Machine Learning with Python Training

Dive into the world of Machine Learning using Python with this industry-aligned course.

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12000+

Trained

20+

Data Scientist

4.6

rating

Overview

The Machine Learning with Python Training Course will acquaint you with the basics of Machine Learning through Python, help you gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.

The course will train you to be able to implement your understanding in real-world situations. You will learn to install the required Python environment and other auxiliary tools and libraries.

The interactive training course is designed to suit the needs of every individual, introducing you to data exploration and different Machine Learning approaches.The course will familiarise you with concepts like supervised and unsupervised learning, regression, and classifications and so on.

Objectives of the course:

  • Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions.
  • Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions.
  • Perform data analysis and manipulation using data structures and tools provided in the Pandas package.
  • Gain expertise in machine learning using the Scikit-Learn package.
  • Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline.
  • Use the matplotlib library of Python for data visualization.

Key Features of the course

High Quality Courseware

Get access to study material, white paper, mock exam and case studies prepared by the ML industry experts.

Interactive instructor-led training

Training sessions that meet the exact needs of every individual.

Practical training

Experience practical project development led by actual examples.

Relevant Study Material

Get relevant study material from Machine Learning Community Platform.

Extensive Learning

Learn better with case studies, activities and quizzes

Self-Paced Learning

40 hours of self-paced learning.

Curriculum

Topics Covered

  • Pre-Training Assessment
    • MCQ based Pre-Training assessment to evaluate understanding current on Python and Data Science
  • Module 1: Introduction to Python, its Sequences and File Operations
    • Overview of Python
    • The Companies using Python
    • Different Applications where Python is Used
    • Discuss Python Scripts on UNIX/Windows
    • Values, Types, Variables
    • Operands and Expressions
    • Conditional Statements
    • Loops
    • Command Line Arguments
    • Writing to the Screen
    • Python Files I/O Functions
    • Numbers
    • Strings and Related Operations
    • Tuples and Related Operations
    • Lists and Related Operations
    • Dictionaries and Related Operations
    • Sets and Related Operations
  • Deep Dive – Functions, Modules, Errors and Exceptions
    • Functions
    • Function Parameters
    • Global Variables
    • Variable Scope and Returning Values
    • Lambda Functions
    • Standard Libraries
    • Modules Used in Python
    • The Import Statements
    • Module Search Path
    • Package Installation Ways
    • Errors and Exception Handling
    • Handling Multiple Exceptions
  • Python Jewels
    • The Zen of Python, Common idioms, Lambda functions List comprehensions, Generator expressions, String formatting
  • Modules and Packages
    • Initialization, code, Namespaces, executing modules as scripts Documentation, Packages and name resolution Naming conventions, Using imports
  • Classes
    • Defining classes, Instance methods and data Initializers, Class methods, Static methods, Inheritance, Multiple inheritance Pseudo-private variable
  • OS
    • The OS module, Environment variables Launching external commands Walking directory trees, Paths, directories file names Working with file systems
  • Data & Time Module
    • Dates and times operations with datetime and time module
  • Regular Expression
    • Defining regular expressions, Compiling regular expressions Using regular expressions, using match objects to extract a value Extracting multiple items, Replacing multiple items
  • Database Access
    • Creating and executing a cursor, CRUD operations with databases like mysql, sqlite3, oracledb , mongodb etc
  • Module 2: Introduction to Data Science
    • What is Data Science, significance of Data Science in today’s digitally-driven world
    • Applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle
  • Data Manipulation / Data Preprocessing using Pandas
    • Pandas
    • Data Structures & Index Operations
    • Basic Functionalities of a Data Object
    • Merging of Data Objects
    • Concatenation of Data Objects
    • Types of Joins on Data Objects
    • Exploring a Dataset
    • Analyzing a dataset
    • Reading and Writing Data from Excel/CSV Formats with Pandas
    • Missing and Replace Values in data frame
    • Data Operations
    • File Read and Write Support by pandas
    • Pandas Sql Operation
  • Mathematical Computing with Python (NumPy)
    • NumPy
    • Introduction to Numpy
    • Activity-Sequence it Right
    • Creating and Printing an ndarray
    • Class and Attributes of ndarray
    • Basic Operations
    • Activity-Slice It
    • Copy and Views
    • Mathematical Functions of Numpy
  • Data Visualization with Matplotlib and Seaborn
    • The Matplotlib Library
    • Introduction to Data Visualization, Line Properties
    • (x,y) Plot and Subplots, Types of Plots
    • Grids, Axes, Plots
    • Markers, Colors, Fonts and Styling
    • Types of Plots - Bar Graphs, Pie Charts, Histograms Contour Plots
  • INTRODUCTION TO MACHINE LEARNING
    • What is Machine Learning?
    • Overview about Sci-Kit learn and TensorFlow
    • Types of ML
    • Some complementing fields of ML,ML algorithms
  • Regression and Classification for Business Applications Using Python
    • Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. For predicting a continuous set of data, we use linear regression. However, for categorical data we use various classification algorithm logistic and tree based methods being the most important of them.
      Learning Outcome:
      After completion of this Module, participants would be able to
    • Identify and remove the outliers in the Data Set
    • Detect Multicollinearity amongst independent variables and tackle that issue
    • Able to build a linear regression model and predict
    • Able to perform diagnostic checks about model's efficiency and accuracy (i.e. calculate R-squared)
    • Able to build a classifier and logistic regression for predicting categorical data
    • Build a confusion matrix for measuring accuracy and precision of a classifier
  • Model Selection
    • What is Model Selection?
    • Need of Model Selection
    • Cross – Validation
  • CLUSTERING BASED LEARNING
    • Definition
    • Types of clustering
    • The k-means clustering algorithm
    • Dimensionality Reduction; Principal Component Analysis
    • PCA Example with the Iris data set
  • Post-Training Assessment
    • Online MCQ based post-training assessment to gauge current understanding on Python and Data Science

Prerequisite

  • Basic knowledge of C Programming and statistics.
  • Eagerness to learn new innovative things.

