3 Month Internship on Artificial Intelligence

3 Month Internship in Artificial Intelligence & Data Science

Artificial Intelligence + Machine Learning + Data Analytics + Python + Deep Learning

What you will get

✅ Complete Video recordings of the course

✅ Unlimited Weekend Live Session – Bootcamp

✅ All attachment Download – PPT,

✅ Private Community Access

✅ E-Internship Certificate

✅ Lifetime course validity

Sample Internship Certificate

Curriculum of Artificial Intelligence:

• DAY – 1 Overview of this course | Introduction to AI | How to create basic AI application (Chat bot using DialogFlow)
• DAY – 2 How to install Python & Libraries | Basics of python Programming for AI.
• COMPUTER VISION
• DAY – 3 Introduction to Computer Vision| How to install computer vision libraries
• DAY – 4 Moving Object Detection and tracking using OpenCV
• DAY – 5 Face Detection and Tracking using OpenCV
• DAY – 6 Object Tracking based on color using OpenCV
• DAY – 7 Face Recognition using OpenCV
• DAY – 8 Face Emotion recognition using 68-Landmark Predictor OpenCV
• DEEP LEARNING
• DAY – 9 Introduction to Deep learning | How to install DL libraries
• DAY – 10 Designing your First Neural Network
• DAY – 11 Object recognition from Pre-trained model
• DAY – 12 Image classification using Convolutional Neural Network
• DAY – 13 Hand gesture recognition using Deep Learning
• DAY – 14 Leaf disease detection using Deep Learning
• DAY – 15 Character recognition using Convolutional Neural Network
• DAY – 16 Label reading using Optical Character recognition
• DAY – 17 Smart Attendance system using Deep Learning
• DAY – 18 Vehicle detection using Deep Learning
• DAY – 19 License plate recognition using Deep Learning
• DAY – 20 Drowsiness detection using Deep Learning
• DAY – 21 Road sign recognition using Deep Learning
• MACHINE LEARNING
• DAY – 22 Introduction to Machine learning| How to install ML libraries
• DAY – 23 Evaluating and Deploying the various ML model
• DAY – 24 Fake news detection using ML
• DAY – 25 AI snake game design using ML
• NATURAL LANGUAGE PROCESSING
• DAY – 26 Introduction to NLP & it’s Terminology | How to install NLP Libraries NLTK
• DAY – 27 Title Formation from the paragraph design using NLP
• DAY – 28 Speech emotion analysis using NLP
• DEPLOYING AI IN HARDWARE
• DAY – 29 Cloud-based AI, Object recognition using Amazon Web Service (AWS) & Imagga
• DAY – 30 Deploying AI application in Raspberry Pi with Neural Compute stick & Nvidia Jetson Nano

Curriculum of Machine Learning:

• Day-1: Overview A.I | Machine Learning
• Day-2: Introduction to Python | How to write code in Google Colab, Jupyter Notebook, Pycharm & IDLE
• SUPERVISED LEARNING – CLASSIFICATION & REGRESSION
• Day-3: Advertisement Sale prediction from an existing customer using LOGISTIC REGRESSION
• Day-4: Salary Estimation using K-NEAREST NEIGHBOR
• Day-5: Character Recognition using SUPPORT VECTOR MACHINE
• Day-6: Titanic Survival Prediction using NAIVE BAYES
• Day-7: Leaf Detection using DECISION TREE
• Day-8: Handwritten digit recognition using RANDOM FOREST
• Day-9: Evaluating Classification model Performance using CONFUSION MATRIX, CAP CURVE ANALYSIS & ACCURACY PARADOX
• Day-10: Classification Model Selection for Breast Cancer classification
• Day-11: House Price Prediction using LINEAR REGRESSION Single Variable
• Day-12: Exam Mark Prediction using LINEAR REGRESSION Multiple Variable
• Day-13: Predicting the Previous salary of the New Employee using POLYNOMIAL REGRESSION
• Day-14: Stock price prediction using SUPPORT VECTOR REGRESSION
• Day-15: Height Prediction from the Age using DECISION TREE REGRESSION
• Day-16: Car price prediction using RANDOM FOREST
• Day-17: Evaluating Regression model performance using R-SQUARED
• INTUITION & ADJUSTED R-SQUARED INTUITION
• Day-18: Regression Model Selection for Engine Energy prediction.
• UNSUPERVISED LEARNING – CLUSTERING
• Day-19: Identifying the Pattern of the Customer spent using K-MEANS CLUSTERING
• Day-20: Customer Spending analysis using HIERARCHICAL CLUSTERING
• Day-21: Leaf types data visualization using PRINCIPLE COMPONENT ANALYSIS
• Day-22: Finding Similar Movie based on ranking using SINGULAR VALUE DECOMPOSITION
• UNSUPERVISED LEARNING – ASSOCIATION
• Day-23: Market Basket Analysis using APIRIORI
• Day-24: Market Basket Optimization/Analysis using ECLAT
• REINFORCEMENT LEARNING
• Day-25: Web Ads. Click through Rate optimization using UPPER BOUND CONFIDENCE
• Natural Language Processing
• Day-26: Sentimental Analysis using Natural Language Processing
• Day-27: Breast cancer Tumor prediction using XGBOOST
• DEEP LEARNING
• Day-28: Bank Customer classification using ANN
• Day-29: Pima-Indians Diabetes Classification using CONVOLUTIONAL NEURAL NETWORK
• Day-30: A.I Snake Game using REINFORCEMENT LEARNING

