tensorflow | Neural Networks using

Tensorflow | Debugging and Monitoring | Convolutional

Neural Networks

**Recurrent Neural Networks**

Introduction to RNN | RNN long & short term dependencies

| Vanishing gradient problem | Basic LSTM | Step by step

walk through LSTM | Use cases

**PROJECT 1**

**ANN ON KERAS**

Title: Credit Default using ANN on Keras

Industry: Finance

Description: This research aimed at the case of customers’

default payments in Taiwan. From the perspective of risk

management, the result of predictive accuracy of the

estimated probability of default will be more valuable than

the binary result of classification – credible or not credible

clients.

**PROJECT 2**

**CNN ON KERAS & TENSORFLOW**

Title: Handwriting/Facial recognition using CNN on

TensorFlow & Keras

Industry: Pattern Recognition

Description: This project will help build a model using

Convolutional Neural Network to recognize

handwriting/faces

**PROJECT 3**

**LSTM**

Title: Google stock price prediction

Industry: Finance

Description: Dataset contains dates, volume, open, close, high and low prices of stocks. Based on this build a LSTM model to predict current and future stock price