Machine Learning Certification Training using Python

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Syllabus

Introduction to Data Science

 

  • What is Data Science?

  • What does Data Science involve?

  • Era of Data Science

  • Business Intelligence vs Data Science

  • Life cycle of Data Science

  • Tools of Data Science

  • Introduction to Python


Data Extraction, Wrangling, & Visualization


  • Data Analysis Pipeline

  • What is Data Extraction

  • Types of Data

  • Raw and Processed Data

  • Data Wrangling

  • Exploratory Data Analysis

  • Visualization of Data

 

Introduction to Machine Learning with Python

 

  • Python Revision (numpy, Pandas, scikit learn, matplotlib)

  • What is Machine Learning?

  • Machine Learning Use-Cases

  • Machine Learning Process Flow

  • Machine Learning Categories

  • Linear regression

  • Gradient descent

  • Supervised Learning - I

  • What is Classification and its use cases?

  • What is Decision Tree?

  • Algorithm for Decision Tree Induction

  • Creating a Perfect Decision Tree

  • Confusion Matrix

  • What is Random Forest?


Dimensionality Reduction


  • Introduction to Dimensionality

  • Why Dimensionality Reduction

  • PCA

  • Factor Analysis

  • Scaling dimensional model

  • LDA


Supervised Learning - II


  • What is Naïve Bayes?

  • How Naïve Bayes works?

  • Implementing Naïve Bayes Classifier

  • What is Support Vector Machine?

  • Illustrate how Support Vector Machine works?

  • Hyperparameter optimization

  • Grid Search vs Random Search

  • Implementation of Support Vector Machine for Classification


Unsupervised Learning


  • What is Clustering & its Use Cases?

  • What is K-means Clustering?

  • How K-means algorithm works?

  • How to do optimal clustering

  • What is C-means Clustering?

  • What is Hierarchical Clustering?

  • How Hierarchical Clustering works?


Association Rules Mining and Recommendation Systems


  • What are Association Rules?

  • Association Rule Parameters

  • Calculating Association Rule Parameters

  • Recommendation Engines

  • How Recommendation Engines work?

  • Collaborative Filtering

  • Content Based Filtering


Reinforcement Learning


  • What is Reinforcement Learning

  • Why Reinforcement Learning

  • Elements of Reinforcement Learning

  • Exploration vs Exploitation dilemma

  • Epsilon Greedy Algorithm

  • Markov Decision Process (MDP)

  • Q values and V values

  • Q – Learning

  • α values


Time Series Analysis


  • What is Time Series Analysis?

  • Importance of TSA

  • Components of TSA

  • White Noise

  • AR model

  • MA model

  • ARMA model

  • ARIMA model

  • Stationarity

  • ACF & PACF


Model Selection and Boosting


  • What is Model Selection?

  • Need of Model Selection

  • Cross – Validation

  • What is Boosting?

  • How Boosting Algorithms work?

  • Types of Boosting Algorithms

  • Adaptive Boosting

Complimentary sessions on communication presentation and leadership skills.

Benefits from the course

Mode of Teaching

Live Interactive

Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms.

This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.After completing this Machine Learning Certification Training using Python, you should be able to:

  • Gain insight into the 'Roles' played by a Machine Learning Engineer
  • Automate data analysis using python
  • Describe Machine Learning
  • Work with real-time data
  • Learn tools and techniques for predictive modeling
  • Discuss Machine Learning algorithms and their implementation
  • Validate Machine Learning algorithms
  • Explain Time Series and it’s related concepts
  • Gain expertise to handle business in future, living the present

 

Prerequisite:

 

  • The pre-requisites for the Machine Learning Certification Training using Python includes development experience with Python.
  • Fundamentals of Data Analysis practised over any of the data analysis tools like SAS/R will be a plus. However, Python would be more advantageous.
  • You will be provided with complimentary “Python Statistics for Data Science Course” as a self-paced course once you enrol for the course.

Prerequisites

  • The pre-requisites for the Machine Learning Certification Training using Python includes development experience with Python.
  • Fundamentals of Data Analysis practised over any of the data analysis tools like SAS/R will be a plus. However, Python would be more advantageous.
  • You will be provided with complimentary “Python Statistics for Data Science Course” as a self-paced course once you enrol for the course.

Course Duration:

36 Hours

Class Hours:

2 Hours Day time slots or 3 Hours week end Slots (May change)

Video Clip

Address

 

Kerala State Rutronix

C-11, Padmasree,

Elankom Gardens
Vellayambalam,

Sasthamangalam P.O 
Thiruvananthapuram - 695010

Kerala, India

e-mail: md@keralastaterutronix.com

        

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