Artificial Intelligence Training in Kasaragod

Module 1: Introduction to Data Science (Duration-1hr)

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types

Module 2: Introduction to Python (Duration-1hr)

• What is Python?
• Why Python?
• Installing Python
• Python IDEs
• Jupyter Notebook Overview

Module 3: Python Basics (Duration-5hrs)

• Python Basic Data types
• Lists
• Slicing
• IF statements
• Loops
• Dictionaries
• Tuples
• Functions
• Array
• Selection by position & Labels

Module 4: Python Packages (Duration-2hrs)

• Pandas
• Numpy
• Sci-kit Learn
• Mat-plot library

Module 5: Importing Data (Duration-1hr)

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to csv file

Module 6: Manipulating Data (Duration-1hr)

• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques

Module 7: Statistics Basics (Duration-11hrs)

• 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
• ositive & Negative correlation

Module 8: Error Metrics (Duration-3hrs)

• Classification
• Confusion Matrix
• Precision
• Recall
• Specificity
• F1 Score
• Regression
• MSE
• RMSE
• MAPE

Module 9: Machine Learning

Supervised Learning (Duration-6hrs)

• 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

Unsupervised Learning (Duration-4hrs)

• K-Means
• K-Means ++
• Hierarchical Clustering

SVM (Duration-2hrs)

• Support Vectors
• Hyperplanes
• 2-D Case
• Linear Hyperplane

SVM Kernal (Duration-2hrs)

• Linear
• Radial
• polynomial

Other Machine Learning algorithms (Duration-10hrs)

• K – Nearest Neighbour
• Naïve Bayes Classifier
• Decision Tree – CART
• Decision Tree – C50
• Random Forest

ARTIFICIAL INTELLIGENCE

AI Introduction (Duration-9hrs)

• Perceptron
• Multi-Layer perceptron
• Markov Decision Process
• Logical Agent & First Order Logic
• AL Applications

Module 11: Deep Learning Algorithms (Duration-10hrs)

CNN – Convolutional Neural Network RNN – Recurrent Neural Network ANN – Artificial Neural Network

Introduction to NLP (Duration-5hrs)

• Text Pre-processing
• Noise Removal
• Lexicon Normalization
• Lemmatization
• Stemming
• Object Standardization

Text to Features (Feature Engineering) (Duration-5hrs)

• 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

Tasks of NLP (Duration-2hrs)

• Text Classification
• Text Matching
• Levenshtein Distance
• Phonetic Matching
• Flexible String Matching