Training Fee

Rs. 6,500/-

May June July

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MANOJ: +91 9676190678


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

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.

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)
Day - 3: Introduction to Python
1. Loops, conditional arguments, Comprehensions, Inbuilt functions , string manipulation etc.
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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.
Day - 31: NLP
1.N-gram Language Models
2.Part Of Speech Tagging and Sequence Labeling
Day – 32: NLP
Day - 32: NLP
1. Basic Neural Networks
2. LSTM Recurrent Neural Networks
Day - 33: NLP
1.Syntactic parsing
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
Day - 35: Hive , sqoop and Spark
1. Big Data ingestion with Hive and sqoop
2.Big Data processing using Apache Spark
Day - 36: Project Development
Day - 37: Project Development
Day - 38: Project Development
Day - 39: Project Development
Day - 40: Project Development
Day - 41: Project Development
Day - 42: Project Development
Day - 43: Project Development
Day - 44: Project Development
Day - 45: Project Development

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