Artificial Intelligence Training in Electronic City Bangalore | AI Course in Electronic City Bangalore

Curriculum

39 Sections
148 Lessons
60 Hours

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Introduction to Data Science
5
- 1.1
  What is Data Science?
- 1.2
  What is Machine Learning?
- 1.3
  What is Deep Learning?
- 1.4
  What is AI?
- 1.5
  Data Analytics & its types
Introduction to Python
5
- 2.1
  What is Python?
- 2.2
  Why Python?
- 2.3
  Installing Python
- 2.4
  Python IDEs
- 2.5
  Jupyter Notebook Overview
Python Basics
11
- 3.1
  Python Basic Data types
- 3.2
  Lists
- 3.3
  Slicing
- 3.4
  IF statements
- 3.5
  Loops
- 3.6
  Dictionaries
- 3.7
  Tuples
- 3.8
  Functions
- 3.9
  Array
- 3.10
  Selection by position & Labels
- 3.11
  Hands-on
Python Packages
5
- 4.1
  Pandas
- 4.2
  Numpy
- 4.3
  Sci-kit Learn
- 4.4
  Mat-plot library
- 4.5
  Hands-on
Importing Data
5
- 5.1
  Reading CSV files
- 5.2
  Saving in Python data
- 5.3
  Loading Python data objects
- 5.4
  Writing data to CSV file
- 5.5
  Hands-on
Manipulating Data
7
- 6.1
  Selecting rows/observations
- 6.2
  Rounding Number
- 6.3
  Selecting columns/fields
- 6.4
  Merging data
- 6.5
  Data aggregation
- 6.6
  Data munging techniques
- 6.7
  Hands-on
Statistics Basics
0
Central Tendency
5
- 8.1
  Mean
- 8.2
  Median
- 8.3
  Mode
- 8.4
  Skewness
- 8.5
  Normal Distribution
Probability Basics
3
- 9.1
  What does mean by probability?
- 9.2
  Types of Probability
- 9.3
  ODDS Ratio?
Standard Deviation
2
- 10.1
  Data deviation & distribution
- 10.2
  Variance
Bias variance Trade off
2
- 11.1
  Underfitting
- 11.2
  Overfitting
Distance metrics
2
- 12.1
  Euclidean Distance
- 12.2
  Manhattan Distance
Outlier analysis
7
- 13.1
  What is an Outlier?
- 13.2
  Inter Quartile Range
- 13.3
  Box & whisker plot
- 13.4
  Upper Whisker
- 13.5
  Lower Whisker
- 13.6
  Scatter plot
- 13.7
  Cook’s Distance
Missing Value Treatment
4
- 14.1
  What is a NA?
- 14.2
  Central Imputation
- 14.3
  KNN imputation
- 14.4
  Dummification
Correlation
2
- 15.1
  Pearson correlation
- 15.2
  positive & Negative correlation
Hands-on
0
Error Metrics
0
Classification
5
- 18.1
  Confusion Matrix
- 18.2
  Precision
- 18.3
  Recall
- 18.4
  Specificity
- 18.5
  F1 Score
Regression
3
- 19.1
  MSE
- 19.2
  RMSE
- 19.3
  MAPE
Hands-on
0
Machine Learning
0
Supervised Learning
0
Linear Regression
4
- 23.1
  Linear Equation
- 23.2
  Slope
- 23.3
  Intercept
- 23.4
  R square value
Logistic regression
5
- 24.1
  ODDS ratio
- 24.2
  Probability of success
- 24.3
  Probability of failure Bias Variance Tradeoff
- 24.4
  ROC curve
- 24.5
  Bias Variance Tradeoff
Hands-on
0
Unsupervised Learning
4
- 26.1
  K-Means
- 26.2
  K-Means ++
- 26.3
  Hierarchical Clustering
- 26.4
  Hands-on
SVM
5
- 27.1
  Support Vectors
- 27.2
  Hyperplanes
- 27.3
  2-D Case
- 27.4
  Linear Hyperplane
- 27.5
  Hands-on
SVM Kernel
4
- 28.1
  Linear
- 28.2
  Radial
- 28.3
  Polynomial
- 28.4
  Hands-on
Other Machine Learning algorithms
6
- 29.1
  K – Nearest Neighbour
- 29.2
  Naive Bayes Classifier
- 29.3
  Decision Tree – CART
- 29.4
  Decision Tree – C50
- 29.5
  Random Forest
- 29.6
  Hands-on
ARTIFICIAL INTELLIGENCE
0
AI Introduction
6
- 31.1
  Perceptron
- 31.2
  Multi-Layer perceptron
- 31.3
  Markov Decision Process
- 31.4
  Logical Agent & First Order Logic
- 31.5
  AL Applications
- 31.6
  Hands-on
Deep Learning
0
Deep Learning Algorithms
4
- 33.1
  CNN – Convolutional Neural Network
- 33.2
  RNN – Recurrent Neural Network
- 33.3
  ANN – Artificial Neural Network
- 33.4
  Hands-on
Introduction to NLP
7
- 34.1
  Text Pre-processing
- 34.2
  Noise Removal
- 34.3
  Lexicon Normalization
- 34.4
  Lemmatization
- 34.5
  Stemming
- 34.6
  Object Standardization
- 34.7
  Hands-on
Text to Features (Feature Engineering)
11
- 35.1
  Syntactical Parsing
- 35.2
  Dependency Grammar
- 35.3
  Part of Speech Tagging
- 35.4
  Entity Parsing
- 35.5
  Named Entity Recognition
- 35.6
  Topic Modelling
- 35.7
  N-Grams
- 35.8
  TF – IDF
- 35.9
  Frequency / Density Features
- 35.10
  Word Embedding’s
- 35.11
  Hands-on
Tasks of NLP
6
- 36.1
  Text Classification
- 36.2
  Text Matching
- 36.3
  Levenshtein Distance
- 36.4
  Phonetic Matching
- 36.5
  Flexible String Matching
- 36.6
  Hands-on
Computer Vision
5
- 37.1
  Image Processing Basics
- 37.2
  OpenCV
- 37.3
  Face Detection & Recognition
- 37.4
  Object Detection
- 37.5
  AI Vision Projects
AI Project & Deployment
4
- 38.1
  End-to-End AI Project
- 38.2
  Real-time Use Case Implementation
- 38.3
  Model Deployment (Flask / FastAPI)
- 38.4
  Cloud Basics (AWS / Azure – Intro)
Capstone Projects (Real-Time)
4
- 39.1
  AI Chatbot
- 39.2
  Recommendation System
- 39.3
  Face Recognition System
- 39.4
  Predictive Analytics Project

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