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Artificial Intelligence (AI) and Machine Learning
in AI and Machine learning
Created by
Suganya P
About this course
Artificial Intelligence and
Machine Learning course
This course provides you with the learning through practical methods. As each topic has been covered, we would be working on the relevant programs and would be given many take home assignments to keep the learners have a diverse knowledge on each and every module.
There would be explanations
and real time implementations of AI
applications in market like Resume builder, ChatGPT and other AI tools. To make
the learners learn the core of every module.
This Program includes,
- Comprehensive curriculum for
working professional and freshers too.
- Live classes 2 days a week to explain the concepts, work on
programming concepts and doubt clearing sessions.
- Every live class would be an
interactive class with class assignments and discussing assignments from
previous classes.
- We also provide live sessions with industry experts in the
respective fields to help understand the marketing perspective and Real
time activities.
- As this would be more practical and project driven, learners would
have to complete industry relevant projects before moving to the further
modules.
- Every class would be for 1.30 to 2 hours including the doubt clearing
sessions, and time can be extended according to the learner needs.
- There would be Project discussion live session to help the learners
in understanding the project and helps them in the implementation.
- All the live class assignments, take home exercise and Projects will
be stored in the Git-Hub for learners for their future references.
Is it worth spending your time and money to our
course? We understand your doubts and questions,
We would like to answer your questions with some real time data, about how the AI/ML is growing in the market and how this helps in your career growth, With the growing market in the Artificial Intelligence and Machine Learning. Our course provides you learning experience with real time programming knowledge to get you ready for the AI market immediately.
For your reference:
https://explodingtopics.com/blog/ai-statistics
https://market.us/report/artificial-intelligence-market/
Comments (1)
Tenaw Derseh
Student
13 Jan 2024 | 02:25 pm
This great information for learners
Python Basics
8 Parts
- 12:10 Hr
Introduction to AI and ML
Presentation on Introduction to AI and ML course, Live class details, Project Assignment details, Google colab usage
60 Min
Attachments:
Python Basics
Basics of python programming, Introduction to operators and Numbers
90 Min
Attachments:
Python data structures
Practical session to understand the Python string, Data structures, Boolean and Looping Statements
90 Min
Attachments:
Python classes and Functions
Exploring the Python classes and objects, Functions, Math functions etc
90 Min
Attachments:
Python and OOPS
OOPS concepts, Error and Exception Handling with Implementation
90 Min
Attachments:
Numpy and Pandas
Introduction and Implementation of Numpy and Pandas
120 Min
Attachments:
Python Regular Expression
Regular Expression in Python
100 Min
Attachments:
Doubt clearing and Assignment validation
90 Min
Attachments:
Data Analysis
3 Parts
- 5:30 Hr
Predictive Analytics
5 Parts
- 7:30 Hr
Predictive Analytics Introduction
Introduction to Predictive Analytics, Measure of spread, Sampling
120 Min
Attachments:
Probability and statistics
Probability and statistics learning and Implementation
90 Min
Attachments:
Random variables and Distribution
Understand the concepts of Random variable and Confidence Interval
60 Min
Attachments:
Introduction to Regression
Introduction to the concepts of Regression
90 Min
Attachments:
Doubt clearing and Assignment validation
90 Min
Attachments:
Machine Learning
9 Parts
- 21:00 Hr
Introduction to Machine Learning
Introduction to ML, its types, Various steps involved in ML
120 Min
Attachments:
Linear Regression Algorithms
Implementation of Regression Models
150 Min
Attachments:
Logistic Regression
Logistic Regression and Implementation
150 Min
Attachments:
SVM,KNN and Naive Bayes Algorithm
Explanation and Implementation of SVM,KNN and Naive bayes
150 Min
Attachments:
Decision trees and Random Forest Algorithms
Introduction and Implementation of Decision Trees and Random Forest
150 Min
Attachments:
ML model boosing and deployment
Implementation of Model optimization techniques, Model boosting and Model deployment
150 Min
Attachments:
Unsupervised ML Algorithms
Introduction and Implementation of Unsupervised ML Algorithms
150 Min
Attachments:
Time series Analysis
Introduction and Implementation to Time Series Analysis
150 Min
Attachments:
Explanation and doubt clearning on 2 projects of NLP
90 Min
Attachments:
Deep Learning
9 Parts
- 21:00 Hr
Introduction to Deep Learning
Introduction to DL, Preceptron, Gradient design
150 Min
Attachments:
Introduction to Tensoflow
Introduction to Tensoflow, Tensor Board and Implementation
120 Min
Attachments:
CNN
Convolutional Neural Networks explanation and implementation
150 Min
Attachments:
Regional – CNN
Regional – CNN explanation and implementation
150 Min
Attachments:
RNN
Recurrent Neural Networks
150 Min
Attachments:
Computer vision with OpenCV
Computer vision with OpenCV explanation and implementation
150 Min
Attachments:
Object Detection using YOLO
Object Detection using YOLO
150 Min
Attachments:
Data Tagging for Computer vision
Data Tagging for Computer vision
150 Min
Attachments:
DL Project Discussion and Doubt clearing
90 Min
Attachments:
Natural Language Processing
5 Parts
- 11:30 Hr
Introduction to NLP
Introduction to NLP, Libraries
150 Min
Attachments:
Phases of NLP
Phases of NLP , Text Classification
150 Min
Attachments:
Text Classification, Analysis and segregating text
Text Classification, Analysis and segregating text
150 Min
Attachments:
Sentiment Analysis and chatbot using NLP
• Sentiment Analysis and chatbot using NLP
150 Min
Attachments:
NLP Project Discussion and doubt clearing
90 Min
Attachments:
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