AI & ML with Python Project Based Summer Training

artificial intelligence training in Jaipur

Artificial Intelligence

Artificial Intelligence (  Data Science + Machine Learning + Deep Learning+Python)

This course is designed to make you learn world’s most demanding recent technologies in the market. It will start from basics of python programming and will cover data science libraries followed by machine learning algorithms and deep learning concepts, neural networks and tensor flow, KERAS deep learning model designing frameworks, natural language processing, and artificial intelligence algorithms. Here you will do things hands-on; also you will develop more than 30+ amazing projects. These projects will enhance your skills, confidence and profile for interviews in the companies. These fields are high in demand in the market and offering lucrative packages.

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.

Course : Building Artificial Intelligence Real Time Personal Assistant using Python
Certification By : TechieNest, An ISO 9001:2008 Certified Company
Study Material : Software and PDFs provided to each student
Fee : INR 14999 + Taxes INR 4999 + Taxes
Duration : 45 Days / 120 Hours

Step 1

Register online for any desired course, duration & location of your training course & obtain a Registration-ID. Registration-ID is a Unique Registration Number which is generated by our system after successful registration for training A student can have multiple IDs for multiple courses & batches. It is displayed while successful registration and it is also mailed to you immediately after registration by our server. if you don’t find it in your mail then, please check your SPAM folder or junk folder of your mail ID.

Step 2

Please deposit your Course fee to any one of our payment gateway/ Bank Account/ paytm.

Payment Gateway link:

Bank Account Details –
A/C Name: TechieNest Pvt. Ltd.
Bank A/C No: 201000689491
IFSC: INDB0000592
Bank Name: Indusind Bank Limited
Address: Malviya Nagar, Jaipur ( Rajasthan)
Paytm Number – 9251494002

Step 3

Update us regarding your fee payment by sending picture/scan copy of bank receipt to: [email protected] and you will receive a confirmation mail on your mail id.

When someone says yes you can do it….it means you can achieve it and when you decide to take an action we come with the surprising offers:

1. Group Discount:

Offer code: TNGD-5
Offer code: TNGD-10
Offer code: TNGD-15

  • If a group size is of: 5 -10 then 5% discount on training
  • 10-20 then 10% discount on training
  • 20 and above then 15% discount on training
2. Referral Offer:

Offer code: TNR3
Offer code: TNR5

  • 3% additional discount to the person who is referring
  • 5% additional discount to the one who is being referred
3. For Former students up to 15% off:

Offer code: TNFS15

  • There will be 15% discount on students who already did training
4. Previous Workshop attended students 5% off:

Offer code: TNPW5

5. 5% additional Discount for Campus Ambassador:

Offer code: TNA5

  •  No PRE-REQUISTIES required
  •  Curriculum designed by industry experts
  •  Application-based learning (Hands on>Case Study> Project)
  •  Industry-Specific Project (Retail, Healthcare, Entertainment, Airlines, Sports, E-commerce)
  •  A dedicated student support team
  •  Business Communication skill and resume making sessions by experts
  •  Exclusive Industry immersion sessions

Certification

All participants will get Certificate from TechieNest Pvt. Ltd. in association with Aavriti’18 IIT Bombay

Why TechieNest

  • Vast experience of having conducted Big Outreach Workshop collaborating with over 300+ colleges in all over India including IIT Bombay, IIT Hyderabad, IIT Bhubaneswar, IIT Jodhpur, IIT Mandi, NIT Raipur, MNIT Jaipur, MANIT Bhopal, NIT Jalandhar, NIT Patna, NIT Srinagar, IIIT Kalyani, BITS Pilani and likewise.
  • Trained more than 20,000 students in the field of EMBEDDED SYSTEMS & ROBOTICS, MATLAB & Machine Vision, Internet of Things, PLC_SCADA, PYTHON, C/C++, Andriod, VLSI & VHDL, JAVA and such top notch courses.
  • Our trainers are efficient in Raspberry pi, Arduino, PLCs, etc. which forms essential hardware in Electronic Industries nowadays.
  • Outreach workshop partner of Sanchaar-Wissenaire’18, IIT Bhubaneswar, 2017-18
  • Zonal workshop partner of Techkriti’18 IIT Kanpur, 2017-2018
  • Outreach workshop partner of Techfest’15 IIT Bombay & Techfest’16 IIT Bombay
  • Zonal workshop partner of Techkriti’17 IIT Kanpur, 2016-2017
  • Outreach workshop & Training partner of nVision’17 IIT Hyderabad, 2016-17
  • Outreach workshop partner of Ignus’17 IIT Jodhpur, 2016-17
  • AIRC’18 (All India Robotics Championship) in association with Techkriti’18 IIT Kanpur.
  • AIRC’17 (All India Robotics Championship) in association with nVision’17 IIT Hyderabad, 2016-17
  • Offering Project Based Training, Projects on Demand, Corporate Projects, Commercial Projects, and Consultancy in Engineering Projects.
    Dedicated 24×7 R&D lab.
  • Trained over 50+ international students in TechieNest Technology Transfer Program 2014-15.
  • TechieNest has Research Engineers having excellent research aptitude, teaching pedagogy who illustrates their finding through practical demos during workshop/training.
  • Manufacturer of Electronic products delivering the same across the country.

