Artificial Intelligence(AL) & Deep Learning
Module 1: Introduction to Data Science
- 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
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
Module 3: Python Basics
- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
Module 4: Python Packages
- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
Module 5: Importing Data
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
Module 6: Manipulating Data
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
Module 7: 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
Module 8: Error Metrics
- Classification
- Confusion Matrix
- Precision
- Recall
- Specificity
- F1 Score
- Regression
- MSE
- RMSE
- MAPE
Module 9: Machine Learning
- 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
- Unsupervised Learning
K-Means
K-Means ++
Hierarchical Clustering
- SVM
Support Vectors
Hyperplanes
2-D Case
Linear Hyperplane
- SVM Kernal
Linear
Radial
polynomial
- Other Machine Learning algorithms
K – Nearest Neighbour
Naïve Bayes Classifier
Decision Tree – CART
Decision Tree – C50
Random Forest
Module 10: ARTIFICIAL INTELLIGENCE
- AI Introduction
Perceptron
Multi-Layer perceptron
Markov Decision Process
Logical Agent & First Order Logic
AL Applications
Module 11: Deep Learning Algorithms
- CNN – Convolutional Neural Network
- RNN – Recurrent Neural Network
- ANN – Artificial Neural Network
- Introduction to NLP
Text Pre-processing
Noise Removal
Lexicon Normalization
Lemmatization
Stemming
Object Standardization
- 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
- Tasks of NLP
Text Classification
Text Matching
Levenshtein Distance
Phonetic Matching
Flexible String Matching
Module 12: Design Effective Reports
- Enhance report design
- Add report objects to enhance design
- Format data and report objects
- Add a background image to a report
- Add row numbers to a report
Module 13: Customize Reports with Conditional Formatting
- Create multi-lingual reports
- Highlight exceptional data
- Show and hide data
- Conditionally render objects in reports
Module 14: Analysis Studio
- Analysis Studio Fundamentals
- Nest Data in Crosstabs in Analysis Studio
- Create Analysis with Multiple filter
- Reusable analysis
- Build Advanced Crosstabs in Analysis Studio
- Focus with Filters in Analysis Studio
- Creating reports from cubes
- Drill down and drill up
Module 15: Event Studio
- Introduction to Event Studio
- Create an agent
- Add tasks to an agent
- Run an agent through its lifecycle
- Schedule an agent
Module 16: Business Insight
- Introdcution to Dashboards
- Create Dashboard
- Types of Filter-Value, Slider and advanced filter
- Overview of RSS Feed and web Page
- Content Pane
- Create Widgets
- Sort, Filter and Calculate data
- Hands on
Module 17: Business Insight Advanced
- Overview of Business Intelligence Advance level
- Create Different types of Reports
- Reporting Styles and filters
- Create dashboard objects
- Summarize data and Create Calculations
- Dispatcher and Services
Module 18: Dispatcher in detail
- All Services
- Properties of Services