Curriculum of Data Analytics:

• Day-1: Introduction to Artificial Intelligence, Data Analytics & Road Map to become a Data Scientist
• EXCEL
• Day-2: Data Preparation – Power Query & Tables
• Day-3: Data analytics- Formula & Pivot Table
• Day-4: Story Telling – Charts & Dashboard
• Day-5: Automation – VBA Macros & Power Query
• STATISTICS & PROBABILITY
• Day-6: Descriptive Statistics – Mean, Mode, Median, Quartile, Range, InterQuartile Range, Standard Deviation
• Day-7: Probability – Permutations, Combinations
• Day-8: Population and Sampling
• Day-9: Probability Distributions – Normal, Binomial and Poisson Distributions
• Day-10: Hypothesis Testing & ANOVA – One Sample and Two Samples – z Test, t-Test, F Test and Chi-Square Test
• BI tools – Tableu
• Day-11: Connect Tableau to a Variety of Datasets
• Day-12: Analyze, Blend, Join, and Calculate Data
• Day-13: Visualize Data in the Form of Various Charts, Plots, and Maps
• BI tools – Power BI
• Day-14: Connect Tableau to a Variety of Datasets
• Day-15: Visualize Data in the Form of Various Charts, Plots, and Maps and Calculate Data
• Python
• Day-16: Introduction to Python & Installing Python and its Libraries
• Day-17: Basic Python Programming for Data Analytics
• Numpy & Pandas
• Day-18: Python Numpy functions
• Day-19: Pandas for Data analytics in Python
• Data Visualization
• Day-20: Matplotlib for data visualization
• Day-21: Seaborn for data visualization
• Kaggle Exploratory
• Day-22: Kaggle Dataset and Notebooks
• Database – SQL
• Day-23: SQL basics for Data analytics – Part-1
• Day-24: SQL basics for Data analytics – Part-2
• Database – MongoDB
• Day-25: MongoDB basics for Data analytics
• Machine Learning
• Day-26: Introduction to Machine Learning & its libraries
• Day-27: Evaluating and Deploying Machine Learning Classification algorithm for classification of State of Electric power system
• Deep Learning
• Day-28: Introduction to Deep Learning & its libraries
• Day-29: Covid-19 Detection using X-Ray Images with CNN
• Natural Language Processing
• Day-30: Tag Identification system using NLTK

Curriculum of Python:

