₹ 4,999

Description

*Python Basics + Computer Vision + Artificial Intelligence + Data Analytics + Machine Learning*

✅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

✅DAY – 1 Introduction to Python & Computer Vision | Python Installation & Installing Libraries | Basic CV| Reading an image | Display,Writing, Saving an Image | Draw a line, circle, rectangle | Draw a text string |Find and Draw Contours | Image Resizing | Blurring an Image

✅DAY – 2 Create Border around Images | Convert an image from one color space to another | Scaling, Rotating, Shifting, and Edge Detection | Erosion and Dilation of images | Denoising of colored images | OpenCV Bitwise AND, OR, XOR, and NOT | Play a video using OpenCV | Video acquisition from the camera | Video acquisition from the Mobile Camera

✅DAY – 3 OpenCV Haar Cascades - Face detection | Eye detection | Mouth detection | Full/partial body detection

✅DAY – 4 Multi-template Matching with OpenCV

✅DAY – 5 OCR a Document, Form with Tesseract & OpenCV

✅DAY – 6 Object Tracking using OpenCV

✅DAY – 7 Watermarking images with OpenCV

✅DAY – 8 Saving Key event video clips with OpenCV

✅DAY – 9 Determining Object Shape and Color with OpenCV

✅DAY – 10 Real-time Panorama & Image Stitching with OpenCV

✅DAY – 11 Barcode reader using Computer Vision

✅DAY – 12 OpenCV Video Augmented Reality

✅DAY – 13 Recognizing digits with OpenCV and Python

✅DAY – 14 Document Scanner using OpenCV

✅DAY – 15 Harry Potter Invisible Cloak

✅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

✅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

✅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