Internship on Python, Artificial intelligence and Data analytics

₹ 2,997

Internship on Python, Artificial Intelligence and Data Analytics📚

What You will Get?

✅Recorded videos ,Online support through forums when you practice Assignments .
✅All Video access for 3 Months
✅Download all source code
✅3 Internship E-Certificate
✅10+ Projects

What you will learn


✅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

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.
✅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
✅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
✅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
✅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
✅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

Data Analytics

✅Day-1: Introduction to Artificial Intelligence, Data Analytics & Road Map to become a Data Scientist
✅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
✅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
✅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