# ARTIFICIAL INTELLIGENCE SUMMER INTERNSHIP PROGRAM 2022

- ARTIFICIAL INTELLIGENCE

COMPLETE TRAINING ON TECHNOLOGY | PROJECT DEVELOPEMENT

#### HYDERABAD

407, 4th Floor, Pavani Prestige (R.S Brothers)Building, Ameerpet, Hyderabad, India Opposite Image Hospital & Beside KLM Fashion Mall.

## About Artificial Intelligance

## Registration Process

## Internship Tracks

## Artificial Intelligence

##### Day - 1 Introduction to Artificail Intelligence

**Introduction to Python**

1.Importance of Artifical Intelligence and Use Cases

2.Differnce betwwen AI, Data Science, Machine Learning and Deep Learning

##### Programming Essentials

##### Day - 2: Introduction to Python

1.Anaconda Installation and Introduction to Jupyter Notebook

2.Data Structures in Python (Lists, Tuples, Dictionaries, sets)

2.Data Structures in Python (Lists, Tuples, Dictionaries, sets)

##### Day - 3: Introduction to Python

1. Loops, conditional arguments, Comprehensions, Inbuilt functions , string manipulation etc.

2. Introuction to OOPS

2. Introuction to OOPS

##### Day - 4: Python for Data Science

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

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

##### Day - 5: Introduction to SQL

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.

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.

##### Statistics & Exploratory Data Analysis (EDA)

##### Day - 6: Introduction To Data Analytics

1. Business and Data Understanding

2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment

2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment

##### Day - 7: Data Visualization in Python

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

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

##### Day - 8: Exploratory Data AnalysisPurpose of IoT Gateway

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

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

##### Day - 9: Inferential Statistics

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

2. Discrete and Continuous Probability Distributions

3. Central Limit Theorem – Introduction and Industrial applications

##### Day - 10: Hypothesis Testing

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

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

##### Machine Learning - I

##### Day - 11: Introduction to Machine Learning

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

##### Day - 12: Simple Linear Regression

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

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

##### Day - 13: Multiple Linear Regression

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

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

##### Day - 14: Model Selection and Best Practices

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

2. Cross Validation and how to avoid overfitting

3. Hyper parameter tuning using GridSearchCV, RandomSearchCV and other libraries

##### Day - 15: Logistic Regression

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

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

##### Day - 16: unsupervised Learning:Means Clustering

Means Clustering:

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

##### Day - 17: unsupervised Learning:Hierarchical Clustering

Hierarchical Clustering:

1. Hierarchical clustering Algorithm

2. Interpreting the dendogram and Types of Linkages

3. Comparison of Kmeans clustering and Hierarchical clustering – advantages and disadvantages

##### Day - 18: unsupervised Learning:Principal Component Analysis(PCA)

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

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

##### Machine Learning - II

##### Day - 19: Tree Models

**Decision Trees:**

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

**Random Forest:**

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

##### Day - 20: Boosting

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

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

##### Day - 21: Other Models

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

##### Day - 22: Time Series

1. Introduction to Time Series with ARIMA

##### Day - 23: Text Mining

1. Introduction to Text Mining

##### Deep Learning

##### Day - 24: Introuction

1.Introduction to deep learning

2.Neural Networks Basics

2.Neural Networks Basics

##### Day - 25: Neural Networks

1. Introducntion to Artificial Neural Networks

##### Day - 26: Neural Networks

1. Introducntion to Recurrent Neural Networks

##### Day - 27: Neural Networks

1. Introduction to Convolutional Neural Networks

##### Day - 28: Neural Networks

1. Introducntion to Generative Adversarial Networks

##### Day - 29: Reinforcement Learning

1. Introduction to Reinformant Learning

##### Natural Language Processing

##### Day - 30: Introduction

1. Introduction

2. NLP tasks in syntax, semantics, and pragmatics.

3.Applications such as information extraction, question answering, and machine translation.

2. NLP tasks in syntax, semantics, and pragmatics.

3.Applications such as information extraction, question answering, and machine translation.

##### Day - 31: NLP

1.N-gram Language Models

2.Part Of Speech Tagging and Sequence Labeling

Day – 32: NLP

2.Part Of Speech Tagging and Sequence Labeling

Day – 32: NLP

##### Day - 32: NLP

1. Basic Neural Networks

2. LSTM Recurrent Neural Networks

2. LSTM Recurrent Neural Networks

##### Day - 33: NLP

1.Syntactic parsing

2.Semantic Analysis

2.Semantic Analysis

##### Big Data

##### Day - 34: Introduction to Big Data storage and Analytics

1. Introduction to Big Data

2. Big Data Storage and processing framework – Hadoop

2. Big Data Storage and processing framework – Hadoop

##### Day - 35: Hive , sqoop and Spark

1. Big Data ingestion with Hive and sqoop

2.Big Data processing using Apache Spark

2.Big Data processing using Apache Spark