Technologies

What you will Learn 

Data Analysis in Python

R Programming 

Machine Learning 

Data Infrastructure

Introduction to Data Science

Data Visualization & Reporting

Git & Github

*  What is Data Science, Data Analysis and Machine Learning?    *  Life cycle of Data Science 

*  Technologies and Algorithms Used in Data Science    *  Roles and Opportunities of Data Scientists

Introduction to Statistics and Bayesian Theory:

*    Basic understanding of linear algebra, matrices,vector     *    What is Statistics?
*    Descriptive Statistics    *    Central tendency    *    Descriptive Statistics    *    Dispersion Measures   
*    Gaussian Probability Density function    *    Bayesian Classifier for Normal Distributed Classes
*    Non parametric Estimation    *    Estimation of Unknown Probability Density function    *    Maximum likelihood parameter Estimation    *    Maximum a posteriori probability Estimation

Modules used in machine Learning:

*    Numpy:     Linear algebra (creating and accessing elements of vectors, matrices, mathematical
operations with arrays, arange function).
*    Pandas:     Creating data frames, slicing, removing unwanted data, sorting, joining, merging.
*    Matplotlib:     Scatter, bar, pie, histogram, subplots
*    Seaborn:     bar, joint plots, regression plots, box plot, heat map, pair plot.
*    Scikit-learn:     Sklearn module for using algorithms.

Machine learning Algorithms:

*    Supervised learning:    - Regression    -   Classification

*    Unsupervised learning:     -  Clustering

*    Reinforcement learning

Supervised learning Algorithms

Regression

*    Simple Linear Regression    *    Multiple Linear Regression     *    Logistic Regression     *    Regularization

*    Model building     *    Model validation    *    Model Performance     *    Case Study

Supervised learning Algorithms

Naïve bays

*    Gaussian Naïve bays    *    Multinomial Naïve bays    *    Bernoulli Naïve bays    *    Model building    

*    Model validation    *    Confusion matrix     *    Classification Report    *    Case Study

Supervised learning Algorithms

K-Nearest Neighbors Classifier

*    Euclidean Distance    *    Model building    *    Model validation     *    Confusion matrix
*    Classification Report     *    Case Study

Supervised learning Algorithms
Decision Tree Classifier

*    What is Decision Tree   *    How to build Decision Tree     *    Model building    *    Model validation

*    Confusion matrix     *    Classification Report     *    Case Study

Supervised learning Algorithms
Ensemble methods

*    Bagging   *   Boosting

Supervised learning Algorithms

Random Forest Classifier

*    What is Random Forest   *    Model building     *    Model validation   

*    Confusion matrix     *    Classification Report     *    Case Study

1. What is Data Science

1. Demand of Data Science

2. Venn Diagram

3. Pipeline

4. Roles

5. Team

6. Knowledge Check

2. Field of study

1. Big Data overview

2. Programming involvement in Data Science

3. Statistics

4. Knowledge check

3. Ethics

1. Ethical issues

2. Knowledge check

4. Data Sources (Getting Data)

1. Data Metrics

2. Existing data

3. APIs

4. Scraping

5. Creating Data

6. Knowledge check

5. Data Exploration (Cleaning Data)

1. Exploratory graphs

2. Exploratory statistics

3. Knowledge check

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6. Programming

1. Spreadsheets

2. R programming

3. Python

4. SQL

5. Web formats

6. Knowledge check

7. Mathematics

1. Algebra

2. Systems of equations

3. Calculus

4. Big O

5. Bayes probability

6. Knowledge check

Data science training in bangalore, Machine learning training in bangalore, AI Training, Data analytics  course in Bangalore​

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Machine Learning

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• Linear Regression with one and multiple variables.

Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent  method for learning. 

1. Cost function, Gradient descent, Normal Equations

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• Logistic regression. What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features.

1. Cost Function, Gradient descent solution.

• Neural Networks. Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech;

1. Back propagation

2. Application of Neural Network

• Support Vector Machines (SVM). Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice. 

1. Large Margin classification

2. Kernels

• UNSUPERVISED

1. Clustering

2. Gaussian Mixture Models

3. HMM

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• Natural Language Processing (NLP)

The course will cover how to make use of text written by humans, such as  blog posts, tweets, etc...

For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.

1. word and sentence tokenization

2. text classification

3. sentiment analysis

4. spelling correction

5. information extraction

6. parsing

7. meaning extraction

8. question answering

• Speech Recognition

Speech technology is increasingly being used to create highly interactive, voice-activated applications. From voice-control, to smart assistants, to speech transcription and translation, to closed-captioning and language learning, the improved accuracy and processing speed of this technology is enhancing the quality of applications and delivering greater user experiences.

In this course we use the open-source Sphinx toolkit (aka CMU Sphinx) to demonstrate and model various types of speech-enabled applications. By the end of the course participants should have a solid grasp of the tools and techniques needed to apply speech technology to their own applications. Sphinx 4 will be the basis for this training, however, coverage of Sphinx 3 can also be arranged.​

• Face Recognition

1. Openface: OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.
   In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.
   
   By the end of this training, participants will be able to:
   
   - Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
   - Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.​

2. Raspberry Pi + OpenCV: The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.
   
   By the end of this training, participants will be able to:
   
   - Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
   - Configure OpenCV to capture and detect facial images.
   - Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
   - Adapt the system for a variety of use cases, including surveillance, identity verification, etc.

R Programming

  Writing code and setting your working directory

1. Getting started and R nuts and Bolts

   1. R console Input and evaluation

   2. Data types – R Objects and attributes, Vectors and Lists, Data types – Matrices, Data types – Factors, Data types – Missing values

   3. Data types – Data frames, Data types – Names Attributes, Data types – summary, 

   4. Reading Tabular Data, Reading large tables, Textual data formats

   5. Connections: Interfaces to outside world

   6. Subsettings – Basics, Lists, Matrices, Partial Matching, Removing Missing values

   7. Vectorized Operations

 Communicating

1. Interpretability, Actionable insights, Visualization for presentation, Reproducible research, Knowledge check

Conclusion and final test