Data Science & Machine Learning using Python

Course Overview

This course will acquaint the participants with the nuts and bolts of the python programming condition, including how to download and introduce python, expected basic python programming methods, and how to discover help with python programming questions. The course will likewise present information control and cleaning methods utilizing the famous python pandas information science library and present the reflection of the Data Frame as the focal information structure for information examination.

Course Objective

This course will empower you to:

  • Gain a top to bottom comprehension of information science process, information wrangling, information investigation, information representation, theory building, and testing. You will likewise take in the nuts and bolts of insights.
  • Install the required Python condition and other assistant instruments and libraries
  • Understand the basic ideas of Python programming like information sorts, tuples, records, dicts, essential administrators, and capacities.
  • Perform abnormal state numerical figuring utilizing NumPy bundle and its expansive library of scientific capacities

Course Outcome

Before the finish of this course you will:

  • Have a comprehension of how to program in Python.
  • Know how to make and control clusters utilizing numpy and Python.
  • Know how to utilize pandas to make and break down informational collections.
  • Know how to utilize matplotlib and seaborn libraries to make lovely information perception.
  • Have an astonishing arrangement of illustration python information investigation ventures!
  • Have a comprehension of Machine Learning and SciKit Learn!

Prerequisites

  • Basic Idea of Fundamental programming (Recommended Python)
  • Fundamental of Mathematics
  • Install Python

FAQ’S

Consider the possibility that I miss a class.

You will never miss a class. You can pick both of the two choices:

  1. You can experience the recorded session of the missed class and the class introduction that are accessible for web based review through the LMS.
  2. You can go to the missed session, in some other live clump. If it’s not too much trouble note, access to the course material will be accessible for lifetime once you have selected into the course.

Course Curriculum

  • Data Science Overview

    • Data Science
      0m
    • Data Scientists
      0m
    • Examples of Data Science
      0m
    • Python for Data Science
      0m
  • Data Analytics Overview

    • Introduction to Data Visualization
      0m
    • Processes in Data Science
      0m
    • Data Wrangling, Data Exploration, and Model Selection
      0m
    • Exploratory Data Analysis or EDA
      0m
    • Data Visualization
      0m
    • Plotting
      0m
    • Hypothesis Building and Testing
      0m
  • Statistical Analysis and Business Applications

    • Introduction to Statistics
      0m
    • Statistical and Non-Statistical Analysis
      0m
    • Some Common Terms Used in Statistics
      0m
    • Data Distribution: Central Tendency, Percentiles, Dispersion
      0m
    • Histogram
      0m
    • Bell Curve
      0m
    • Hypothesis Testing
      0m
    • Chi-Square Test
      0m
    • Correlation Matrix
      0m
    • Inferential Statistics
      0m
  • Python: Environment Setup and Essentials

    • Introduction to Anaconda
      0m
    • Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
      0m
    • Jupyter Notebook Installation
      0m
    • Jupyter Notebook Introduction
      0m
    • Variable Assignment
      0m
    • Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
      0m
    • Creating, accessing, and slicing tuples
      0m
    • Creating, accessing, and slicing lists
      0m
    • Creating, viewing, accessing, and modifying dicts
      0m
    • Creating and using operations on sets
      0m
    • Basic Operators: ‘in’, ‘+’, ‘*’
      0m
    • Functions
      0m
    • Control Flow
      0m
  • Mathematical Computing with Python (NumPy)

    • NumPy Overview
      0m
    • Properties, Purpose, and Types of ndarray
      0m
    • Class and Attributes of ndarray Object
      0m
    • Basic Operations: Concept and Examples
      0m
    • Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
      0m
    • Copy and Views
      0m
    • Universal Functions (ufunc)
      0m
    • Shape Manipulation
      0m
    • Broadcasting
      0m
    • Linear Algebra
      0m
  • Scientific computing with Python (Scipy)

    • SciPy and its Characteristics
      0m
    • SciPy sub-packages
      0m
    • SciPy sub-packages –Integration
      0m
    • SciPy sub-packages – Optimize
      0m
    • Linear Algebra
      0m
    • SciPy sub-packages – Statistics
      0m
    • SciPy sub-packages – Weave
      0m
    • SciPy sub-packages – I O
      0m
  • Data Manipulation with Python (Pandas)

