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AI Masterclass 1

AI Masterclass

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330 students

This Certified Course on Artificial Intelligence is designed with extreme preference and caution to make the participants a complete insight into the multiple dimensions of AI using Python Language. Whilst the program provides the necessary adept to Python, its prime intention is to light the concepts of AI with special focus. much more in an elaborate, easy to understand models and with some application use cases

Total Duration: 25 Hrs                 Modules: 5                         Assignments: 15                             Capstone Project – 1

Module 1: Python – Introduction, Tools & Syntaxes
Module 2: Machine Learning Concepts & Algorithm Specifics
Module 3: Data Preparation, Deep Learning Algorithms & Applications
Module 4: Neural Networks & Natural Language Processing
Module 5: Capstone Project


Module 1: Python – Fundamentals, Tools & Syntaxes 

Key Learning Objectives: This module will cover the in & out of Python Programming. Tools and syntaxes of Python that come in handy while implementing ML / DL Algorithms will be on limelight during the sessions. Quizzes and additional learning modules will provide the necessary conceptual learning for beginners.
Tools Covered: Python IDLE

Lesson 1: Python Programming – Fundamentals 
Types of Programming Languages – Advantages of Python – Installation of Python – Data types & Operators in Python – Numerical Operations on Python – String Operations on Python – Loops in Python
Assignment 1: Swapping of Numbers Assignment 2: Password Authentication
Go the Extra Mile – Try Looping within a Loop for any specific operation

Lesson 2: Python – Tools | Syntaxes & Data Structures
Python Lists & Tuples – Functions – Conversions – Arguments – Function Calls – Void statements – Functions with Multiple Arguments – Building Custom Functions in Python
Assignment 3 – Palindrome Assignment 4: Tower of Hanoi
Go the Extra Mile – Write a python program that takes a list of strings as an input and using while loop, display the strings in the descending order.
Other Related Materials – Pdfs / PPT and Video Links Software Download Links – Python IDLE


Module 2: Machine Learning Concepts & Algorithm Specifics

Key Learning Objectives: This Module will cover the essentials of Machine Learning Concepts in Theory and Hands on Sessions. Whilst conceptual learning on ML is provided, algorithmic focus which the main Industrial Demand is also dealt with. With major focus on ML Concepts & Algorithmic Implementation, this module provides the complete insight into multiple functionalities of Machine Learning Domain

Tools Covered: Python IDLE, Jupiter Notebook

Lesson 3: Machine Learning Concepts
ML Concepts – Terminologies – Real Life Applications of ML – Python Libraries for ML – ML Model – Working with Data Set – ML Algorithms – ML Challenges & Implementation
Assignment 5 – Read Employee data as csv & plot it on Histogram
Go the Extra Mile – Learn and understand the ML concepts behind FB’s Friend Recommendations

Lesson 4: Data Preparation & Visualisation
Data Pre Processing – Data Loading for ML / DL Application – Reading data as csv – Numpy – Pandas – Understanding Data using Statistics – Key Terminologies – Class Distribution – L1 & L2 Normalisation – Data Visualisation Techniques

Lesson 5: Unsupervised Learning & Reinforcement Learning
Supervised – Unsupervised Learning – Types of Unsupervised Learning –– K Means Clustering – Application Demo – Reinforcement Learning
Assignment 6 – Data Clustering using K Means
Go the Extra Mile – Apply K Means Clustering to any applicable dataset and plot the results

Lesson 6: Supervised Learning & Algorithmic Application
Supervised Learning – Linear Regression – SVM Algorithm – Naïve Bayes Algorithm – Application Demo – Linear Regression & SVM
Assignment 7 – Load cancer Dataset and use SVM Algorithm to predict the disease
Go the Extra Mile – Compare the accuracy levels for the same application for NB & SVM


Module 3: Deep Learning Algorithms & Applications

Key Learning Objectives: This Learning module will provide a broader perspective into the domain of AI with relevance to Data Preparation, DL Algorithms and its conceptual Application in real time scenarios. Attempt to learn with assistive guidance and LIVE Integrations with the mentor and get deep routed yourself to the domain of DL. Assignments & Quizzes make your learning process ease to go through.

Lesson 7: Deep Learning Algorithms & KERAS
Deep Learning Algorithms – KERAS Fundamentals – Installation & Configuration – LSTM Algorithm – ANN Overview & Application – Demo

Lesson 8: Deep Learning Algorithms & KERAS
Deep Learning Algorithms – CNN Algorithm Implementation – Application Demo – ANN Overview & Application – Demo


Module 4: Neural Networks & Natural Language Processing 

Key Learning Objectives: Participants in this module will get the final insights into the advanced AI Prospects of Neural Networks and a much more advanced concept of NLP – Natural Language Processing that integrates the AI Concepts into Real time scenarios. This Blended Learning system provides contemplated prospects of AI with deep explanatory insights and illustrative Hands on Demo Sessions with assignments to provide participants a better insight into the domain.

Lesson 9: Neural Networks – ANN & CNN
Neural Networks Introduction – ANN – CNN Concepts & Demo – Image Classification using CNN – Overview of GRU – Advanced Concepts of Neural Networks & Its Implications

Lesson 10: Natural Language Processing – Techniques & Process
Concepts of NLP – Components & Process of Natural Language Processing – Installation of NLTK – Tokenisation – Stemming – Lemmatization – Bag of Word ( Bow ) Model
LIVE PROJECT DEMO – Sentimental Analysis using Twitter Dataset


Module 5: Capstone Project 

• Prediction of Weather
• Forecast of Ground Water Level
• IPL Prediction
• COVID Data Analysis & Prediction Model

Curriculum is empty

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AI Masterclass 1
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  • Price
  • Instructor Pantech eLearning
  • Duration 30 Hrs
  • Enrolled 330 student

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