ARTIFICIAL INTELLIGENCE SUMMER INTERNSHIP PROGRAM IN BANGALORE
- ARTIFICIAL INTELLIGENCE
COMPLETE TRAINING ON TECHNOLOGY | PROJECT DEVELOPEMENT
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
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Day - 1 Introduction to Artificail Intelligence
1.Importance of Artifical Intelligence and Use Cases
2.Differnce betwwen AI, Data Science, Machine Learning and Deep Learning
Day - 2: Introduction to Python
2.Data Structures in Python (Lists, Tuples, Dictionaries, sets)
Day - 3: Introduction to Python
2. Introuction to OOPS
Day - 4: Python for Data Science
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
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
2. CRISP-DM Framework – Data Preparation, Modelling, Evaluation and Deployment
Day - 7: Data Visualization in Python
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
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
2. Discrete and Continuous Probability Distributions
3. Central Limit Theorem – Introduction and Industrial applications
Day - 10: Hypothesis 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
Day - 12: Simple Linear Regression
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
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
2. Cross Validation and how to avoid overfitting
3. Hyper parameter tuning using GridSearchCV, RandomSearchCV and other libraries
Day - 15: Logistic Regression
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
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
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)
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
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
Day - 20: 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
Day - 22: Time Series
Day - 23: Text Mining
Day - 24: Introuction
2.Neural Networks Basics
Day - 25: Neural Networks
Day - 26: Neural Networks
Day - 27: Neural Networks
Day - 28: Neural Networks
Day - 29: Reinforcement Learning
Natural Language Processing
Day - 30: Introduction
2. NLP tasks in syntax, semantics, and pragmatics.
3.Applications such as information extraction, question answering, and machine translation.
Day - 31: NLP
2.Part Of Speech Tagging and Sequence Labeling
Day – 32: NLP
Day - 32: NLP
2. LSTM Recurrent Neural Networks
Day - 33: NLP
Day - 34: Introduction to Big Data storage and Analytics
2. Big Data Storage and processing framework – Hadoop
Day - 35: Hive , sqoop and Spark
2.Big Data processing using Apache Spark