Artificial Intelligence Course Training in Bengaluru
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- Certificate of Excellence Award for
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- “58 Million jobs to be created in Next Few Years”
- Artificial Intelligence Course Training in Bengaluru - Key Highlights
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- Artificial Intelligence Course Training in Bengaluru - Course Content
- Computer Vision with Python:
- NLP with Python:
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- Difference Between Artificial Intelligence-Machine Learning and Deep Learning
- Top Artificial Intelligence(AI) Companies in Bangalore
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- Real Life Applications of AI
- The Important Roles of AI Engineers in 2020
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Artificial Intelligence Course Training in Bengaluru - Course Content
- What is Data Science? – Introduction.
- Importance of Data Science.
- Demand for Data Science Professional.
- Brief Introduction to Big data and Data Analytics.
- Lifecycle of data science.
- Tools and Technologies used in data Science.
- Installing Python IDE
- Installing Python Environments like Jupyter, Pycharm, Spyder etc.
- Installing Packages – Loading and Unloading Packages
|INTRODUCTION TO STATISTICS|
|Basic business statistics||How businesses use statistics |The basic vocabulary of statistics |The types of data used in business|
Organizing and Visualizing Data
The sources of data used in business | To construct tables and charts for numerical data | To construct tables and charts for categorical data | The principles of properly presenting graphs
Numerical Descriptive Measures
To describe the properties of central tendency, variation, and shape in numerical data | To construct and interpret a boxplot | To compute descriptive summary measures for a population | To compute the covariance and the coefficient of correlation
|Basic Probability and Terms|| |
Basic probability concepts | Conditional probability | To use Bayes’ Theorem to revise probabilities | Various counting rules
|Probability Distributions||Types of Distributions like Discrete and Continuous | Functions of Random Variables | Probability Distribution Graphs|
|Sampling and Sampling Distributions|| |
To distinguish between different sampling methods | The concept of the sampling distribution | To compute probabilities related to the sample mean and the sample proportion | The importance of the Central Limit Theorem
Confidence Interval Estimation
To construct and interpret confidence interval estimates for the mean and the proportion | How to determine the sample size necessary to develop a confidence interval estimate for the mean or proportion | How to use confidence interval estimates in auditing
|Fundamentals of Hypothesis Testing||Null and Alternate hypothesis | One sample Tests | Test statistic and critical values | Possible errors in testing | p-value approach | t and z-tests | Testing on proportions|
|Introduction to Python|| |
Installation | Python Basics | Spyder IDE | Jupyter Notebook | Floats and Strings Simple Input & Output | Variables | Operators | Single and Multiline Comments |
Taking input from user
|Data Structures||List | Strings | Tuple | Dictionary | Sets and their examples|
|Conditional Statements||If | if-else | if-elif-else | nested if else|
|Loops||For | while | nested loops| loop control statements|
|Functions||Creating user defined Functions | Function arguments like- Required, Keyword, Default and variable-length | Scope of variables in creating functions | Anonymous Functions – Lambda|
|Exception and File handling||Exception Handling | Raising Exceptions | Assertions | Files I/O|
|Object Oriented Programming||Introduction | Class & Instance Attributes | Properties vs getters and setters | Inheritance | Abstract Classes|
|DATA ANALYSIS & VISUALIZATION WITH PYTHON|
|Numpy module||Basics of numpy | creating multidimensional arrays | Numpy operations|
|Pandas||Introduction to Pandas | IO Tools | Pandas – Series and Dataframe and their wide range of functionalities|
|Matplotlib, Seaborn & Word Cloud|| |
Graphical representation of data using various plots like bar plots, Pie plot, Histogram, Scatter plot, Box plot etc.|
Creating word clouds with text data
|Scikit learn||Introduction to SciKit Learn | Load Data into Scikit Learn | Run Machine Learning Algorithms Both for Unsupervised and Supervised Data | Supervised Methods: Classiﬁcation & Regression | Unsupervised Methods: Clustering, Gaussian Mixture Models | Decide What’s the Best Model for Every Scenario|
|Data Transformations|| |
Merge, Rollup, Transpose and Append | Smoothing | Aggregation | Normalization | Attribute construction
|Feature Engineering||Missing value Imputation | Outlier Analysis and Treatment | Binning | Creating dummy variables | feature scaling | Extracting Date | Log Transformation | Feature split | Label Encoding | One-Hot Encoding|
