Radicals.in

DATASCIENCE & MACHINE LEARNING WITH PYTHON TRAINING IN BANGALORE

Data Science, Statistics with Python This course Start with introduction to Data Science and Statistics using Python. It covers both the aspects of Statistical concepts and the practical implementation using Python. If you’re new to Python, don’t worry – the course starts with a crash course to teach you all basic programming concepts. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems. Analytics: Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Data frames to manipulate data with ease. Machine Learning and Data Science : Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets. Real life examples: Every concept is explained with the help of examples, case studies and source code wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant. finance context.

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Why Radical Technologies

Live Online Training

Highly practical oriented training
Installation of Software On your System
24/7 Email and Phone Support
100% Placement Assistance until you get placed
Global Certification Preparation
Trainer Student Interactive Portal
Assignments and Projects Guided by Mentors
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

Weekend / Weekdays / Morning / Evening Batches
80:20 Practical and Theory Ratio
Real-life Case Studies
Easy Coverup if you missed any sessions
PSI | Kryterion | Redhat Test Centers
Life Time Video Classroom Access ( coming soon )
Resume Preparations and Mock Interviews
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Self Paced Training

Self Paced Learning
Learn 300+ Courses at Your Own Time
50000+ Satisfied Learners
Course Completion Certificate
Practical Labs Available
Mentor Support Available
Doubt Clearing Session Available
Attend Our Virtual Job Fair
10% Discounted Global Certification
Course completion certificate and Global Certifications are part of our all Master Program

Live Online Training

Highly practical oriented training
Installation of Software On your System
24/7 Email and Phone Support
100% Placement Assistance until you get placed
Global Certification Preparation
Trainer Student Interactive Portal
Assignments and Projects Guided by Mentors
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

Weekend / Weekdays / Morning / Evening Batches
80:20 Practical and Theory Ratio
Real-life Case Studies
Easy Coverup if you missed any sessions
PSI | Kryterion | Redhat Test Centers
Life Time Video Classroom Access ( coming soon )
Resume Preparations and Mock Interviews
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Self Test Training

300+ Technologies - Learn at your Convenience
4500+ High Quality Videos
Self Paced Training By Experts
Self Paced Hands-On Practical LABS
Cloud Sand Boxes
Do Projects and Assignments with Live LABS
100+ Exam Simulators & Discounted Vouchers
Live Mentor Support - By 10+ Years Experts
Course completion certificate and Global Certifications are part of our all Master Program

Why enroll for Terraform course ?

Course Benefits

A huge library ecosystem. Python offers a vast choice of libraries for AI development, which contain base-level items that save coding time. … High readability. … The flexibility of the language. … Abundant community support. … Excellent visualization options.

Designations

Want to become Engineer?

Want to become Engineer?

Want to become Engineer?

WhyTerraform ?

Multi-Cloud Support

Terraform supports multiple cloud providers, including AWS, Azure, Google Cloud Platform (GCP), and others. This allows you to use a single tool and consistent workflow across different cloud environments, enabling hybrid and multi-cloud deployments.

Declarative Configuration

Terraform uses a declarative language to define infrastructure configurations. This means you specify the desired state of your infrastructure, and Terraform handles the provisioning and management to achieve that state. This approach is intuitive and allows for easier understanding and maintenance of infrastructure code.

Resource Graph

Terraform builds a dependency graph of your infrastructure resources based on their relationships and dependencies. This enables Terraform to determine the correct order of resource provisioning and manage complex infrastructures with ease.

Modularity and Reusability

Terraform encourages modularization and code reuse through the use of modules. Modules are self-contained units of Terraform configurations that can be reused across projects, teams, and environments. This promotes consistency, reduces duplication, and speeds up development.

State Management

Terraform maintains a state file that tracks the current state of your infrastructure. This state file is used to plan and apply changes, track resource attributes, and manage updates. Terraform's state management ensures consistency and facilitates collaboration among team members.

Plan and Apply Workflow

Terraform follows a plan and apply workflow, where changes to infrastructure are first planned and previewed before being applied. This allows you to review the proposed changes, identify potential issues, and apply changes safely, minimizing the risk of unintended consequences.

Extensibility

Terraform is highly extensible and integrates with a wide range of third-party tools and services. This includes integrations with configuration management tools, CI/CD pipelines, monitoring solutions, and more, allowing you to build comprehensive infrastructure automation workflows. While other IaC tools offer similar capabilities, Terraform's multi-cloud support, declarative configuration, resource graph, modularity, state management, workflow, and extensibility make it a popular choice for infrastructure automation in diverse environments. Additionally, Terraform's active community, robust documentation, and frequent updates contribute to its widespread adoption and continued development.

About your Terraform Certification Course

Terraform Skills Covered

  • State Management

  • Terraform Modules

  • Dependency Management

  • Terraform CLI

  • Terraform Configuration Language (HCL)

  • Infrastructure as Code (IaC) Principles

  • Resource Provisioning

  • Terraform Providers

  • Terraform Workspaces

  • Terraform Best Practices

Curriculum Designed by Experts

Datascience & Machine Learning With Python Course Syllabus

Course content summary -Introduction to Data Science with Python / Python Essentials

Course content summary -Introduction to Data Science with Python

  • What is analytics & Data Science?
  • Common Terms in Analytics
  • Analytics vs. Data warehousing, OLAP, MIS Reporting
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • How leading companies are harnessing the power of analytics?
  • Critical success drivers
  • Overview of analytics tools & their popularity
  • Analytics Methodology & problem solving framework
  • List of steps in Analytics projects
  • Identify the most appropriate solution design for the given problem statement
  • Project plan for Analytics project & key milestones based on effort estimates
  • Build Resource plan for analytics project

Python Essentials

  • Why Python for data science?
  • Overview of Python- Starting with Python
  • Introduction to installation of Python
  • Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
  • Understand Jupyter notebook & Customize Settings
  • Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels – Date & Time Values
  • Basic Operations – Mathematical – string – date
  • Reading and writing data
  • Simple plotting
  • Control flow & conditional statements
  • Debugging & Code profiling
  • How to create class and modules and how to call them?
    Scientific Distributions Used In Python For Data Science

NumPy, pandas, scikit-learn, stat models, nltk

 

Accessing/Importing And Exporting Data Using Python Modules / Data Manipulation – Cleansing – Munging using python modules

Accessing/Importing And Exporting Data Using Python Modules

  • Importing Data from various sources (Csv, txt, excel, access etc)
  • Database Input (Connecting to database)
  • Viewing Data objects – subsetting Data, methods
  • Exporting Data to various formats
  • Important python modules: Pandas, beautiful soup

Data Manipulation – Cleansing – Munging using python modules

  • Cleansing Data with Python
  • Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data t