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Artificial Intelligence Course Details
 

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Batch Date: Feb 20th @9:30PM

Faculty: Mrs. Sasmitha
(10+ Yrs of Exp,..)

Duration: 75 Days

Venue :
DURGA SOFTWARE SOLUTIONS,
Flat No : 202, 2nd Floor,
HUDA Maitrivanam,
Ameerpet, Hyderabad - 500038

Ph.No: +91 - 9246212143, 80 96 96 96 96


Syllabus:

Artificial Intelligence, MLOPS,
AWS Industry Ready Program

PYTHON Programming

Python Basics

  • Variable, print(), Taking input from User
  • Data Types (List , Tuple , Set , Dictionary , String)
  • Control Statement and Loops (lf Else, While, For)
  • Functions, Special functions lambda,map, filter, recursion
  • Python Practice Set-1 (15 Questions)

Python Advance

  • File handling(Opening,reading,writing,editing,with statements)
  • Exception Handling(Try,Except,Finally,Raising Exceptions,Asertion)
  • Object Oriented Programming(Class,Object, Method,Module,Packages)
  • Inheritance
  • Python Practice Set-2 (15 Questions)

Python For DataScience- Pandas

  • Data Frame Basics ,Read-write
  • Grouping, Merging , Joining and Concatenating Data
  • sorting, Handling Missing Values
  • Python Practice Set-3 (15 Questions)

Python For DataScience- Numpy

  • Creating Arrays
  • Array methods
  • Basic Math operations on Arrays
  • Python Practice Set-4 (15 Questions)

Python For DataScience- Plotly

  • Scatter Plot, Histogram, Line Plot, Area Plot, Box Plot
  • Bubble Chart, Bar Plot , Sunburst Chart
  • Tree Map, Heat Map, Customizing Plots
  • Python Practice Set-5 (15 Questions)

TABLEAU

Project-1 (Company Sales Dashboard)

  • Installation, Download Drivers and Connect, Start Page, Navigation
  • Connect to data Source, Import Excel File, Join Data Bases, Join Files
  • Creating, Adding, Renaming, Duplicating Worksheet
  • Calculations, sort and filter data, Different Charts,
  • Create Dashboards, Filters, Create Stories
  • Project-1 (Company Sales Dashboard)

MACHINE LEARNING

STATISTICS

  • Central limit theorem, Correlation
  • R, R Square, Adj R Square
  • Variance, Standard Deviation, Quartiles, Inter Quartile Range
  • Z Score, Normal Distribution
  • Probability Practice Set (15 Questions)

DATA CLEANING

  • Data Normalization, Data Standardization, Missing Value Treatment
  • Multi Collinearity
  • Outliers Detection and Removal
  • Feature Selection Techniques,Handling Class imbalance Problems
  • Project-2 Machine Failure Prediction

REGRESSION

  • Linear Regression - Know the Math behind
  • Right Fit, Underfit, Over fit
  • Validation Technique (RMSE, MSE, MAE)
  • Project-3 Admission Probability Prediction

CLUSTERING

  • KMeans - Know the Math behind
  • Project-5 Document Clustering

DEEP LEARNING

NEURAL NETWORK (ANN)

  • Perceptron, Activation Functions
  • Artificial Neural Network Architecture
  • ANN Learning- Know the Math Behind
  • Project-6 Stock market Prediction

NLP

TEXT MINING

  • Web Scraping using beutifulsoup, Selenium
  • Text Data Preprocessing, Stemming, Lemmatization
  • Word embedding techniques- count vectorizer, tf-idf vectorizer
  • Regular expression
  • Project-7 Web Scraping

MLOPS

MI-FLOW, DOCKER, GITHUB

  • Model registry, Model Tracking
  • Concept drift, Data drift
  • Version control, Containerization

CLOUD COMPUTING

AWS

  • Storage Services - S3
  • Compute Services
  • AWS sage maker, Deployment on AWS

PORTFOLIO BUILDING

  • Project-8 -->
    End to End Deployment (collecting data from Database Cleaning Data ,Visualizing Data, Building Model, Validating and Deploying Models on AWS