|INTRODUCTION TO MACHINE LEARNING|
|Introduction||What is Machine Learning? | End-to-end Process of Investigating Data Through a Machine Learning Lens | Evolution and Trends | Application of Machine Learning | Best Practices of Machine Learning|
|Machine Learning Methods||Supervised | Unsupervised|
|Machine Learning Algorithms||Classiﬁcation | Regression | Time Series | Collaborative Filtering | Clustering | Principal Component Analysis|
|MACHINE LEARNING APPLICATIONS|
|Linear Regression||Implementing Simple & Multiple Linear Regression | Making Sense of Result Parameters | Model Validation | Handling Other Issues/Assumptions in Linear Regression: Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity | Prediction and Conﬁdence Intervals | Use Cases|
|Logistic Regression|| |
Implementing Logistic Regression| Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test | Goodness of Fit Measures | Model Validation: Cross Validation, ROC Curve, Confusion Matrix | Use Cases
|Decision Tree||Implementing Decision Trees | Homogeneity | Entropy Information Gain | Gini Index | Standard Deviation Reduction | Vizualizing & Prunning a Tree | Implementing Random Forests using Python | Random Forest Algorithm | Important hyper-parameters of Random Forest for tuning the model | Variable Importance | Out of Bag Errors|
|Naïve Bayes||Bayes Theorem | Gaussian Naïve Bayes & its implementation | Multinomial Naïve Bayes, Count vectorizer, TF-IDF Vectorizer and its use cases on Text Classification|
|K-Nearest Neighbors||Concept of nearest neighbors | Euclidean Distance | Use Cases of KNN Classifier & Regressor|
|Support Vector Machine||Introduction to support vectors | Concept of hard & soft margins | slack variable | Lagrangian Primal & Dual | Kernel Trick | Use Cases|
|Time Series||Introduction | Components of Time Series | Stationarity | ACF | PACF | ARIMA model for forecasting | Use Cases|
|K-Means Clustering||Clustering concept | Finding optimal number of clusters | Use Cases|
|Hierarchical Clustering||Agglomerative & Divisive Clustering | Dendrograms | Linkage Matrix like Single, Complete & Average | Use Cases|
|Ensemble Techniques||Bagging | Boosting | Stacking | Regularization | Different cross-validation techniques used to treat Over fitting and Under fitting in machine learning models|
|Principal Component Analysis||Concept of Dimensionality reduction | Eigen values | Eigen Vectors | Use Cases|
|PROJECT 1|| |
Title: Real Estate Price Prediction using Linear Regression
Industry: Real Estate
Description: The goal of this Use-case is to make property price predictions using Real Estate data. The dataset contains the of the price of apartments and various characteristics of the property. Based on this data, decide on the price of new properties.
|PROJECT 2|| |
Title: Loan Prediction using Logistic Regression & Decision Tree
Description: The goal is to build a classification model to predict if a loan is approved or not. Dataset contains demographic information like age, income etc. of various customers. Based on this, predict for a new customer whether loan will be approved or not
|PROJECT 3|| |
Title: Recommendation for Movie, Summary
Topics: This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details, and others.
|PROJECT 4|| |
Title: Handwritten digit recognition using Decision Tree &Random forest
Description: Dataset contains various pixel values of handwritten digits between 0 to 9. The goal is to build a classification model to predict the digit from its pixel values.
|PROJECT 5|| |
Title: Amazon fine food review-Sentiment Analysis
Description: The analysis is to study Amazon food review from customers and try to predict whether a review is positive or negative. The dataset contains more than 500k reviews with number of upvotes and total votes to those comments
|DEEP LEARNING ALGORITHMS & APPLICATIONS|
|Neural Networks||Understanding Neural Networks | The Biological Inspiration | Perceptron Learning & Binary Classiﬁcation | Backpropagation Learning | Learning Feature Vectors for Words | Object Recognition|
|Keras||Keras for Classiﬁcation and Regression in Typical Data Science Problems | Setting up KERAS | Diﬀerent Layers in KERAS | Creating a Neural Network Training Models and Monitoring | Artiﬁcial Neural Networks|
|Tensorflow||Introducing Tensorﬂow | Neural Networks using Tensorﬂow | Debugging and Monitoring | Convolutional Neural Networks|
|Recurrent Neural Networks||Introduction to RNN | RNN long & short term dependencies | Vanishing gradient problem | Basic LSTM | Step by step walk through LSTM | Use cases|
ANN ON KERAS
Title: Credit Default using ANN on Keras
Description: This research aimed at the case of customers’ default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classiﬁcation – credible or not credible clients.
