Artificial Intelligence Online Training

Programming

• Commands and Syntax of R
• What are the different Packages and Libraries and how are they used in R
• Introduction to Data Types
• Explore Data Structures in R - Matrices, Vectors, Arrays, Factors, Data Frames, Lists
• Methods Importing and Exporting Data.
• How to use Control structures and Functions

Descriptive Statistics

• Various types of Data exploration (bar chart, histograms, box plot, scatter plot, line graph,)
• What is Qualitative and Quantitative Data
• Measure of Central Tendency (Mean, Median and Mode),
• Measure of Positions ( Quantiles, Quartiles, Deciles, and Percentiles)
• Measure of Dispersion ( Median, Quartiles, Variance, Standard deviation, and Absolute deviation about median), Anscombe's quartet
• Other Measures: Interquartile Range, Quartile and Percentile

Initial Data Analysis

• Relationship between attributes: Chi Square, Covariance, Correlation Coef?cient,
• Measure of Distribution (Kurtosis and Skewness ), Box and Whisker Plot (Box Plot and its parts, Using Box Plots to compare distribution) and other statistical graphs

Probability

• Understanding Probability (Conditional probabilities, Joint, and marginal)
• About Probability distributions (Continuous and Discrete)
• How to use Density Functions and Cumulative functions

• Gather information from different sources.
• Explain internal systems and External systems.
• What are Web APIs, Open Data Sources, Data APIs, Web Scrapping
• Relational Database access (queries) to process/access data.

• Concepts of Wrangling, Data Munging
• What are Plyr packages and use of it?
• What is Cast/Melt?

• What is Data imputation?
• Different types of Data Transformation ( log transform, z-score transform, Wrangling etc.,).
• Standardization, Binning, and Classing
• Anomalies and Outlier/Noise

• What is Bag-of-words model and how to use it?
• What are Regular Expressions
• Tokenization and Sentence Splitting
• Incorrect spellings, Punctuations and Stop words
• Properties of words and Word cloud
• Understanding Lemmatization and Term-Document TxD computation
• Sentiment Analysis (Case Study)

• Introduction to Big Data
• Challenges of processing Big Data (Velocity, Volume, and Variety perspective)
• Use Cases

• Programming, Processing, and Storage Framework
• Hadoop eco-system Components and their functions
• How to use Essential Algorithms (Page Rank, Word count,  IT-IDF)
• Spark: RDDs, Streaming and Spark ML
• NoSQL concepts (CAP, ACID, NoSQL types)

• Science of Visualization
• Visualization Periodic Table
• Aesthetics and Story telling
• Concepts of measurement - scales of measurement
• Design of data collection formats with illustration
• Principles of Data Visualization – Various methods of presenting data in business analytics.
• Concepts of Shape, Size, Color
• Various Visualization types
• How to display Bubble charts
• What are Geo-maps (Chlorpeths)
• What are Gauge charts and how to display them?
• What is Tree map?
• What is a Heat map
• How to display Motion charts
• What are Force Directed Charts etc.,

• Sample versus population
• Sample techniques (stratified, simple, random, clustered)
• What are Sampling Distributions?
• Parameter Estimation methods
• Imbalanced data treatment

• How to understand the data, attributes, distributions
• Procedure for statistical testing, etc.
• Test of Hypothesis (Concept of Hypothesis testing, Null Hypothesis and Alternative Hypothesis)
• What is meant by Cross Tabulations and how to create them (Fisher’s exact test, Contingency table, their use, and Chi-Square test)
• What are the conditions for One Sample t test (Performing the test, interpretation of results,  Concept, Assumptions, Hypothesis, Veri?cation)
• Independent Samples t test
• Paired Samples t test
• One way ANOVA (Post hoc tests: Fisher's LSD, Tukey's HSD).
• What is Z-test and F-test and what is the difference?

• Regression basics: Relationship between attributes using Correlation and Covariance

• Relationship between multiple variables: Regression (Multivariate, Linear) in prediction.

• Residual Analysis

• Identifying multi- collinearity, signi?cant features, feature reduction using AIC

• Non-normality and Heteroscedasticity

• Hypothesis testing of Regression Model

• Con?dence intervals of Slope

• R-square and goodness of ?t

• In?uential Observations – Leverage

• What is Polynomial Regression
• Regularization methods
• Concepts of Lasso, Ridge and Elastic nets
• Categorical Variables in Regression

• What is Logit function and interpretation?
• Types of error measures (ROCR)
• What is Logistic Regression in classi?cation?

• Trend analysis
• Seasonal and Cyclical analysis
• Box-Jenkins, Moving averages, Holt-winters, Auto-correlation; ARIMA, Smoothing
• Applications of Time Series in ?nancial markets

• ML Techniques overview
• Validation Techniques (Cross-Validations)
• Dimensionality reduction/Feature Reduction
• Principal components analysis (Eigen vectors, Eigen values, Orthogonality)

• Concepts of Distance measurement
• Different clustering methods (Hierarchical, Distance, Density)
• What is Iterative distance-based clustering
• Dealing with continuous, categorical values in K-Means
• Constructing a hierarchical cluster
• K-Medoids, k-Mode and density-based clustering
• Measures of quality of clustering

Naïve Bayes Classi?er

• Model Assumptions, Probability estimation
• Required data processing
• M-estimates, Feature selection: Mutual information
• Classi?er

K-Nearest Neighbors

• Computational geometry; Voronoi Diagrams; Delaunay Triangulations
• K-Nearest Neighbor algorithm; Wilson editing and triangulations
• Aspects to consider while designing K-Nearest Neighbor

Support Vector Machines

• Linear learning machines and Making Kernels, Kernel space and working in feature space
• SVM for classi?cation and regression problems.

Decision Trees

• ID4, C4.5, CART

Ensembles methods

• Boosting and Bagging and its impact on variance and bias
• C5.0 boosting
• Random forest
• Gradient Boosting Machines and XGBoost

• The applications of Association Rule Mining: Recommendation Engines, Market Basket, etc.
• A mathematical model for Large item sets,  Association Rules, Association analysis
• Apriori: Constructs large item sets with mini sup by iterations; Interestingness of discovered association rules;
• Application examples; Association analysis vs. classi?cation
• FP-trees  

• AI: Application areas
• AI Basics ( Greedy, Branch and Bound, Gradient Descent, Divide and Conquer)
• NN basics (Back propagation, Perceptron and MLN, FFN)

• Image classi?cation
• Text classi?cation
• Image classi?cation and hyper-parameter tuning
• Emerging NN architectures

• Building recurrent NN
• Long Short-Term Memory
• Time Series Forecasting

• Auto-encoders and unsupervised learning
• Stacked auto-encoders and semi-supervised learning
• Regularization - Dropout and Batch normalization