29/10/2020
ARTIFICIAL INTELLIEGENCE
Get transition in AI with Future Institute of Education.
This is a great opportunity to dive into the modern technology
Contents for Data Science: -
Full Data Science course duration – 100 hrs
Data analytics course(R,STATISTICS,TABLEAU)- 60hrs
Introduction to Data Science
What is Data Science?
What is Data Analytics?
What is the need of Data Science?
Life cycle of Data Science.
R for Data Science(15*1hr= 15hrs)
Introduction to R.
Installation of R Studio.
Data types in R.
File types in R.
Connecting to database.
Operators, loops, conditional statements in R.
Functions in R
Web scraping in R.
Visualization techniques in R.
Statistics For Data Science(30*1hr=30hrs)
What is statistics?
Types of statistics methods.
What is descriptive and inferential statistics?
Types of descriptive statistics?
Types of inferential statistics?
Types of Probability distribution
Types of data.
Types of variable.
Central limit theorem
ANOVA
Z-test
T-test
F-test
Each topic has problem statement.
Online test on statistics.
Tableau for data science(5*1hr = 5hrs)
Introduction to tableau
Installation of tableau
Various visualization techniques in tableau.
Making story line in tableau.
Project in tableau.
Python for Data Science(15*1hr=15hrs)
Introduction to python.
Installation of anaconda.
Data types in python
File types in python
List, set, tuple, dictionary in python
Loops, break, continue and conditional statements in python.
Pass, date and time in python.
Various keywords in python.
Functions, syntax in python.
Web scrapping in python.
NumPy, Pandas in python
Visualization techniques in python (like- Matplotlib, seaborn)
Online test in python and statistics.
Machine Learning.
(10 * 1hr= 10hrs)
What is machine learning.
Various machine learning techniques.
Difference between supervised and unsupervised techniques.
Classification of machine learning techniques.
Various supervised algorithm
Simple Linear regression
Multi Linear regression
Ridge and lasso regression.
Elastic net regression.
Logistic regression.
K nearest neighbors.
Naïve Bayes.
Decision tree.
Random forest.
Support vector machine.
Online test.
Implementation in project for each algorithm.
Natural Language Processing(5*1hr = 5hrs)
Introduction to NLP
Various analysis in NLP
Text preprocessing in NLP (stemming, lemmatization, Tokenization ..etc)
Various techniques in NLP (vectorizer, parser, word2vector, Bag of Words ..etc )
Audio procession in NLP
Online test
End to end project implementation in NLP.
Neural Networks (5*1hr = 5hrs)
Introduction to neural networks.
Discuss about various techniques.
Single layer perceptron.
Multi-layer perceptron.
Different MLP algorithms.
ANN, CNN, RNN
Different functions in different techniques.
Different layers in neural network.
Activation functions in neural networks.
Image processing and audio processing using neural network.
Project implementation in NLP using neural network.
Computer vision (5*1hr =5hrs)
Introduction to computer vision.
Use of OpenCV
Various techniques in OpenCV
Implementation project on open cv
Project on open cv using neural network.