Study material:

1. Course Materials are important as they are aligned with the course covered in class and can be easily downloaded from the Big Data Community Platform.

2. A Comprehensive Guide that covers all your doubts and includes a detailed reading list, accessible after course completion through the Learning Plan in the Big Data Community Platform.

Benefits Attendees Get:

  • A Machine Learning with Python certificate.
  • A Study Guide to help you navigate further hurdles, if any come up.
  • Posters to be used internally in Organization or projects (Softcopy, PDF format).
  • Case studies to review and relearn from.
  • A huge amount of practical exercises to keep you on your feet.
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What does Xebia provide differently?

Step into the realm of learning for an all-inclusive growth. Xebia is a pioneering IT consultancy and service provider that aims at Enterprise Development, Agile Development, DevOps, and Outsourcing Services.

World-class Training

World-class Training

Xebia Academy offers an intensive learning program and industry-specific training courses. It’s a globally acclaimed APMG International Partner for Big Data & Data Science training and certification courses. ReadmoreReadless

Boon To Career

Boon To Career

Xebia offers excellent consultancy, innovative tools, and continuous career growth. We will train you to become a Big Data and Data Science expert. ReadmoreReadless

Expert Advantage

Expert Advantage

Get trained by our In-House Data Science experts with an average of 18 years of experience: Data Science and Big Data Experts with extensive knowledge of data and AI.ReadmoreReadless

Flexible Learning

Flexible Learning

Pick the right course: You can choose a public class at our training centre, or learn with your colleagues in a customized, in-company training program, facilitated on-site at your location, anywhere in the world.ReadmoreReadless

Global Experience

Global Experience

18 years of professional training experience and trusted by over 1,00,000 professionals worldwide. Xebia Academy is the largest producer of Big Data and Data Science certifications globally.ReadmoreReadless

Global Experience

Hands-on And Practical Learning Experience

Our trainers are hands-on practitioners and provide interactive training sessions which let students master required skills in real-world scenarios, giving them an edge in the industry.ReadmoreReadless

Certification Process

  • 01

    Enroll for Machine Learning With Python Course

  • 02

    Attend the training sessions

  • 03

    Get certified by Xebia Academy Global

Industry Connect

Who should attend this course?

  • Developers

  • Analytics Managers

  • Information Architects

  • Analytics Professionals

  • Anyone interested in Machine Learning and using it to solve problems

  • Software or data engineers interested in quantitative analysis with Python

  • Data analysts, economists or researchers

What skills will you learn in the course?

The Basics

You’ll learn the essential concepts of Python programming such as data types, basic operators and functionsReadmoreReadless

Practical Implementation

You’ll learn to install the required Python environment and other auxiliary tools and libraries. ReadmoreReadless

Working with NumPy

You’ll learn how to perform high-level mathematical computing using the NumPy package and its large library of mathematical functions. ReadmoreReadless

Working with Data

You’ll learn data analysis and manipulation using data structures and tools provided in the Pandas package, and to use the matplotlib library of Python for data visualization.ReadmoreReadless

Detailed Machine Learning Essentials

You’ll gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline. ReadmoreReadless

Why should you attend this course?

By the end of this course, you’ll acquire an understanding of:

  • The essential concepts of Python programming such as data types, basic operators and functions.
  • How to install the required Python environment and other auxiliary tools and libraries.
  • Working with the NumPy package and its library of mathematical functions.
  • Data analysis and manipulation using data structures and tools provided in the Pandas package, and to use the matplotlib library of Python for data visualization.
  • Supervised learning and unsupervised learning models.

Program Visual Library

FAQs

There are no prerequisites required for this course. Basic knowledge of C Programming and statistics would be useful.

Anyone who has an interest in this field and has the prerequisite knowledge. Pre-requisites:

H/W
  • Processor – i3 or above.
  • RAM - 3GB or above
  • Batch size
Software
  • Window – 10,7(Service Pack 1(mandatory (updated))), 64 bit
  • Version- Python-3.7 or above

The enrollment process is actually easy. You just need to choose your options: Debit/Credit Card or PayPal.

The Machine Learning with Python certification is valid for a lifetime. You do not need to renew it.

The training course is of a total forty hours, which you can finish at your own pace.

The sessions will start from scratch and go on to advanced level, to cover all the required cases:

  • Understanding and Execution of Python scripts with python containers.
  • Understanding and Execution of Python scripts with conditional statements.
  • Understanding and Execution of Python scripts with loops.
  • Understanding and Execution of Python scripts with operators.
  • Read and write data into files based on conditions.
  • Schedules Python Scripts with system.
  • Create a GUI with Python.
  • Create executable files for scripting and GUI for windows.
  • Apply CRUD operations on databases.
  • Read data from Xlsx and CSV...etc files using Pandas and prepare based on conditions(vlookup,join,search..etc.).
  • Export data into files after pre-processing using Pandas python.
  • Preprocess Data set for analysis and prepare data for Machine Learning algorithm apply Regression on dataset using Sklearn ML module of Python
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