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Artificial Intelligence Certification Training Course
Introduction to Data Science
What is Data Science?
What is Machine Learning?
What is Deep Learning?
What is AI?
Data Analytics & its types
Introduction to Python
What is Python?
Why Python?
Installing Python
Python IDEs
Jupyter Notebook Overview
Python Basics
Python Basic Data types
Lists
Slicing
IF statements
Loops
Dictionaries
Tuples
Functions
Array
Selection by position & Labels
Hands-on
Python Packages
Pandas
Numpy
Sci-kit Learn
Mat-plot library
Hands-on
Importing Data
Reading CSV files
Saving in Python data
Loading Python data objects
Writing data to CSV file
Hands-on
Manipulating Data
Selecting rows/observations
Rounding Number
Selecting columns/fields
Merging data
Data aggregation
Data munging techniques
Hands-on
Statistics Basics
Central Tendency
Mean
Median
Mode
Skewness
Normal Distribution
Probability Basics
What does mean by probability?
Types of Probability
ODDS Ratio?
Standard Deviation
Data deviation & distribution
Variance
Bias variance Trade off
Underfitting
Overfitting
Distance metrics
Euclidean Distance
Manhattan Distance
Outlier analysis
What is an Outlier?
Inter Quartile Range
Box & whisker plot
Upper Whisker
Lower Whisker
Scatter plot
Cook’s Distance
Missing Value Treatment
What is a NA?
Central Imputation
KNN imputation
Dummification
Correlation
Pearson correlation
positive & Negative correlation
Hands-on
Error Metrics
Classification
Confusion Matrix
Precision
Recall
Specificity
F1 Score
Regression
MSE
RMSE
MAPE
Hands-on
Supervised Learning
Linear Regression
Linear Equation
Slope
Intercept
R square value
Logistic regression
ODDS ratio
Probability of success
Probability of failure Bias Variance Tradeoff
ROC curve
Bias Variance Tradeoff
Hands-on
Unsupervised Learning
K-Means
K-Means ++
Hierarchical Clustering
Hands-on
SVM
Support Vectors
Hyperplanes
2-D Case
Linear Hyperplane
Hands-on
SVM Kernal
Linear
Radial
Polynomial
Hands-on
Other Machine Learning algorithms
K – Nearest Neighbour
Naive Bayes Classifier
Decision Tree – CART
Decision Tree – C50
Random Forest
Hands-on
AI Introduction
Perceptron
Multi-Layer perceptron
Markov Decision Process
Logical Agent & First Order Logic
AL Applications
Hands-on
Deep Learning Algorithms
CNN – Convolutional Neural Network
RNN – Recurrent Neural Network
ANN – Artificial Neural Network
Hands-on
Introduction to NLP
Text Pre-processing
Noise Removal
Lexicon Normalization
Lemmatization
Stemming
Object Standardization
Hands-on
Text to Features (Feature Engineering)
Syntactical Parsing
Dependency Grammar
Part of Speech Tagging
Entity Parsing
Named Entity Recognition
Topic Modelling
N-Grams
TF – IDF
Frequency / Density Features
Word Embedding’s
Hands-on
Tasks of NLP
Text Classification
Text Matching
Levenshtein Distance
Phonetic Matching
Flexible String Matching
Hands-on
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