An E-Learning Product by Softpro Group

Python with Machine Learning

Content designed by Industry Experts

Become an expert on Machine Learning through POLYPREP's Online Summer Training/Internship Program under the guidance of Experienced Consultants & Live Classroom features.

200 Students 60 Days

Learning Objective

Python with Machine Learning Training Program helps the trainees to learn the basic concepts of Python Language and its application with Machine Learning. The main aim is to provide the learners with an introductory and broad overview of the field of ML with the focus on its applications in real world. Supervised Machine Learning methods are used in today’s real world applications like banking, e-commerce, transportation services and many others.
The module of this program covers all the theoretical and practical knowledge needed by one to learn and implement Machine Learning.
A brief preview of topics that are covered in more details in subsequent modules of the training on Python with Machine Learning is mentioned below. The training program is designed for the students of Engineering & related fields.

Complimentory Technologies

Hyper Text Markup Lanuguage (HTML)

Cascading Style Sheet (CSS)

Java Script (JS)




Getting Started with Python

Python Overview
About Interpreted Language
Advantages/ Disadvantages of Python pydoc.
Starting Python
Interpreter Path
Using the Interpreter
Running a Python script
Using Varriables
Built-in Functions
Strings Different Literals
Math Operators and Expressions
Writing to the Screen
String Formatting
Command Line Parameters and Flow Controls.

Sequences and File Operations

Indexing & Slicing
Iterating throw a Sequences
Using Enumerate()
Operators and Keywords for Sequences
The xrange() Function
List Comprehensions
Generator Expressions
Dictionaries and Sets

Deep Dive- Functions Sorting errors and Exception Handling

Function Parameters
Global Variables
Alternate Keys
Lambda expressions
Sorting Collections of Collection
Sorting Dictionaries
Sorting List in Place
Errors and Exception Handling
Handling multiple Exceptions
The Standard Exception hierarchy
Using Modules
The Import statements
Module Search Path
Package Installation Ways

Regular Expression's Packages and Object Oriented Programming in Python

The Sys Module
Interpreter Information
Launching External Programs
Paths Directories and Filenames
Walking Directory Trees
Maths Function
Random Numbers
Dates and Times
Zipped Archives
Introduction to Python Classes
Defining Classes
Instances Methods
Class Methods and DataStatic Methods
Private Methods and Inheritance
Module aliases and Regular Expression

Debugging, Databases and Project Skeletons

Dealing with Errors
Creating a Database with SQLite3
CRUD Operations
Creating a Database Object


Learning NumPy
Plotting using matplotlib and Seabron
Machine Learning Application
Introduction to Pandas
Creating Data Frames
Grouping Sorting
Plotting Data
Creating Functions
Converting Different Formats
Combining Data from Various Formats
Slicing / Dicing Operations

First Machine Learning Algorithm Python

Various Machine Learning Algorithm in Python
Apply Machine learning Algorithm in Python

Features Selection and Preprocessing

How to select the right Data
Which are the best feature to use
Additional features selection techniques
A Feature selection case study
Preprocessing Introduction
Preprocessing Scaling techniques
How to Preprocess your Data
How to Scale your Data
Feature Scaling Final Project

Which Algorithm Perform Best

Highly Efficient machine Learning Algorithms
Bagging Decision Trees
The Power of ensembles
Random Forest Ensemble technique
Boosting - Adaboost
Boosting enesemble stochastic gradient boosting
A final ensemble technique

Model Selection Cross Validation Score

Introduction Model Tuning
Parameter Tuning GridSearchCV
A Second method to tune your Algorithm
How to automate machine learning
Which ML Algo should you choose
How to compare machine learning Algorithms in practice

Neural Networks and deep learning in Python

Neural Networks Introduction
What is deep learning
What is one hot encoding
How to implement one hot encoding
How to handle missing values
How to impute missing Values
Introducing the MNIST dataset

Tensor Flow

Programming a neural network in tensorflow
Programming a neural network-Multilayer perceptron in tensorflow


Introduction to keras - a convient way to code neural networks
What is a convolutional neural network
How does a cnn work


Creating a convolutional neural network from scratch
What are RNNs - Introduction to RNNs
Recurrent Neural Network rnn in python
LSTMs for Begginners - Understanding LSTMs
Long short term memory neural network LSTM in Python

Industry Endorsed Project Work