EDDZE - Machine Learning

15%

Course Description

Data Science, Machine Learning, Deep Learning and Artificial Intelligence

Objectives of the Program

To enable participants understand how Data Science is used in every aspect of our daily lives and businesses
To enable the participants to learn basics of Python.
To enable the participants to learn Data Science/Machine learning/Deep Learning using Python.
To empower the participants with sufficient knowledge to use latest technologies of Data Science (Hands-On).
To make the participants understand overview of how Exploratory Data Analysis works.
To provide the participants Hands-on experience in Data science concepts.
To provide the participants Hands-on experience in Machine Learning concepts.
To empower the participants with sufficient knowledge to use latest technologies of
Data Science/Machine Learning/Deep Learning/ Artificial Intelligence (Hands-On).
Overview of the Program Content
Introduction to Data Science, Machine Learning, Deep Learning and Artificial
Intelligence
Basics of Python and Hands-on on Python
Introduction to Data Science using Python
Hands on session on Data Science with Python [Database Connectivity with
PYTHON: Performing Database Transactions (Inserting, Deleting, and Updating
the Database)]
Introduction to Machine Learning and its types
Examples Overview of Machine Learning packages in Python
Introduction to Deep Learning and its types
Project in Data Science/Machine Learning /Artificial Intelligence

CURRICULUM DETAILS: MODULES AND CHAPTERS

Module 1: Python for Data Science/Machine Learning/Deep Learning

Basic building blocks of python
Data Types
Operators
Control Structure
Functions
Data Structures
String Operations and Regular Expressions
File Handling
Exception Handling
The Object-Oriented Approach

Module 2: Maths Behind Data Science

Statistical Analysis: Descriptive Statistics & Inferential Statistics
Hypothesis Testing

Module 3: Data Science (Exploratory data analysis (EDA) /Data wrangling/
Data Munging/Data Cleaning Techniques/ Data Preparation/Feature
Engineering)

o Data Science Library and data visualization Using Python
o Data Exploration (Univariate/Bivariate/Multivariate Analysis)
o Missing value treatment
o Outlier treatment
o Categorical to Numerical
o Feature Rescaling
o Feature (Variable) transformations
o Correlation Analysis
o Feature selection Methods

Module 4: Data Visualization using Tableau

Module 5: Introduction to Machine Learning: Supervised Learning

Regression Analysis- Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Advanced Regression
Classification Methods- Logistic Regression/Naïve Bayes/KNN/ Support Vector Machines (SVM)/Decision Tree/Random Forest

Module 6: Supervised Learning: Classification Methods: Ensemble
Techniques