Full Course Structure

DAY 1 -Introduction & Python Recap ( 2.5 Hours)
  • 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
DAY 2 -Python Recap – 2 ( 2.5 Hours)
  • Comprehensions
  • Python User Defined Functions
  • Python Generators
  • Lambda Expressions
  • Python Modules: Usage and Installation
  • Understanding the OOP of Python
DAY 3 -Data & DBMS in Python ( 2.5 Hours)
  • Types of DATA?
  • The key steps of Data Analysis
  • The file handling in python
  • Dealing with Excel/Json/CSV/txt files
  • Connecting an SQL based Database
  • Basic SQL Operations using python

Exercise – 0

DAY 4 -Numpy – ( 2.5 Hours)
  • 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

DAY 5 -Pandas – ( 2.5 Hours)
  • The Series and DataFrame
  • Creating, Accessing, Manipulating Pandas Data
  • Series and DataFrame Attributes & Basic Functions
  • Iteration on Data
  • Statistical Functions• String Functions
  • Logical Indexing
  • Merging, Joining & Concatenation of Data
  • Sorting & Reindexing

Exercise – 2

DAY 6 - Pandas – 2 ( 2.5 Hours)
  • Understanding Kaggle/Hackerearth Platforms
  • Pandas File Handling
  • Grouping Data
  • Function Application
  • Missing Data & Treatment
  • Date & Time Functionality

Exercise – 3

DAY 7 - Project – ( 2.5 Hours)

Project 1: “Movie Recommendation System”

DAY 8 - Data Visualization ( 2.5 Hours)
  • How Data is Beautiful?
  • Visualization Libraries in Python
  • MATPLOTLIB PYPLOT: line, scatter, pie, box, area etc
  • Decorating the plots using Matplotlib (labels, colors, markers, legend, grids, figure sizes etc)
  • The Subplots and axes in matplotlib
  • Showing Images

Exercise – 4

 

DAY 9 - Data Visualization ( 2.5 Hours)
  • 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 Billing & Tips Data”

DAY 10 - Machine Learning – Regression ( 2.5 Hours)
  • 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

Exercise – 6

Project 3: “Real-estate House Price Predictor Model”

DAY 11 - Machine Learning – Regression ( 2.5 Hours)
  • Multiple Linear Regression: Case-study
  • Polynomial Regression: The Non-linearity in Data

Exercise – 7

Project 4: “Startup Profit Prediction System”

DAY 12 - Machine Learning – Classification ( 2.5 Hours)
  •  Logistic Regression: Concept
  •  Dealing with missing data in real-time information

Exercise – 8

Project 5: “Flower Species Classification Model”

DAY 13 - Machine Learning – Classification ( 2.5 Hours)
  • The Information Theory
  • Decision Trees Classifier
  • Random Forest Classifier
  • Extended Gradient Boost Classifier

Project 6: “Heart Disease Detection App”

DAY 14 - Natural Language Processing – Intro ( 2.5 Hours)
  • 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
  • NLTK in Python

Exercise – 9

Project 7: “Fake News Detection System”

DAY 15 - Machine Learning – Naïve Bayes ( 2.5 Hours)
  •  The Probability
  •  Bayes Theorem
  •  Naïve Bayes Algorithm for Machine Learning
  •  SMTP with Python
  • Reading and Sending Mail from Python

Project 8: “Email SPAM Detection Application”

DAY 16 - Deployment of ML Models ( 2.5 Hours)
  • Introduction with Flask
  • Deployment of Machine Learning Models over GITHUB and Cloud Services (AWS/ Google/ and similar platforms)
DAY 17 - Data Visualization – Seaborn ( 2.5 Hours)
  •  Easy and advanced Data Visualization from Seaborn
  •  Categorical, Distributive, Regression, Matrix, Grid Plots
  •  Customizing Color Themes
  •  Some Datastore

Exercise – 10

DAY 18 - Data Visualization – Plotly ( 2.5 Hours)
  • 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
  • 3D Charts
  • Map Plotting

Exercise – 11

DAY 19 - Data Visualization – Plotly ( 2.5 Hours)
  • Graph Transformation, Filtering, Aggregation, Grouping
  •  User Instructiveness with User Controller
  • Creating Dashboards (Introduction)

Project 9: “Weather Prediction System”

DAY 20 - Machine Learning – Classification ( 2.5 Hours)
  • Kernel Nearest Neighbors (KNN)
  •  Working with sound data
  •  MFCC for speech processing

Project 10: “Music Genre Prediction System”