    • Introduction to Pandas
      0m
    • Data Structures
      0m
    • Series
      0m
    • DataFrame
      0m
    • Missing Values
      0m
    • Data Operations
      0m
    • Data Standardization
      0m
    • Pandas File Read and Write Support
      0m
    • SQL Operation
      0m
  • Machine Learning with Python (Scikit–Learn)

    • Introduction to Machine Learning
      0m
    • Machine Learning Approach
      0m
    • How Supervised and Unsupervised Learning Models Work
      0m
    • Scikit-Learn
      0m
    • Supervised Learning Models – Linear Regression
      0m
    • Supervised Learning Models: Logistic Regression
      0m
    • K Nearest Neighbors (K-NN) Model
      0m
    • Unsupervised Learning Models: Clustering
      0m
    • Unsupervised Learning Models: Dimensionality Reduction
      0m
    • Pipeline
      0m
    • Model Persistence
      0m
    • Model Evaluation – Metric Functions
      0m
  • Natural Language Processing with Scikit-Learn

    • NLP Overview
      0m
    • NLP Approach for Text Data
      0m
    • NLP Environment Setup
      0m
    • NLP Sentence analysis
      0m
    • NLP Applications
      0m
    • Major NLP Libraries
      0m
    • Scikit-Learn Approach
      0m
    • Scikit – Learn Approach Built – in Modules
      0m
    • Scikit – Learn Approach Feature Extraction
      0m
    • Bag of Words
      0m
    • Extraction Considerations
      0m
    • Scikit – Learn Approach Model Training
      0m
    • Scikit – Learn Grid Search and Multiple Parameters
      0m
    • Pipeline
      0m
  • Data Visualization in Python using Matplotlib

    • Introduction to Data Visualization
      0m
    • Python Libraries
      0m
    • Plots
      0m
    • Matplotlib Features:
      0m
    • Line Properties Plot with (x, y)
      0m
    • Controlling Line Patterns and Colors
      0m
    • Set Axis, Labels, and Legend Properties
      0m
    • Alpha and Annotation
      0m
    • Multiple Plots
      0m
    • Subplots
      0m
    • Types of Plots and Seaborn
      0m
  • Data Science with Python Web Scraping

    • Web Scraping
      0m
    • Common Data/Page Formats on The Web
      0m
    • The Parser
      0m
    • Importance of Objects
      0m
    • Understanding the Tree
      0m
    • Searching the Tree
      0m
    • Navigating options
      0m
    • Modifying the Tree
      0m
    • Parsing Only Part of the Document
      0m
    • Printing and Formatting
      0m
    • Encoding
      0m
  • Python integration with Hadoop, MapReduce and Spark

    • Need for Integrating Python with Hadoop
      0m
    • Big Data Hadoop Architecture
      0m
    • MapReduce
      0m
    • Cloudera QuickStart VM Set Up
      0m
    • Apache Spark
      0m
    • Resilient Distributed Systems (RDD)
      0m
    • PySpark
      0m
    • Spark Tools
      0m
    • PySpark Integration with Jupyter Notebook
      0m

Instructors

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A Technical expert and a passionate trainer has expertise in the field of Internet of Things and Machine Learning, Technical Training and Project Management, he has a proven work record of delivering Technical Training in various technologies and domains at the premier organizations.. He has delivered 50+ corporate Training to clients from India and abroad.

INTERNET OF THINGS
• Corporate Clients – UST Global, mphasis, Accenture, UST Global, CompuCom etc,
• Guest Speaker at 3+ International Conferences and Summits on IoT, Smart City Development and Technological Advancements.
• Designed Course material for online courses on IoT, AI & Machine Learning
• Worked 3+ Industrial IoT Projects

ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
• Corporate Clients – Kronos, HARMAN, mphasis, Accenture, Ericsson, UTC etc,
• Delivered Online training on Machine Learning, Artificial Intelligence and Internet of things to clients from Saudi Countries, South Africa, Bangladesh & US
• Delivered 100+ Lectures across pan India including IIT Roorkee, IIT Kanpur, IIT Jodhpur, IIT Chennai, IIT Bhubaneswar, NIT Sikkim, NIT Surathkal etc.
• Machine learning techniques and algorithms: Good practical experience on complex data for recommendation process, churn and classification, using Random forest, SVM, K-NN, Neural Network, and other decision tree techniques (CHAID, CART)

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