CNN ON KERAS & TENSORFLOW
Title: Handwriting/Facial recognition using CNN on TensorFlow & Keras
Industry: Pattern Recognition
Description: This project will help build a model using Convolutional Neural Network to recognize handwriting/facial images
Title: Google stock price prediction
Description: Dataset contains dates, volume, open, close, high and low prices of stocks. Based on this build a LSTM model to predict current and future stock price
Title: Traffic Signs Recognition
In self-driving cars in which the passenger can fully depend on the car for traveling. But to achieve level 5 autonomous, it is necessary for vehicles to understand and follow all traffic rules.
In the world of Artificial Intelligence and advancement in technologies, many researchers and big companies like Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, etc are working on autonomous vehicles and self-driving cars. So, for achieving accuracy in this technology, the vehicles should be able to interpret traffic signs and make decisions accordingly.
Highlights: This Python project is about building a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.
Computer Vision with Python:
- Introduction to OpenCV
- Core Operations
- Image Processing in OpenCV
- Feature Detection and Description
- Video Analysis
- Machine Learning
- Object Detection
- OpenCV-Python Bindings
NLP with Python:
- Introduction to NLTK
- Tokenizing words and Sentences with NLTK
- Stop words and Stemming with NLTK
- Part of Speech Tagging with NLTK
- Chunking & Chinking
- Lemmatizing with NLTK
- Wordnet with NLTK
- Converting words to Features
- Text Classification with NLTK
- Combining Algorithms with NLTK
- Creating a module for Sentiment Analysis
- Twitter Sentiment Analysis with NLTK
- Named Entity Recognition with Stanford NER Triggers
- Testing NLTK and Stanford NER Triggers for Accuracy and Speed
SQL (Structured Query Language)
- Introduction to SQL
- SQL Select Statements
- Execute a basic SELECT statement
- Restricting and Sorting Data
- Limit the rows retrieved by a query
- Sort the rows retrieved by a query
- Single-Row Functions
- Describe various types of functions available
- Use character, number, and date functions in SELECT statements
- Describe the use of conversion functions
- Displaying Data from Multiple Tables
- Write SELECT statements to access data from more than one table using equality and nonequality joins
- View data that generally does not meet a join condition by using outer joins
- Join a table to itself by using a self join
- Aggregating Data Using Group Functions
- Identify the available group functions
- Describe the use of group functions
- Group data using the GROUP BY clause
- Include or exclude grouped rows by using the HAVING clause
- Manipulating Data
- Describe each DML statement
- Insert rows into a table
- Update rows in a table
- Delete rows from a table
- Merge rows in a table
- Control transactions
- Creating and Managing Tables
- Including Constraints
- Describe constraints
- Create and maintain constraints
According to the World Economic Forum Report. The growth of Artificial Intelligence could create 58 million jobs in next few years.
- Artificial Intelligence course is on demand and most adorable technology for the fresh graduates as well as professionals who are willing to Kick-start their career in robotic /artificial world. Artificial intelligence is a process of the computers or robots can perform tasks intelligently by using Machine Learning,Computer Vision, Natural Language Processing and Deep Learning techniques.
- Artificial intelligence (AI) is a new factor of production and has the potential to introduce new sources of growth, changing how work is done and reinforcing the role of people to drive growth in business.
- Accenture research on the impact of AI in 12 developed economies reveals that AI could double annual economic growth rates in 2035 by changing the nature of work and creating a new relationship between man and machine. The impact of AI technologies on business is projected to increase labor productivity by up to 45 percent and enable people to make more efficient use of their time.
Why Artificial Intelligence ?
- To Create Expert Systems: The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.
- To Implement Human Intelligence in Machines: Creating systems that understand, think, learn, and behave like humans.
- The goal of AI is to develop computers that can simulate the ability to think, as well as see, hear, walk, talk, and feel.
Real Life Applications of AI
- Expert Systems
The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.
Examples: Flight-tracking systems, Clinical systems
- Natural Language Processing
Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English.
Examples: Google Now feature, speech recognition, Automatic voice output, AI Chatbots
- Neural Networks Examples
Yet another research area in AI, neural networks, is inspired from the natural neural network of human nervous system.
Examples: Pattern recognition systems such as face recognition, character recognition, handwriting recognition.
Robotics is a branch of AI, which is composed of Electrical Engineering, Mechanical Engineering, and Computer Science for designing, construction, and application of robots.
Examples: Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, carving etc.
5. Fuzzy Logic
Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO.
Examples: Consumer electronics, automobiles, etc