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Machine Learning with Python

Course Summary

In recent years, Python has become most preferred language for Data Science and considered as powerful and flexible platform for building Machine Learning systems.

Python with Machine Learning is designed to provide in depth knowledge on building Machine Learning algorithms from bottom up with Python.


Course Objectives

  • Introduction to Python for Machine Learning
  • Machine Learning algorithm overview
  • Hands-on coding of popular ML algorithms with Python
  • Building Machine Learning applications with Python package Scikit-Learn
  • Assess the knowledge through the International Association of Business Analytics (IABAC™) framework

Course Outline

Python for Machine Learning

  • Programming Basics
  • Python Data Types
  • Structures and conditional statements
  • Python core packages
  • Introduction to Jupyter Notebook
  • Introduction to Numpy and Pandas
  • Data filtering and selecting
  • Find duplicates and treating missing values
  • Concatenate and transform data

Basic Statistics for Machine Learning

  • Basic Statistics and Exploratory Analysis
  • Descriptive summary statistics with Numpy
  • Summarize continous and categorical data
  • Outlier analysis

Introduction to Machine Learning

  • Overview of Supervised and Unsupervised Machine Learning
  • Linear Regression
  • Clustering with K-means
  • Naive Bayes Classification
  • Introduction to Neural Networks

Data Processing for Machine Learning

  • Advanced Data Mugging
  • Outlier Analysis
  • Treating for missing values
  • Normalization vs Standardization of data

Machine Learning Algorithms

  • Supervised Machine Learning algorithms
  • K-Nearest Neighbors (KNN) concept and application
  • Naive Bayes concept and application
  • Logistic Regression concept and application
  • Classification Trees concept and application
  • Unsupervised Machine Learning algorithms
  • Clustering with K-means concept and application
  • Hierarchial Clustering concept and application

Building and Training Machine Learning models

  • Setting up the project with ML workflow.
  • Data Preprocessing and statistical exploration
  • Building , Training and evaluation of Machine Learning Model

Hands on case study: Property Estimations with ML

  • Analysis the case study: Property Estimations
  • Setting up the project with ML workflow
  • Data Preprocessing and statistical exploration
  • Choosing the Machine Learning Algorithm for this case
  • Building, Training and evaluation of Machine Learning Model

Target Audience

This course is an intermediate-level course, so most of the aspiring Machine Learning candidates can opt for this course

  • Professionals aspiring to pursue career in Machine Learning or Data Science in general
  • Fresh colleague graduates, who are looking to career options in Data Science
  • Candidates pursuing Data Scientist tracks

Prerequisite

  • Basic programming skills recommended
  • Basic Statistics Knowledge is recommended
  • Hands-on coding of popular ML algorithms on classic data sets

Course Objectives

  • Introduction to Python for Machine Learning
  • Machine Learning algorithm overview
  • Hands-on coding of popular ML algorithms with Python
  • Building Machine Learning applications with Python package Scikit-Learn
  • Assess the knowledge through the International Association of Business Analytics (IABAC™) framework

Course Outline

Python for Machine Learning

  • Programming Basics
  • Python Data Types
  • Structures and conditional statements
  • Python core packages
  • Introduction to Jupyter Notebook
  • Introduction to Numpy and Pandas
  • Data filtering and selecting
  • Find duplicates and treating missing values
  • Concatenate and transform data

Basic Statistics for Machine Learning

  • Basic Statistics and Exploratory Analysis
  • Descriptive summary statistics with Numpy
  • Summarize continous and categorical data
  • Outlier analysis

Introduction to Machine Learning

  • Overview of Supervised and Unsupervised Machine Learning
  • Linear Regression
  • Clustering with K-means
  • Naive Bayes Classification
  • Introduction to Neural Networks

Data Processing for Machine Learning

  • Advanced Data Mugging
  • Outlier Analysis
  • Treating for missing values
  • Normalization vs Standardization of data

Machine Learning Algorithms

  • Supervised Machine Learning algorithms
  • K-Nearest Neighbors (KNN) concept and application
  • Naive Bayes concept and application
  • Logistic Regression concept and application
  • Classification Trees concept and application
  • Unsupervised Machine Learning algorithms
  • Clustering with K-means concept and application
  • Hierarchial Clustering concept and application

Building and Training Machine Learning models

  • Setting up the project with ML workflow.
  • Data Preprocessing and statistical exploration
  • Building , Training and evaluation of Machine Learning Model

Hands on case study: Property Estimations with ML

  • Analysis the case study: Property Estimations
  • Setting up the project with ML workflow
  • Data Preprocessing and statistical exploration
  • Choosing the Machine Learning Algorithm for this case
  • Building, Training and evaluation of Machine Learning Model

Target Audience

This course is an intermediate-level course, so most of the aspiring Machine Learning candidates can opt for this course

  • Professionals aspiring to pursue career in Machine Learning or Data Science in general
  • Fresh colleague graduates, who are looking to career options in Data Science
  • Candidates pursuing Data Scientist tracks

Prerequisite

  • Basic programming skills recommended
  • Basic Statistics Knowledge is recommended
  • Hands-on coding of popular ML algorithms on classic data sets

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