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ARTIFICIAL INTELLIGENCE Building Next-gen JARVIS

“Self-implementation of CV & NLP based System; An AI Shadow, capable of

textual, voice or visual data interpretation using ML Algorithms, Deep Learn-

ing Frameworks and Python for Web and Portable Device Platforms”

AI is any technique, code or algorithm that enables machines to develop, demonstrate and mimic

human cognitive behavior or intelligence and hence the name “Artificial Intelligence”. Some of the

most successful applications of AI around us can be seen in Robotics, Computer Vision, Virtual Real-

ity, Speech Recognition, Automation, Gaming and so on…

Artificial Intelligence is constantly pushing the boundaries of what machines are capable of. The Main

purpose of training real time smart machine is to use their speed and capability. Most importantly

machine can think and perform task like humans. By this course student will be able to design and

develop an advance AI System.

Why should you learn this?

The Technology of TODAY and TOMORROW

A constant force to push the limits of what machine are capable of

To know and utilize the speed and capability of Real-time machines

To make machines thinkable

With this course, you would be able to design and develop and AI System by your own

The curriculum is designed according to the latest trends in technology and In-dustry Demands


COURSE HIGHLIGHTS

Projects Covered:

1. Movie Recommendation System

2. Descriptive Analytic Model of Restaurant Billing & Tips Data

3. Real-estate House Price Predictor Model

4. Startup Profit Prediction System

5. Company Valuation Prediction

6. Flower Species Classification Model

7. Heart Disease Detection App

8. Fake News Detection System

9. Email SPAM Detection Application

10. Weather Prediction System

11. Music Genre Prediction System

12. Finding the organic users on social media

13. Detecting Real versus Fraud Credit Card Applications

14. Speech Emotion Recognition

15. MNIST Image Classification Model

16. Live Web Chatbot

17. My Selfie Machine

18. Optical Character Recognition using KNN

19. Real Time Face Recognition System

20. CNN based Object Recognition System

21. Facial Expression Based Music Player

22. Gender & Age Prediction System

23. Object Detection & Recognition Model

24. The Cartpole Agent Balancing

25. CAPSTONE PROJECT: “Build JARVIS or FRIDAY or anything for your Personal Assistance”

NOTE

Apart from the Course Content, we conduct special sessions on Resume Writing, Soft Skills, Personality Development and Mock Interview Sessions for our techies


ARTIFICIAL INTELLIGENCE

Duration – 6 Weeks (80 Hours)

1 Introduction & Python Recap

Introduction with AI & Machine Learning Data Science vs Data Engineering vs Data Analysis vs AI Use of Data in the world of AI Basic Linux/Windows Commands Why Python? Installing & Setting up Python on System Understanding its Command Line & Scripts Simple Python Program Python Revisit: Keywords, Data Types, Operators Conditional/Looping/Error Handling in Python

2 Hours

2 Python Recap - 2

Comprehensions Python User Defined Functions Python Generators Lambda Expressions Python Modules: Usage and Installation Understanding the OOP of Python

2 Hours

3 Data formats & Python

Types of DATA? The key steps of Data Analysis The file handling in python Dealing with Excel/Json/CSV/txt files What are SQL based Database Basic SQL Operations Exercise – 0

2 Hours

4 Numpy

Python Numpy Arrays Creating, Accessing, Manipulating Numpy Array Numpy Data Types Array Attributes Data Operations Arithmetic and Statistical Methods Sort, Search, Count File Handling with Numpy Exercise – 1

2 Hours

5 Pandas

The Series and DataFrame Creating, Accessing, Manipulating Pandas Data Series and DataFrame Attributes & Basic Functions Iteration on Data Statistical Functions; String Functions Logical Indexing; Sorting & Reindexing Merging, Joining & Concatenation of Data Exercise – 2

2 Hours


6 Pandas - 2

Understanding Kaggle/Hackerearth Platforms Pandas File Handling Grouping Data Function Application Missing Data & Treatment Date & Time Functionality Exercise – 3

2 Hours

7 Project Project 1: “Movie Recommendation System” 2

Hours

8 Data Visualiza-tion

How Data is Beautiful? Visualization Libraries in Python MATPLOTLIB PYPLOT: line, scatter, pie, box, area etc Decorating the plots using Matplotlib (labels, colors, mark-

ers, legend, grids, figure sizes etc) The Subplots and axes in matplotlib Showing Images Exercise – 4

2 Hours

9 Data Visualiza-tion

Pandas Visualization: Basic Plots bar, barh, hist, box, kde, density, area, scatter, hexbin, pie

plots Plotting with Missing Data Exercise – 5 Project 2: “Descriptive Analytic Model of Restaurant Bill-

ing & Tips Data”

2 Hours

10

Machine Learning - Re-gression

Understanding the concept of Machine Learning The Flow of Machine Learning The Mathematics Required for ML Types of Learning and their sub-categories The Scikit-learn Library REGRESSION: Linear Regression The Line Equation; Fitting Data in Model Performance Evaluation of Model Project 3: “Real-estate House Price Predictor Model” Exercise – 6

2 Hours

11

Machine Learning – Re-gression

Multiple Linear Regression: Case-study Polynomial Regression: The Non-linearity in Data Project 4: “Startup Profit Prediction System” Project 5: “Companies Valuation Prediction”

