### About Artificial Intelligance

AI is the simulation of the processes of human intelligence by machines, particularly computer systems. It is an area of computer science which emphasises on the creation or formation of intelligent machines that react and work like human beings. The human intelligence processes that artificial intelligence simulates are learning, reasoning, self-correction, etc. AI acquires information and also the rules for using that information as a part of learning. It reasons by using those specific sets of rules for reaching definite or approximate conclusions.

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# Artificial Intelligence

**Introduction to Python**- 1.Importance of Artifical Intelligence and Use Cases
- 2.Differnce betwwen AI, Data Science, Machine Learning and Deep Learning

- 1.Anaconda Installation and Introduction to Jupyter Notebook
- 2.Data Structures in Python (Lists, Tuples, Dictionaries, sets)

- 1. Loops, conditional arguments, Comprehensions, Inbuilt functions , string manipulation etc.
- 2. Introuction to OOPS

- 1. Introduction to Numpy and operations in Numpy
- 2. Introduction to Pandas and Operations in Pandas - Pandas Basics, Indexing and selecting Data, Merge and Append, Grouping and Summarizing, Lambda functions and Pivot tables
- 3. Introduction to Reading and Cleaning Data

- 1. Introduction to Database design, OLAP vs OLTP, Star Schema etc.
- 2. Basics of SQL, Data Retrieval, sorting, compound functions and relational operators, pattern matching with wild cards.
- 3. Basics on Table creation, updating, modifying etc.
- 4. Overall Structure of data retrieval queries, Merging tables, User Defined Functions (UDF), Frames.

- 1. Business and Data Understanding
- 2. CRISP-DM Framework - Data Preparation, Modelling, Evaluation and Deployment

- 1.Introduction to visualization and Importance of Visualization
- 2. Introduction to various charts
- 3. Data visualization toolkit in Python (Libraries or modules available in Python)
- 4. Plotting Data in Python using matplotlib and seaborn - Univariate Distributions, Bi-variate Distributions
- 5. Plotting Time series data

- 1. Introduction to Data Sourcing and various sources available for data collection
- 2. Data Cleaning - Fixing rows and columns, Missing value Treatment, standardizing values, handling invalid values, Filtering data
- 3. Data types - Numerical, Categorical (ordered and unordered)
- 4. Univariate Analysis, Bivariate Analysis, Segmented univariate Analysis
- 5. Derived Metrics and Feature Engineering
- 6. Introduction to Outliers and their handling

- 1. Introduction to inferential statistics - basics of probability, Random Variables, Expected value, Probability Distributions
- 2. Discrete and Continuous Probability Distributions
- 3. Central Limit Theorem - Introduction and Industrial applications

- 1. understanding Hypothesis Testing, Null and Alternate Hypothesis, Industry Relevance
- 2. Concepts of Hypothesis Testing - p value method, critical value method
- 3. Types of Errors, T Distribution, other types of tests
- 4. Industry Demonstration and A/B Testing

- 1. Introduction to Machine Learning - Supervised and Unsupervised learning Methods

- 1. Introduction to Regression and Best Fit Line
- 2. Assumptions of Linear Regression (LINE)
- 2. Cost Functions, Strength of Linear relationship - OLS, coefficient of correlation, coefficient of Determination
- 3. Intuition to Gradient Descent for optimizing cost function
- 4. Hypothesis Testing in Linear Regression
- 5. Building a Linear Model - Reading Data, Cleaning Data, Libraries available - Sklearn, Statsmodel.api
- 6. Model Building using Sklearn and Training and Test Data, Model Development, Model validation using Residual Analysis, Evaluation against the test Data

- 1. Using Multiple Predictors for Linear Regression
- 2. Introduction to overfitting, Multi-collinearity
- 3. Dealing with Categorical variables - OHE, Dummies, Label Encoding
- 4. Building the model using statesmodel.api and importance of p-values
- 5. Model Evaluation Metrics - Coefficient of Determination, Adjusted R2, RMSE, AIC, BIC and other model evaluation Metrics
- 6. Variable Selection - RFE, Step wise selection etc.
- 8. Gradient Descent and Normal Equation for Multiple Linear Regression
- 7. Industry Demonstration: Linear Regression Case Study

- 1. Bias - Variance Trade off, Occam's Razor, Curse of Dimensionality
- 2. Cross Validation and how to avoid overfitting
- 3. Hyper parameter tuning using GridSearchCV, RandomSearchCV and other libraries

- 1. Introduction to Classification
- 2. Binary classification using univariate logistic regression
- 3. Maximum Likelihood function, Sigmoid Curve and Best Fit
- 4. Intuition of odds and log-odds
- 5. Feature selection using RFE
- 6. Model evaluation - Confusion Matrix and Accuracy
- 7. Why Accuracy is not Enough and introduction to sensitivity, specificity, precision, recall, area under curve
- 8. Logistic Regression Case Study

- 1. Understanding clustering with practical examples
- 2. KMeans Clustering - understanding the algorithm
- 3. Practical consideration for KMeans Clustering - Elbow curve, silhouette metric and hopkings test for clustering tendency of data, impact of outliers

##### Means Clustering:

- 1. Hierarchical clustering Algorithm
- 2. Interpreting the dendogram and Types of Linkages
- 3. Comparison of Kmeans clustering and Hierarchical clustering - advantages and disadvantages

##### Hierarchical Clustering:

- 1. Intuition behind PCA and practical examples
- 2. Variance as information and basis transformation of vectors
- 3. Singular Value Decomposition and Identifying optimum principal components using scree plots
- 4. Model building with PCA
- 5. Advantages of PCA and Limitations

- 1. Introduction to decision trees and Interpretation
- 2. Homogeneity measures for splitting a node 1. Gini Index 2. Entropy 3. R2
- 3. Understanding Hyper parameters - Truncation and Pruning
- 4. Advantages and Disadvantages
- 1. Introduction to ensembling, bagging and intuition
- 2. Random Forest - Introduction and Hyperparamters
- 3. Building a model using Random Forest
- 4. Hyper-parameters impact on model and tuning them
- 5. Importance of predictors using Random Forrest

##### Decision Trees:

##### Random Forest:

- 1. Intuition behind Boosting
- 2. Adaboost Algorithm - Understanding and Model Building
- 3. Understanding Gradient Boosting
- 4. Introduction to Boosting Algorithms : XGBoost, lightGBM, Catboost
- 5. Advantages of Boosting Algorithms
- 6.XGBoost Model Building and importance of various Hyper parameters
- 7. Hyper-parameter tuning for XGBoost

- 1. Introduction to Other Models such as SVM, KNN, Navie Bayes etc.

- 1. Introduction to Time Series with ARIMA

- 1. Introduction to Text Mining

- 1.Introduction to deep learning
- 2.Neural Networks Basics

- 1. Introducntion to Artificial Neural Networks

- 1. Introducntion to Recurrent Neural Networks

- 1. Introduction to Convolutional Neural Networks

- 1. Introducntion to Generative Adversarial Networks

- 1. Introduction to Reinformant Learning

- 1. Introduction
- 2. NLP tasks in syntax, semantics, and pragmatics.
- 3.Applications such as information extraction, question answering, and machine translation.

- 1.N-gram Language Models
- 2.Part Of Speech Tagging and Sequence Labeling

- 1. Basic Neural Networks
- 2. LSTM Recurrent Neural Networks

- 1.Syntactic parsing
- 2.Semantic Analysis

- 1. Introduction to Big Data
- 2. Big Data Storage and processing framework - Hadoop

- 1. Big Data ingestion with Hive and sqoop
- 2.Big Data processing using Apache Spark