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Artificial Intelligence(AI) Training In Nepal

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Last updated 2024/04/21

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Artificial Intelligence(AI) Training In Nepal
  • Schedule Black Duration 2 Months
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Introduction

The Artificial Intelligence(AI) training in Nepal class will teach you how to construct Artificial Intelligence and accelerated computing applications. The course concepts will be explained in a simple language that makes you easy to grasp. Ever since the invention of computers, their ability to perform various tasks grew exponentially. Humans have developed the capacity of computer systems to the next generation level concerning their usefulness in each and every segment of a market, along with their speed and reducing their size as time passed. AI is achieved by studying the thinking process of human brains, how humans learn, decide, and work while trying to find a solution. After learning the course we need to use the results of this study in developing intelligent computers and software. Enroll & Become Certified!

Artificial Intelligence training in Nepal is provided by TechAxis, the one of the Top AI Training Institute in Nepal. Artificial intelligence has existed since humans first began entering automation and related technologies. The Greeks had mythical stories about robots, the Chinese and the Egyptians also had achieved some advancement in this field. AI is continuously advancing at a fast rate as companies are discovering new ways to reap the benefits of this technology to automate processes, increase productivity, and reduce cost.

Over the recent years, this has triggered a substantial increase in the need for Artificial Intelligence certified professionals. However, a student often encounters difficulties while trying to find the best AI(Artificial Intelligence) training in Nepal. The Nepal education market is full of training institutes that provide AI training, but they aren't all reliable. our extended experience in this realm, we provide our students with real-time project-based training that will make you adept at AI, like professionals.

The Future of Artificial Intelligence in Kathmandu appears promising. There are lots of private and governmental organizations in Nepal especially in Kathmandu that have begun using Artificial Intelligence for automating a variety of tasks through machines and devices such as drones, robots, communication intercepting softwares, chatbots it.

    WHY ARTIFICIAL INTELLIGENCE(AI) TRAINING IN NEPAL?

    AI(Artificial Intelligence) has become an integral part of modern technology and is being used in various industries, including healthcare, finance, manufacturing, and more. AI training can be valuable for anyone looking to advance their career in the technology industry, increase their earning potential, and develop new and innovative products and services using AI technologies.

    • There is a high demand for AI professionals across various industries, and this trend is expected to continue.
    • With AI becoming increasingly important in the technology industry, learning AI can help individuals advance their careers and increase their earning potential.
    • AI technologies can automate repetitive tasks and improve efficiency, saving time and money for businesses.
    • AI can help businesses develop new and innovative products and services, leading to increased competitiveness and profitability.
    • AI is being used in various industries, such as healthcare, finance, manufacturing, and more, to solve complex problems and improve outcomes.


Course overview

Artificial Intelligence commonly known as AI is something like machine intelligence. Typically, it is the intelligence demonstrated by machines in contrast to natural language displayed by human and other animals. AI research can be defined as the study of intelligent agent i.e. any device that perceive its environment and take quick decision like human being.

It is the theory and deployment of computer system able to perform task normally associated with human beings and other animals. The example of AI is: Robots, auto running car, Speech recognition. AI system normally take actions as living being. It is the technology which enables developer to build the machine that thinks, act, and react like human being.

Why AI Training ?

Rather than focusing on career options, opportunity, jobs, and salary let’s talk about passion. There is no-one in the world who doesn’t like machine where AI system is applied. If you have interest in building some of the best functional machine with no error, then AI is the thing you have to learn.

Everything in today’s world is depends on the machine, without this I can’t imagine my life. Can you? Though human itself build these machine but we mankind can’t perform task like machine. They don’t get tired of doing work. All you need to do is just supply power and fuel. Just imagine there is a world where all the machine does the task normally like human beings. Imagine a machine which serves you in every part of your daily life, Life would be too much easy.

Well AI is not about programming and frameworks, it’s about research and how can you develop a problem solving machine using AI. Once you found a solution then there are number of programmer to implement your ideas.

You can build application like traffic control, automating support, automating manufacturing, Fraud Detection, imposing laws without corruption, lie detector, Automatic running vehicle, and many more. Once you develop an application then that can be sold in millions of dollars.

There is a belief that computer will be as smart as human beings by 2040. There are lots of Tech companies who does AI research and take it as very serious note. You can find your career all over the world and make a secure future.

Career opportunity In AI

The career in AI can be realized within a variety of settings including private companies, public organization, education, health, military, and Government organization. Developing a useful application and giving it to your government is the best thing you can do. But working for a private as well as governmental organization is not a bad idea too.

