## Machine Learning Certification Training using Python

## Secure enrollment now

## 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

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# 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)