• Introduction to Python
• Installing & Working with Python IDLE
• Configuring Environmental Variables – Command Window
• Installing Anaconda Navigator (Jupyter Notebook)
• Working with Anaconda Navigator (Spyder Notebook)
• Working with Google Colab
• Working with Pycharm
• Working with Libraries
• Simple Arithmetic
• Introduction to Strings
• Indexing and Slicing with Strings
• String Properties and Methods
• Print Formatting with Strings
• Lists in Python
• Dictionaries in Python
• Tuples with Python
• Sets in Python
• Booleans in Python
• I/O with Basic Files in Python
• Python Objects and Data Structures
• Comparison Operators in Python
• Chaining Comparison Operators in Python with Logical Operators
• If Elif and Else Statements in Python
• For Loops in Python
• While Loops in Python
• Useful Operators in Python
• List Comprehensions in Python
• Methods and the Python Documentation
• Introduction to Functions
• Basics of Python Functions
• Logic with Python Functions
• Tuple Unpacking with Python Functions
• *args and **kwargs in Python
• Lambda Expressions, Map, and Filter Functions
• Attributes & Class Keyword
• Class Object Attributes and Methods
• Inheritance and Polymorphism
• Special(Magic/Dunder) Methods
• Modules and Packages
• name and “main”
• Errors and Exceptions Handling
• Pylint Overview
• Decorators with Python Overview
• Generators with Python
• Python Collections Module
• Opening and Reading Files and Folders
• Python Datetime Module
• Python Math and Random Modules
• Python Debugger
• Python Regular Expressions
• Timing Your Python Code
• Zipping and Unzipping files with Python
• Setting Up Web Scraping Libraries
• Grabbing a Title
• Grabbing an Image
• Book Examples
• Introduction to Images with Python
• Working with CSV Files in Python
• Working with PDF Files in Python
• Sending Emails with Python
• Receiving Emails with Python

Curriculum of Deep Learning:

• Section 1: Course Overview
• DAY–1 Introduction to Deep Learning
• DAY–2 Basic Computer Vision
• Section 2: Artificial Neural Network
• DAY–3 Neurons & Perceptron
• DAY–4 Activation Function
• DAY–5 Gradient Descent
• DAY – 6 Stochastic Gradient Descent
• DAY – 7 Backpropagation
• DAY – 8 Artificial Neural Network – Project 1
• Section 3: Deep Neural Network
• DAY – 9 Optimization Algorithms – SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
• DAY – 10 Batch Normalization
• DAY– 11 Hyperparameter tuning
• DAY– 12 Interpretability
• DAY– 13 Deep Neural Network – Project 2
• Section 4: Convolutional Neural Network
• DAY– 14 Convolutional Neural Network & its Layers
• DAY– 15 CNN Architecture
• Day–16 Different frameworks on Deep Learning (Tensorflow, Keras, PyTorch & Caffe)
• Day-17 Object Recognition using Pre Trained Model – Caffe – Project 3
• Day-18 Image classification using Convolutional Neural Network from Scratch – Tensorflow & Keras – Project 4
• Day-19 Custom Image Classification using Transfer Learning – Project 5
• Day-20 YOLO Object recognition – Project 6
• Day 21 Image Segmentation – Project 7
• Day 22 Project using MxNet – Project 8
• Day 23 Project using PyTorch – Project 9
• Day 24 Social Distancing detector – Project 10
• Day 25 Face Mask detector – Project 11
• Section 5: Recurrent Neural Network
• Day 26 Introduction to RNN and LSTM
• Day 27 Project using RNN – Project 12
• Section 6:
• Day 28 Introduction CUDA Toolkit and cuDNN for deep learning
• Day 29 Getting started with the Intel Movidius Neural Compute Stick – Project 13
• Day 30 Custom Object classification using Nvidia Jetson – Project 15

Frequently asked questions

1. Once I have Done the Payment where should I access the course?
   After the payment is done. Please signup to learn.pantechsolutions.net using the same mail id used for payment.
2. Where can I access the course?
   You can access the courses in our learning portal learn.pantechsolutions.net
3. What is the validity of the course?
   Each course’s validity Lifetime
4. Is it only Self Paced Learning or I will get a Live session?
   It is a Self-paced Learning. Apart from that you will be having live sessions on Saturday Bootcamp
5. Where can I clear my doubts?
   You can clear doubts in Weekly Bootcamp Session.
6. Will you provide job opportunities?
   Yes, but in Gold Membership
7. Can I Take More than One Course at a Time?
   Yes, you can each progress of the course will be noted respectively.
8. My Payment Did Not Go Through. What Do I Do?
   Please Mail us to learn@warriorsway.in
9. When do I get my internship Certificate?
   The internship Certificate will be generated automatically in the portal after completing the course for 100% . you can download from the portal for the respective course.