Machine Learning with Python for Data Science

The number of data science professionals in US alone would be around 700,000 by 2020. For aspiring Data Scientists Python is the most important language to learn because of its rich ecosystem. Python is among top 3 languages officially listed and used by Google. Python currently is used in more than 75% of all data analysis work across the world. Python programmers requirement including Machine Learning knowledge is expected to reach approximately 400,000 by end of 2018

Program Duration

3 months

Daily or Weekend

3 – 4 hours per day

Program Covers

Python Programming core & advanced
Python Scripting & Web Development

Detailed course

Python Programming

Introduction To Python

Installing Python

The Python Interpreter

Basics of Python
• Variables
• DataTypes

Python Strings
• String Methods
• Python Numbers and Booleans

Python Lists

Python Sets

Python Tuples

Python Ranges

Python Dictionaries
• Dictionaries – Methods
• Conversions Between Data Types Python Conditionals, Loops and Exceptions
• Conditionals – If / Elif / Else
• Loops – For / While
• Exceptions
• Try / Except / Else / Finally

Python Regular Expressions

Python Classes and Objects
• Classes – Objects
• Objects and their attributes
• Classes – Inheritance

Python Functions and Modules
• Functions – Basics
• Recursive functions
• Modules – Importing

Python File Operations

Advance Python Programming

• List / Set / Dictionary Comprehensions
• Lambda Functions
• map() and filter()
• Iterators and Generators
• Decorators
• Threading Basics
• Connecting to Database using Python
• Create Table/Insert/Update/Delete with Python
• Committing and Rolling Back Transactions

Python for Analytics & Machine Learning

• Fundamentals of Python
• Numpy Arrays
• Introduction to Matrices
• Pandas DataFrames
• Importing data into DataFrames
• Visualization
• Introduction to Matplotlib

Machine Learning with Python

Introduction of Data Science and Machine Leaning
Introduction Python

Statistics for Machine Learning
• Inferential Statistics
• Descriptive Statistics

Theory of Distribution
• Probability Distribution
• Sampling Data
• Types of Sampling

Regression & Modelling
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Decision Tree Regression
• Random Forest Regression
• Logistic Regression

K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Naive Bayes
Decision Tree Classification
Random Forest Classification
• K-Means , Hierarchical Clustering

Introduction to Deep Learning

• Apriori
• Upper Confidence Bound (UCB)
• Thompson Sampling
• Natural Language Processing
• Artificial Neural Networks