Artificial Intelligence Certification Training Course → Dependency Grammar

Curriculum

21 Sections
150 Lessons
60 Hours

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Introduction to Data Science
5
- 1.0
  What is Data Science?
- 1.1
  What is Machine Learning?
- 1.2
  What is Deep Learning?
- 1.3
  What is AI?
- 1.4
  Data Analytics & its types
Introduction to Python
5
- 2.0
  What is Python?
- 2.1
  Why Python?
- 2.2
  Installing Python
- 2.3
  Python IDEs
- 2.4
  Jupyter Notebook Overview
Python Basics
11
- 3.0
  Python Basic Data types
- 3.1
  Lists
- 3.2
  Slicing
- 3.3
  IF statements
- 3.4
  Loops
- 3.5
  Dictionaries
- 3.6
  Tuples
- 3.7
  Functions
- 3.8
  Array
- 3.9
  Selection by position & Labels
- 3.10
  Hands-on
Python Packages
5
- 4.0
  Pandas
- 4.1
  Numpy
- 4.2
  Sci-kit Learn
- 4.3
  Mat-plot library
- 4.4
  Hands-on
Importing Data
5
- 5.0
  Reading CSV files
- 5.1
  Saving in Python data
- 5.2
  Loading Python data objects
- 5.3
  Writing data to CSV file
- 5.4
  Hands-on
Manipulating Data
7
- 6.0
  Selecting rows/observations
- 6.1
  Rounding Number
- 6.2
  Selecting columns/fields
- 6.3
  Merging data
- 6.4
  Data aggregation
- 6.5
  Data munging techniques
- 6.6
  Hands-on
Statistics Basics
36
- 7.0
  Central Tendency
- 7.1
  Mean
- 7.2
  Median
- 7.3
  Mode
- 7.4
  Skewness
- 7.5
  Normal Distribution
- 7.6
  Probability Basics
- 7.7
  What does mean by probability?
- 7.8
  Types of Probability
- 7.9
  ODDS Ratio?
- 7.10
  Standard Deviation
- 7.11
  Data deviation & distribution
- 7.12
  Variance
- 7.13
  Bias variance Trade off
- 7.14
  Underfitting
- 7.15
  Overfitting
- 7.16
  Distance metrics
- 7.17
  Euclidean Distance
- 7.18
  Manhattan Distance
- 7.19
  Outlier analysis
- 7.20
  What is an Outlier?
- 7.21
  Inter Quartile Range
- 7.22
  Box & whisker plot
- 7.23
  Upper Whisker
- 7.24
  Lower Whisker
- 7.25
  Scatter plot
- 7.26
  Cook’s Distance
- 7.27
  Missing Value Treatment
- 7.28
  What is a NA?
- 7.29
  Central Imputation
- 7.30
  KNN imputation
- 7.31
  Dummification
- 7.32
  Correlation
- 7.33
  Pearson correlation
- 7.34
  positive & Negative correlation
- 7.35
  Hands-on
Error Metrics
11
- 8.0
  Classification
- 8.1
  Confusion Matrix
- 8.2
  Precision
- 8.3
  Recall
- 8.4
  Specificity
- 8.5
  F1 Score
- 8.6
  Regression
- 8.7
  MSE
- 8.8
  RMSE
- 8.9
  MAPE
- 8.10
  Hands-on
Machine Learning
0
Supervised Learning
12
- 10.0
  Linear Regression
- 10.1
  Linear Equation
- 10.2
  Slope
- 10.3
  Intercept
- 10.4
  R square value
- 10.5
  Logistic regression
- 10.6
  ODDS ratio
- 10.7
  Probability of success
- 10.8
  Probability of failure Bias Variance Tradeoff
- 10.9
  ROC curve
- 10.10
  Bias Variance Tradeoff
- 10.11
  Hands-on
Unsupervised Learning
4
- 11.0
  K-Means
- 11.1
  K-Means ++
- 11.2
  Hierarchical Clustering
- 11.3
  Hands-on
SVM
5
- 12.0
  Support Vectors
- 12.1
  Hyperplanes
- 12.2
  2-D Case
- 12.3
  Linear Hyperplane
- 12.4
  Hands-on
SVM Kernal
4
- 13.0
  Linear
- 13.1
  Radial
- 13.2
  Polynomial
- 13.3
  Hands-on
Other Machine Learning algorithms
6
- 14.0
  K – Nearest Neighbour
- 14.1
  Naive Bayes Classifier
- 14.2
  Decision Tree – CART
- 14.3
  Decision Tree – C50
- 14.4
  Random Forest
- 14.5
  Hands-on
ARTIFICIAL INTELLIGENCE
0
AI Introduction
6
- 16.0
  Perceptron
- 16.1
  Multi-Layer perceptron
- 16.2
  Markov Decision Process
- 16.3
  Logical Agent & First Order Logic
- 16.4
  AL Applications
- 16.5
  Hands-on
Deep Learning
0
Deep Learning Algorithms
4
- 18.0
  CNN – Convolutional Neural Network
- 18.1
  RNN – Recurrent Neural Network
- 18.2
  ANN – Artificial Neural Network
- 18.3
  Hands-on
Introduction to NLP
7
- 19.0
  Text Pre-processing
- 19.1
  Noise Removal
- 19.2
  Lexicon Normalization
- 19.3
  Lemmatization
- 19.4
  Stemming
- 19.5
  Object Standardization
- 19.6
  Hands-on
Text to Features (Feature Engineering)
11
- 20.0
  Syntactical Parsing
- 20.1
  Dependency Grammar
- 20.2
  Part of Speech Tagging
- 20.3
  Entity Parsing
- 20.4
  Named Entity Recognition
- 20.5
  Topic Modelling
- 20.6
  N-Grams
- 20.7
  TF – IDF
- 20.8
  Frequency / Density Features
- 20.9
  Word Embedding’s
- 20.10
  Hands-on
Tasks of NLP
6
- 21.0
  Text Classification
- 21.1
  Text Matching
- 21.2
  Levenshtein Distance
- 21.3
  Phonetic Matching
- 21.4
  Flexible String Matching
- 21.5
  Hands-on

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Artificial Intelligence Certification Training Course

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