Machine Learning Training in Chennai | Machine Learning Course in Chennai

30 SEATS

COURSE INSTRUCTOR

Venkatesh

Machine Learning

Expert in Machine Learning.More than 4 year

BASIC INFORMATION

Lessons : 12
Length : 4 Months
Level : Basic
Category : Software Training

Started : 01-04-2019
Shift : 02
Class : 120

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Machine Learning Training and Course Description

We will walk you through all the aspects of machine learning from simple linear regressions to the latest neural networks, and you will learn not only how to use them but also how to build them from scratch. Big part of this path is oriented on Computer Vision(CV), because it’s the fastest way to get general knowledge, and the experience from CV can be simply transferred to any ML area. We will use TensorFlow as a ML framework, as it is the most promising and production ready. Learning will be better if you work on theoretical and practical materials at the same time to get practical experience on the learned material.

Machine Learning Training and Course Syllabus

Module 1: Data Science Overview

Get introduced to Data Science with Python
Sectors of Python
Components of Python

Module 2: Data Analytics Overview

Know about Exploratory Data Analysis(EDA) techniques
Data types for plotting

Module 3: Statistical Analysis and Business Applications

Learn about statistical and non-statistical analysis
Categories and processes of statistics
Data distribution and dispersion
Histogram
Testing

Module 4: Python Environment Setup and Essentials

How to install Anaconda Python Distribution?
How to work with Python Data Types

Module 5: Mathematical Computing with Python

Comprehend mathematical functions of Numpy

Module 6: Scientific Computing with Python

Acquire knowledge on SciPy sub package

Module 7: Data Manipulation with Pandas

Understand Pandas SQL operation

Module 8: Machine Learning with Scikit

Get a knowhow on Scikit
Supervised Learning Model Considerations
Supervised Learning Models
Unsupervised Learning Models
Pipeline

Module 9: Natural Language Processing with Scikit

Gain overview
Applications
Libraries of NLP

Module 10: Data Visualization in Python

Introduction to Data Visualization
Line Properties
Decision Boundary
Types of Plots

Module 11: Web Scraping with Beautiful Soup

Web Scraping and Parsing
Navigating options
Modifying the Tree

Module 12: Python integration with Hadoop MapReduce and Spark

Understand Why Big Data Solutions Are Provided for Python
Hadoop Core Components
Python Integration with Spark using PySpark

Module 13: Introduction to Artificial Intelligence and Machine Learning

Get introduced to Machine Learning concepts
logarithms
its applications

Module 14: Techniques of Machine Learning

supervised
unsupervised
semi-supervised
reinforced machine learning techniques

Module 15: Data Preprocessing

Comprehend the meaning
process
importance of data preparation
feature engineering and scaling
datasets
dimensionality reduction

Module 16: Math Refresher

Overview of Linear Algebra
Eigenvalues
Eigenvectors
Eigen-decomposition
Calculus
Probability and Statistics

Module 17: Regression

Know Linear Regression
Equations and Algorithms in this lesson

Module 18: Classification

Gain knowledge on classification types such as SVM
KNN
Naive Bayes
decision tree
random forest
logistic regression
k-nearest neighbours
support vector machines

Module 19: Unsupervised learning - Clustering

Clustering definition
Clustering algorithms
prototype-based clustering
K-means clustering example

Module 20: Introduction to Deep Learning

Understand the meaning
importance of deep learning
Artificial Neural networks
TensorFlow

COURSE INSTRUCTOR

Venkatesh

BASIC INFORMATION

Machine Learning Training and Course Description

Machine Learning Training and Course Syllabus

Module 1: Data Science Overview

Module 2: Data Analytics Overview

Module 3: Statistical Analysis and Business Applications

Module 4: Python Environment Setup and Essentials

Module 5: Mathematical Computing with Python

Module 6: Scientific Computing with Python

Module 7: Data Manipulation with Pandas

Module 8: Machine Learning with Scikit

Module 9: Natural Language Processing with Scikit

Module 10: Data Visualization in Python

Module 11: Web Scraping with Beautiful Soup

Module 12: Python integration with Hadoop MapReduce and Spark

Module 13: Introduction to Artificial Intelligence and Machine Learning

Module 14: Techniques of Machine Learning

Module 15: Data Preprocessing

Module 16: Math Refresher

Module 17: Regression

Module 18: Classification

Module 19: Unsupervised learning - Clustering

Module 20: Introduction to Deep Learning

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Machine Learning Training and Course in Chennai

COURSE INSTRUCTOR

Venkatesh

BASIC INFORMATION

Machine Learning Training and Course Description

Machine Learning Training and Course Syllabus

Module 1: Data Science Overview

Module 2: Data Analytics Overview

Module 3: Statistical Analysis and Business Applications

Module 4: Python Environment Setup and Essentials

Module 5: Mathematical Computing with Python

Module 6: Scientific Computing with Python

Module 7: Data Manipulation with Pandas

Module 8: Machine Learning with Scikit

Module 9: Natural Language Processing with Scikit

Module 10: Data Visualization in Python

Module 11: Web Scraping with Beautiful Soup

Module 12: Python integration with Hadoop MapReduce and Spark

Module 13: Introduction to Artificial Intelligence and Machine Learning

Module 14: Techniques of Machine Learning

Module 15: Data Preprocessing

Module 16: Math Refresher

Module 17: Regression

Module 18: Classification

Module 19: Unsupervised learning - Clustering

Module 20: Introduction to Deep Learning

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