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Data Science for AI and Machine Learning Using Python and R
Data Science for AI and Machine Learning Using Python and R
Duration
8 Weeks
Mode of Training
Classroom/Online
Level
Advanced
You may also see course Curriculum
Data Science for AI and Machine Learning Using Python and R
ABC’s of Data Analytics
Data Science
Data Scientist
How does a Data Scientist differ
Data - Intelligence - Insights
Data Scientist Are Found at the Confluence
Data Products
Data Analysis
Data Mining
Machine Learning
Learning Techniques
Supervised Learning
Unsupervised Learning
Analytics
Analytics Value Chain
What is Data?
Basis of data Categorization?
Types of data
Binary data
Nominal data
Ordinal data
Discrete and Continues data
Raw data
Processed data
Data Collection Types
Forms of Data
Sources of Data
Data Quality Issues
Big Data Landscape
5 Vs of Big Data
Big Data : The Challenges
Tools of Data Processing
Anomaly Detection
Life Cycle Models
Team Data Science Process (TDSP) – Agile
Model
Cross Industry Standard Process for Data Mining (CRISP-DM).
Knowledge Discovery In Database (KDD)
Exploratory Data Analysis (EDA)
Missing Data
Noisy Data
Inconstant Data
Univariate, Bivariate and Multivariate
Analysis.
Data Cleaning – Preprocessing
Data Preparation
Why is Data Preprocessing used?
How is Data Preprocessing performed?
Tasks of Data Preprocessing
Data Profiling
Data Wrangling
Why Data Wrangling is used?
How to Check Presence of Data Leakage?
How is Data wrangling performed?
Tasks of Data Wrangling
Balanced and Imbalanced data
Re-Sampling Data
Random Under-Sampling
Random Over-Sampling
Imbalanced Data Handling Techniques
SMOTE
ADASYN
Near Miss Algorithm
Random Forest
Simple Ensemble Methods
o Max-Voting
o Averaging
o Weighted Averaging
Advanced Ensemble Methods
Bagging
Boosting - ADA ,XG
Stacking
Navy Bayes
Time Series Analysis
Stationary series
Trend
Seasonality : ACF,PACF
ARIMA Model
Feature Engineering
Metrics
Dimensionality Reduction
Deployment
Statistics
Machine Learning Algorithms
Artificial Intelligence
Recommender Systems
Natural Language Processing (NLP)
Audio Data Analytics
Computer Vision – OpenCV
Computer Vision – OpenCV
R –Programming
Introduction
Installation
Data Types
Data Structures and Vectors
Data Frames
List & Arrays & Matrix
Factors
Packages
Importing Data Files
Exporting & Reading Data in R
Conditional statements
Loops
Functions
Python Programming
Introduction
Installation
Object Oriented Programming Concepts
Syntax
Variables
Data types and Comments
Data structure
Exporting & Reading Data in Python
Conditional statements
Loops - while,foor,break,repeat,next statements
Data frame
Object and Class
Libraries
Importing Data Files
Functions
Debugging python programs
Scientific Computing
Introduction to the Theano library.
Introduction to the TensorFlow library.
Introduction to the Keras library.
Neural Network With Keras.
Case Studies