Exercise – 7

2 Hours

12

Machine Learning - Classification

Logistic Regression: Concept Dealing with missing data in real-time information Exercise – 8 Project 6: “Flower Species Classification Model”

2 Hours

13


The Information Theory Decision Trees Classifier Random Forest Classifier Project 7: “Heart Disease Detection App”

2 Hours


14

Natural Lan-guage Pro-cessing - Intro

What is NLP? Linguistic to Natural Language Text and Speech Processing Text to Speech and Speech to Text Modules in Python Morphological Analysis Syntactic Analysis Word Clouds Libraries in Python Exercise – 9 Project 8: “Fake News Detection System”

2 Hours

15

Machine Learning – Na-ïve Bayes

The Probability Bayes Theorem Naïve Bayes Algorithm for Machine Learning Naive Bayes Algo implementation Project 9: “Email SPAM Detection Application”

2 Hours

16 Deployment of ML Models

Introduction with Flask Deployment of Machine Learning Models over GITHUB

and Cloud Services (AWS/ Google/ and similar plat-forms)

QUERY SESSION

2 Hours

17 Data Visualiza-tion - Seaborn

Easy and advanced Data Visualization from Seaborn Categorical, Distributive, Regression, Matrix, Grid Plots Customizing Color Themes Some Datastore Exercise – 10

2 Hours

18 Data Visualiza-tion - Plotly

The Real-time challenges of Data Visualization The Front End of ML - PLOTLY Plotly Express for Quick and Fiery Interactive Graphs Scatter, Pie, Line, Bubble, Bar, Error, Box, Histograms Heatmaps, Funnel, Waterfall, Candlestick, Distribution Plots Map Plotting Exercise – 11

2 Hours

19 Data Visualiza-tion - Plotly

Graph Transformation, Filtering, Aggregation, Grouping User Instructiveness with User Controller Creating Dashboards (Introduction) Project 10: “Weather Prediction System”

2 Hours

20


Kernel Nearest Neighbors (KNN) Working with sound data MFCC for speech processing Project 11: “Music Genre Prediction System”

2 Hours

21


Support Vector Machines (SVMs) The Hyperplane Concept Trade-off between biases-variances Project 12: “Finding the organic users on social media” Exercise – 12

2 Hours


22

Unsupervised Machine Learning

Clustering Theory K-Means Clustering Principal Component Analysis Project 13: “Detecting Real versus Fraud Credit Card Ap-

plications”

2 Hours

23

Deep Learning – Speech Recognition

What is Deep Learning? Understanding Neural Network Multi-layer perceptron Problem Libraries for Deep Neural Network – sklearn, pytorch, Ten-

sorFlow, Keras The MLP Classifier Project 14: “Speech Emotion Recognition”

2 Hours

24 Artificial Neu-ral Network

What is ANN? The basic terminology – Layers, weights, biases, activation

functions, losses, optimizers, learning rate The Concept of Forward Propagation Backward Propagation Gradient Descent & SGD

2 Hours

25 ANN - Tensor-Flow

ANN Using TensorFlow TensorFlow 1.x vs TensorFlow 2.x Project 15: “MNIST Image Classification Model”

2 Hours

26

Neural Net-work - Ad-vanced NLP

The Chatbots Connecting ML over Cloud NLP using ANN in TensorFlow Integrating Chatbot with other applications Project 16: “Live Web Chatbot”

2 Hours

27

Image Pro-cessing - OpenCV

What is Digital Image & its Processing Why is it necessary in AI Reading & Writing Images using OpenCV in python Changing Color-spaces, Geometric Transformations Image Thresholding, Filtering, Morphology Live Image Capturing Exercise – 13

2 Hours

28


Segmentation in Images Color Feature Detection in Images K-Means with Images K-Nearest Neighbors with Images Project 17: “My Selfie Machine”

2 Hours

29


Image Feature Detection, Extraction and Matching Harris Corners, SIFT, BRIEF, SURF Algorithms BFMatchers Histograms of Gradients Project 18: “Optical Character Recognition using KNN”

2 Hours

30


Face Detection on Live Images The Face Recognition Systems Project 19: “Real Time Face Recognition System”

2 Hours


31

Convolutional Neural Net-work

The Convolution Theory – Filters, Pools etc. Image Augmentation Exercise – 14 Project 20: “CNN based Object Recognition System”

2 Hours

32 Computer Vi-sion - CNN

The CNN Architectures – Caffe/Resnet Model Project 21: “Facial Expression Based Music Player”

2 Hours


Project 22: “Gender & Age Prediction System” 2

Hours


The CNN Architectures – Torch Vision (pytorch) Project 23: “Object Detection & Recognition Model”

2 Hours

35 Reinforcement Learning

Introduction with Reinforcement Learning What is Q-Learning Problem? The Cartpole Agent Concept The Q-Values, Rewards, Temporal Differences Priority Setup Double Deep Q Networks Project 24: “The Cartpole Agent Balancing”

2 Hours

36

Power of Artifi-cial Intelli-gence

Capstone Project: “Build Jarvis or FRIDAY or anything for your Personal Assistance having extensive features”

10 Hours

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