Data Science & AI Online Training | Data Science & AI Online Course

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Data Science & AI

Course Overview

Explore Data Science online training with real-world examples taught by industry experts. Learn key concepts using Hadoop, R programming, and machine learning. Gain insights into big data, R analytical tools, and dataset analysis. Develop skills in data transformation, visualization, and exploratory analysis. This course covers the basics of Data Science for practical applications

Description

What is Data science?

Data science is the systematic application of scientific methods and mathematical principles to efficiently process, analyze, and extract meaningful insights from data. It leverages powerful hardware, sophisticated algorithms, and software programs to transform raw data into valuable knowledge, facilitating informed decision-making in commercial and professional contexts.

Course Objectives

Upon finishing the Data Science Online Course at Naresh i Technologies, you will gain expertise and eligibility in the following:

Comprehensive understanding of Data Science.
Proficiency in analyzing big data.
Competence in Data Mining.
Skill in working with Statistics.
Proficiency in utilizing various tools such as Tableau and MapReduce.
Ability to create decision trees.
Exploration of Big Data concepts.

This course equips you with the knowledge and skills needed to excel in the field of Data Science, covering a spectrum of topics from data analysis to the application of different tools for effective decision-making.

Prerequisites

This course is suitable for any IT professional with a basic understanding of:
- Mathematics
- Statistics
- Any Programming Language
No advanced knowledge is necessary, making it accessible for individuals with a foundational understanding of these concepts.

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Course Curriculum

Introduction to Data Science
Python – Basics
Python - Data Types & Utilities
Set
Tuple
Dictionary and Dictionary comprehension
Functions
Packages
Map Reduce
OOPs
Class & Object
Methods
Python Decorator
Polymorphism
Python - Production Level
Pickling & Unpickling
Pandas
Numpy
Matplotlib
Seaborn
Scipy
Statsmodels

Set Theory
Combinatorics
Probability
Linear Algebra
Euclidean Distance & Manhattan Distance
Calculus
Indices & Logarithms

Introduction
Types of Data
Measures of Central Tendency
Descriptive statistic Measures of symmetry
Measurement of Spread
Measurement of Spread
Levels of Data Measurement
Variable
Frequency Distribution Table
Types of Variables
Frequency Distribution Table
Correlation, Regression & Collinearity
Others
Bias and Variance in ML
Entropy in ML
Information Gain
Surprise in ML
Loss Function & Cost Function
Inferential Statistics

Introduction
Keys
Constraints
CRUD Operations
SQL Languages
SQL Commands
Operators
Wild Cards
Aggregate Functions
SQL Joins

EDA
Data Visualisation
ML Introduction
Important Element of Machine Learning
Multiclass Classification
Data Processing
Regression
Evaluation Metrics for Regression
Classification
Clustering
Introduction
K-Means Clustering
Hierarchical Clustering
Agglomerative clustering
DBSCAN Clustering
Association Rules
Recommendation Engines
Time Series & Forecasting
Model Selection & Evaluation
Over Fitting & Under Fitting
Others
ML Pipeline
ML Model Deployment in Flask

Introduction
Basic Report Design
Visual Sync, Grouping
Hierarchies, Filters
Power Query
DAX Functions

Deep learning at Glance
Training MLP: Backpropagation
Cost Function
Gradient Descent Backpropagation - Vanishing and Exploding Gradient
Problem
Introduce to Py-Torch
Regularization
Optmizers
Hyperparameters and tuning of the same
TensorFlow Framework
ANN (Artificial Neural Network)
Py-Torch Library
RNN (Recurrent Neural Network)
Basics of Image Processing
Convolutional Neural Networks (CNN)

Introduction to Natural Language Processing (NLP)
Document Vectorization
Twitter Sentiment Analysis Using Textblob
Spacy Library

Human vision vs Computer vision
Image Processing with OpenCV
Video Processing with OpenCV
Reinforcement Learning
OPEN AI
Time Series and Forecasting
MakerSuite Google
Azure ML

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Who can learn this course

IT professionals seeking to advance their career in Development or Data Science.
College students pursuing B.E, B.Tech, BSC, MCA, M.Sc Computers, M.Tech, BCA, or BCom in any stream.
Recent graduates keen on enhancing their skills.

This course is suitable for a broad audience, providing valuable knowledge and practical skills for both experienced IT professionals and individuals starting their career journey.