DAY 21 - Machine Learning – Classification ( 2.5 Hours)
  • Support Vector Machines (SVMs)
  •  The Hyperplane Concept
  •  Trade-off between biases-variances

Exercise – 12

Project 11: “Finding the organic users on social media”

DAY 22 - Unsupervised Machine Learning ( 2.5 Hours)
  • Clustering Theory
  • K-Means Clustering
  • Principal Component Analysis
  • Self-Organizing Maps (SOMs)

Project 12: “Detecting Real versus Fraud Credit Card Applications”

DAY 23 - Deep Learning – Speech Recognition ( 2.5 Hours)
  • What is Deep Learning?
  • Understanding Neural Network
  • Multi-layer perceptron Problem
  • Libraries for Deep Neural Network – sklearn, pytorch, TensorFlow, Keras
  • The MLP Classifier

Project 13: “Speech Emotion Recognition”

DAY 24 - Artificial Neural Network ( 2.5 Hours)
  • 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
DAY 25 - ANN – TensorFlow ( 2.5 Hours)
  • Using TensorFlow Library for ANN
  • TensorFlow 1.x vs TensorFlow 2.x

Project 14: “MNIST Image Classification Model”

DAY 26 -ANN – Keras ( 2.5 Hours)
  • Using Keras Library for ANN
  • Building and Compiling real-time Sequential Neural Network Model

Project 15: “Building Churn Prediction System for Customers/Employees”

DAY 27 - Neural Network – Advanced NLP ( 2.5 Hours)
  • The Chatbots
  • Connecting ML over Cloud
  • NLP using ANN in TensorFlow
  • Integrating Chatbot with other applications

Project 16: “Live Web Chatbot”

DAY 28 - Recurrent Neural Network ( 2.5 Hours)
  • What is RNN
  • How RNN is different from ANN
  • The Concept of Long-Short-Term Memory (LSTM)
  • LSTM based Neural Networks for Future Prediction!!

Project 17: “Stock Market Price Prediction”

DAY 29 - Image Processing – Opencv ( 2.5 Hours)
  • 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

DAY 30 - Image Processing – Opencv ( 2.5 Hours)
  • Segmentation in Images
  • Color Feature Detection in Images
  • K-Means with Images
  • K-Nearest Neighbors with Images

Project 18: “My Selfie Machine”

DAY 31 - Image Processing – Opencv ( 2.5 Hours)
  • Image Feature Detection, Extraction and Matching
  • Harris Corners, SIFT, BRIEF, SURF Algorithms
  • BFMatchers
  • Histograms of Gradients

Project 19: “Optical Character Recognition using KNN”

DAY 32 - Image Processing – Opencv ( 2.5 Hours)
  • Face Detection on Live Images
  • The Face Recognition Systems

Project 20: “Real Time Face Recognition System”

DAY 33 - Convolutional Neural Network ( 2.5 Hours)
  • The Convolution Theory – Filters, Pools
  • Image Augmentation

Exercise – 14

Project 21: “CNN based Object Recognition System”

DAY 34 - Computer Vision – CNN ( 2.5 Hours)
  • Extending CNN with RNN (LSTM) Approach

Project 22: “Automatic Image Caption Generator”

DAY 35 - Computer Vision – CNN ( 2.5 Hours)
  • The CNN Architectures – Caffe/Resnet Model

Project 23: “Facial Expression Based Music Player”

DAY 36 - Computer Vision – CNN ( 2.5 Hours)

Project 24: “Gender & Age Prediction System”

DAY 37 - Computer Vision – CNN ( 2.5 Hours)
  • The CNN Architectures – Torch Vision (pytorch)

Project 25: “Object Detection & Recognition Model”

 

DAY 38 - Deep Neural Network Deployment ( 2.5 Hours)
  • Concept of Deep CNN

Project 26: “Realtime Surveillance System”

DAY 39 - Reinforcement Learning ( 2.5 Hours)
  • 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 27: “The Cartpole Agent Balancing”

DAY 40 - Transfer Learning ( 2.5 Hours)
  • What is Transfer Learning?
  • Approaches for TL
  • Transfer Learning with VGG, Resnet, Google Inception

Exercise – 15

DAY 41 - Transfer Learning ( 2.5 Hours)
  • Transfer Learning with word2vec and GloVe

Project 28: “Toxic comment detection on your social wall”

DAY 42 - Automatic Learning ( 2.5 Hours)
  • Auto-ML: The Game changer
  • Google Cloud/AWS with Auto-ML
DAY 43 - Automatic Learning ( 2.5 Hours)

Project 29: “Gesture Recognition System”

DAY 44 - Semi- Supervised Learning ( 2.5 Hours)
  • Theory of GAN
  • Generator Network
  • Discriminator Model
  • Calculating Loss

Project 30: “Artificial Face Image Generating Machine”

 

DAY 45- Power of Artificial Intelligence ( 2.5 Hours)

Project 31: “Build Jarvis or FRIDAY or anything for your Personal Assistance”