Machine Learning with Python Online Training


Machine Learning with Python Training

Machine learning is a division of science that deals with programming the system in such a method to facilitate they automatically study and recover with knowledge. Here, learning means recognize and considerate the input information and creation wise decision base on the complete data.

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It uses this data to essentially explain and predict the behavior of human or machine generated system. Techniques of many fields are used, thus one should have the knowledge of linear algebra, non-linear systems, analytical geometry, optimization, calculus programming language (R, Python, SAS), statistics, machine learning (supervised, unsupervised and reinforcement learning). However, machine learning which is originated from artificial intelligence is used as a tool by data scientist to produce enhanced results. In the following module, we are covering machine learning basics, techniques and algorithms and how machine learning can be beneficial for solving different types of problems.

 Course Summary:

Course Name Machine Learning Online Training
ContentsFundamentals of  Machine Learning using Python
Duration48 Hours with Flexible timings
DeliveryInstructor Led-Live Online Training, Learning Management System Access.
EligibilityBasic Linear Algebra, Programming Experience, Statistics, and Probability
Live Online TrainingLive Interactive Training by Certified & Industry Expert Trainers by Providing Server and Lab access.
Ideal forProfessionals seeking to enhance their knowledge of machine learning python and data science.
AvailabilityRegular/Weekend Batches. 24×7 Teaching Assistance and Support.

Course Objectives

The main purpose of machine learning is to determine the pattern in your statistics and then build predictions based on those often, composite patterns to reply business questions, and assist resolve problems. The purpose of this class is to give you a whole understanding of machine learning, cover theory, applications, and internal mechanism of supervised, unsupervised, and deep knowledge of algorithms.


Most Python programmers would have the same opinion that the main benefit of Python is that it is simple to pick and choose. Ease of use and simple readability are more than just an ease. It can also advantage the user of your agenda. Simple usability helps you think extra visible when you write a program, and for others who have to develop or maintain the program.

Experts, as well as beginners, can simply recognize the system and you can rapidly become creative with this language since it has less ‘dialects’ than the additional popular language like Perl. Since its source code resembles the pseudo code, it is also simple to learn. As soon as you start learning, you can start code successfully nearly without delay.

Machine Learning

Course Curriculum

This is time to learn Machine learning online training. Here you can get an idea about python machine learning Supervised and Unsupervised learning and examples for each category. This tutorial provides machine learning online course with Real-time Industry Experts. Also providing corporate training worldwide in USA, UK, Canada, Europe, Dubai, Australia, and India.  Access to pre-recorded Machine Learning Python Training Videos. Download of self-paced Machine Learning Training course content/material prepared by industry experts.


 Download Course Curriculum

Machine Learning

Module 1:   Machine learning Introduction & Use Cases 

Module 2:   Statistics 2 – Inferential Statistics

Module 3:   Linear Regression

Module 4:   Logistic Regression

Module 5:   Decision Trees, Random Forest

Module 6:   Modeling Techniques (PCA, Feature Engineering)

Module 7:    KNN, Naive Bayes

Module 8:    Support Vector Machines (SVM)

Module 9:    Clustering, K-means

Module 10:  Time Series Modelling


Advanced Machine Learning

Module 1:  Market Basket Analysis & Apriori Algorithm 

Module 2:  Recommendation System

Module 3:  Recommendation System – Mini Project

Module 4:  Dimensionality Reduction (LDA, SVD)

Module 5:  Dimensionality Reduction (Matrix optimization) 

Module 6:  Anomaly Detection

Module 7:   XG Boost

Module 8:   Gradient Boosting Machine(GBM)

Module 9:   Stochastic Gradient Descent(SGD) 

Module 10:  Ensemble Learning – I

Module 11:  Ensemble Learning – II

Module 12:  Introduction to Neural Networks


Modules covered in the course (Python)

Topic 1

 Python Overview: Introduction, features.

Basic Syntax: Interactive mode programming, script mode programming, identifiers, line and indentation, quotation, comment and command line arguments in python.

Variables Type: Assigning a value to a variable, multiple assignment, standard data types, number, string, list, tuple, dictionary, data type conversion.

Topic 2

 Basic Operators: Arithmetic operators, comparison operators, assignment operators, bitwise operators, logical operators, membership operators, identity operators.

Decision Making: Single statement suites.

Python Loops: Loops (while, for, nested), control statement of loops.

Topic 3

Number (Number: int, long, float, complex): Assigning a value to a number, delete the reference to a number, number type conversion, mathematical functions, random number functions, trigonometric functions, mathematical constants.

 String: Accessing values in a string, updating strings, escape characters, string special characters, string special operators, string formatting operator, triple code, Unicode string, built-in string methods.

Topic 4

List: Basic list operations, indexes, accessing values in the list, updating list, delete list elements.

Tuple: Basic tuple operations, indexing, accessing values in the tuple, updating tuple, delete tuple element.

Dictionary: Accessing values in the dictionary, updating dictionary, delete dictionary elements, list under dictionary, dictionary under list, sorting in a dictionary.

Topic 5

Date and Time: Tick, time tuple, current time, getting formatted time, getting a calendar.

Python Function: Defining a function, calling a function, overloading concept, function arguments, required arguments, keyword arguments, default arguments, variable length arguments, anonymous function, return statements, the concept of variables.

The concept of oops: Classes and objects, an overview of oop terminology, creating classes, creating instance objects, accessing attributes, built in class attributes, destroying objects, class inheritance, overriding methods, overloading operators, data hiding, Encapsulation, data abstraction, polymorphism.

Topic 6

Module: Import statements, from import, from import * statement, locating modules, PYTHONPATH variable, namespace and scoping, dir () function, reload() function, packages in python.

Topic 7

Exception: Exception handling, assert statement, except clause, try-finally clause, an argument of exception, raising the exception, user-defined exception.

Topic 8: Advanced Python Libraries and Anaconda

Topic 9: Web Development using Django

Topic 10: Web Scraping using Scrapy

Topic 11: Data Science Essentials

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