Teaching Methodology

  • Handful of assignments, tutorials and lab test of each chapter.
  • Periodic feedback from Trainer and Trainee and do the required changes as per necessity.
  • Each trainee need to develop demo application on their own, taking assistance form trainer when ever required.

Prerequisites

  • Good command in english language
  • Good knowledge of Computer, Softwares
  • Basic Knowledge programming language like C/C++ and basic python would be a plus
  • Understanding of Softwares and Software installation.

Course content

courses | 2 Months

Introduction to Python

  • Concepts of Python programming
  • Configuration of Development Environment
  • Variable and Strings
  • Functions, Control Flow and Loops
  • Tuple, Lists and Dictionaries
  • Standard Libraries

Data Science Fundamentals

  • Introduction to Data Science
  • Real world use-cases of Data Science
  • Walkthrough of data types
  • Data Science project lifecycle

Introduction to NumPy

  • Basics of NumPy Arrays
  • Mathematical operations in NumPy
  • NumPy Array manipulation
  • NumPy Array broadcasting

Data Manipulation with Pandas

  • Data Structures in Pandas-Series and DataFrames
  • Data cleaning in Pandas
  • Data manipulation in Pandas
  • Handling missing values in datasets
  • Hands-on: Implement NumPy arrays and Pandas DataFrames

Data Visualization in Python

  • Plotting basic charts in Python
  • Data visualization with Matplotlib
  • Statistical data visualization with Seaborn
  • Hands-on: Coding sessions using Matplotlib, Seaborn packages

Exploratory Data Analysis

  • Introduction to Exploratory Data Analysis (EDA) steps
  • Plots to explore relationship between two variables
  • Histograms, Box plots to explore a single variable
  • Heat maps, Pair plots to explore correlations
  • Perform EDA to explore survival using titanic dataset

Introduction to Machine Learning

  • What is Machine Learning?
  • Use Cases of Machine Learning
  • Types of Machine Learning - Supervised to Unsupervised methods
  • Machine Learning workflow

Linear Regression

  • Introduction to Linear Regression
  • Use cases of Linear Regression
  • How to fit a Linear Regression model?
  • Evaluating and interpreting results from Linear Regression models
  • Predict Bike sharing demand

Logistic Regression

  • Introduction to Logistic Regression
  • Logistic Regression use cases
  • Understand use of odds & Logit function to perform logistic regression
  • Predicting credit card default cases

Decision Trees & Random Forest

  • Introduction to Decision Trees & Random Forest
  • Understanding criterion(Entropy & Information Gain) used in Decision Trees
  • Using Ensemble methods in Decision Trees
  • Applications of Random Forest
  • Predict passenger survival using Titanic Data set

Model Evaluation Techniques

  • Introduction to evaluation metrics and model selection in Machine Learning
  • Importance of Confusion matrix for predictions
  • Measures of model evaluation - Sensitivity, specificity, precision, recall & f-score
  • Use AUC-ROC curve to decide best model
  • Applying model evaluation techniques to Titanic dataset

Dimensionality Reduction using PCA

  • Unsupervised Learning: Introduction to Curse of Dimensionality
  • What is dimensionality reduction?
  • Technique used in PCA to reduce dimensions
  • Applications of Principle component Analysis (PCA)
  • Optimize model performance using PCA on SPECTF heart data

KNearest Neighbours

  • Introduction to KNN
  • Calculate neighbours using distance measures
  • Find optimal value of K in KNN method
  • Advantage & disadvantages of KNN

Naive Bayes Classifier

  • Introduction to Naive Bayes Classification
  • Refresher on Probability theory
  • Applications of Naive Bayes Algorithm in Machine Learning
  • Classify spam emails based on probability

K-means Clustering

  • Introduction to K-means clustering
  • Decide clusters by adjusting centroids
  • Find optimal 'k value' in K-means
  • Understand applications of clustering in Machine Learning
  • Segment hands in Poker data and segment flower species in Iris flower data

Support Vector Machines
  • Introduction to SVM
  • Figure decision boundaries using support vectors
  • Identify hyperplane in SVM
  • Applications of SVM in Machine Learning
  • Predicting wine quality using SVM
Advance Algorithm

  • Neural Networks
  • CNN, Deep Learning, LSTM etc.

Project Work

  • Project Work in developing the Machine Learning Model
  • Project Evaluation and Feedback.
  • Deploying Project

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