AI, Machine Learning & Data Analytics

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₹ 2,997
Description

Combo 1

6 Months Validity for each Course - 3 Certificate

🚀Artificial Intelligence

🚀Machine Learning

🚀Data Analytics


What You Will Get?

🔑Recorded videos
🔑Online support through forums
🔑What we are teaching is a year of experience in the Field, You could reduce your research time and learn in 30 days.
🔑All Video access for 6Months - Validity
🔑3 Certificate
🔑Download all source code
🔑PPTs
🔑Internship e Certificate
🔑Assignments


Detailed Agenda of 3 Courses below:

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

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

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