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Machine Learning Training In Nepal

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Last updated 2024/04/21

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Machine Learning Training In Nepal
  • Schedule Black Duration 2 Months
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Introduction

Generally machine learning can be defined as the scientific study of a set of instructions and statistical models which is used by any computer system to effectively perform a task or calculation relying on different patterns and inference rather than using explicit instructions. It can also be said as the subset of Artificial Intelligence (AI) and a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data such as from sensors and databases.

Machine learning training in Nepal has been developed with prime objectives of machine learning technology is to build algorithms that can get input data and leverage statistical analysis to predict an acceptable output value. Machine learning is expected to bring heavy changes to the world of technologies. It is a subfield of artificial intelligence and computer science that allows the software application to be more accurate in predicting results.

Machine learning training has plenty of advantages as it is used in so many industries of applications such as banking and financial sector, healthcare, social media, publishing and retail, publishing, etc. Due to machine learning, there are tools available to provide continuous quality improvement in complex and large process environments. It also helps in efficient utilization of resources and reducing time cycle.

Machine learning in these days has widely been used in data mining, natural language processing, computer version, biometrics, search engines, handwriting recognition, detection of credit card fraud, securities market analysis, strategy games and robotics. The best way to understand the potential of machine learning is to explore how people and companies are currently taking advantages of it. In a core, it is the process of granting a machine or model access to data and letting it learn for itself.

The prime focal point of machine learning research is to automatically learn to recognize complex patterns. The difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to able to produce a useful output in new cases.

Machine learning and pattern recognition, a feature is an individual measurable property or characteristics of a phenomenon being observed. Choosing informative and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression.

    WHY MACHINE LEARNING TRAINING IN NEPAL?

    Machine learning is a rapidly growing field of artificial intelligence that involves teaching machines to learn from data and improve their performance over time.

    • Machine learning can help you make better decisions by analyzing large volumes of data and identifying patterns and insights that might be difficult or impossible for humans to detect.
    • Machine learning can automate repetitive tasks and processes, allowing you to work more efficiently and focus on more strategic tasks.
    • Machine learning algorithms can improve accuracy and reduce errors in a variety of applications, including fraud detection, predictive maintenance, and medical diagnosis.
    • Machine learning is becoming increasingly important in many industries, including finance, healthcare, and manufacturing. By learning machine learning, you can gain a competitive advantage in your field and open up new career opportunities.
    • Machine learning is a highly sought-after skill in the technology industry. By learning machine learning, you can enhance your job prospects and open up opportunities for career advancement.



Course overview

The data and the information of any company is lifeblood of all the organization. In today’s competitive market machine learning helps you to keep the data safely of your clients, company and any other internal of external entity of your organization. Machine learning trained candidate are in higher demand in today’s prospect for small to large scale organization.

Why Machine learning training is important in today’s context?

  • Manufacturing Firm needs machine learning certified employee for predictive maintenance of the assets and monitoring the activities which are running in the firms.
  • Retailers needs machine learning certified employee for increasing the revenue of the company by increasing the sales volume which can be done through marketing.
  • Healthcare and life Sciences need machine learning certified employee for identification and risk satisfaction of diseases.
  • Travel and hospitality machine learning certified employee for knowing the updated information about the changing price of tours and travel
  • Financial Services need machine learning certified employee for analyzing the risk that organization can have in future and perform accordingly.

Career in Machine Learning

The evolution of machine learning is rapidly changing all around the world today. Machine learning include wider areas like technology, mathematics, statistics, and business ideas. The machine learning expert can build his handsome career as a data analyst in this field.

Machine learning training in Nepal helps you to work on most effective and efficient manner so that you will be so impressive in the eyes of any organization to get hired. Machine learning training helps in reducing the errors and pain while working so you can have the productive career in this field. Many innovation are invented by any companies like start of automation in order to develop the quality of the performance.

Machine Learning Training in Nepal has come with plenty of benefits after being certified. A person can build his/her career in machine learning at initial phase bye developing the application which performs some common task handled by human beings. The software developers can switch their career path in machine learning after the training. Having some sting knowledge in statistics, science and mathematics also can develop their career path in machine learning. A person with the good knowledge of programming and coding can easily shift their career in machine learning.Hence, we are here to reflect your career after the completion of training so feel free to contact us and gran the opportunities offered by machine learning.

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++ would be a plus
  • Understanding of Softwares and Software installation.

Course content

courses | 2 Months

Introduction to Data Science

  • What is data science and why is it so important?
  • Applications of data science
  • Various data science tools
  • Data Science project methodology
  • Tool of choice-Python: what & why?
  • Case study

Introduction to Python

  • Installation of Python framework and packages: Anaconda & pip
  • Writing/Running python programs using Spyder Command Prompt
  • Working with Jupyter notebooks
  • Creating Python variables
  • Numeric , string and logical operations
  • Data containers : Lists , Dictionaries, Tuples & sets
  • Practice assignment

Iterative Operations & Functions in Python

  • Writing for loops in Python
  • While loops and conditional blocks
  • List/Dictionary comprehensions with loops
  • Writing your own functions in Python
  • Writing your own classes and functions
  • Practice assignment

Data summary & visualization in Python

  • Need for data summary & visualization
  • Summarising numeric data in pandas
  • Summarising categorical data
  • Group wise summary of mixed data
  • Basics of visualisation with ggplot & Seaborn
  • Inferential visualisation with Seaborn
  • Visual summary of different data combinations
  • Practice assignment

Data Handling in Python using NumPy & Pandas

  • Introduction to NumPy arrays, functions & properties
  • Introduction to Pandas & data frames
  • Importing and exporting external data in Python
  • Feature engineering using Python

Generalised Linear Models in Python

  • Linear Regression
  • Regularisation of Generalised Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Methods of threshold determination and performance measures for classification score models
  • Case Study

Tree Models using Python

  • Introduction to decision trees
  • Tuning tree size with cross validation
  • Introduction to bagging algorithm
  • Random Forests
  • Grid search and randomized grid search
  • ExtraTrees (Extremely Randomised Trees)
  • Partial dependence plots
  • Case Study & Assignment

Machine Learning Basics

  • Converting business problems to data problems
  • Understanding supervised and unsupervised learning with examples
  • Understanding biases associated with any machine learning algorithm
  • Ways of reducing bias and increasing generalisation capabilites
  • Drivers of machine learning algorithms
  • Cost functions
  • Brief introduction to gradient descent
  • Importance of model validation
  • Methods of model validation
  • Cross validation & average error

Support Vector Machines(SVM) & kNN in Python

  • Introduction to idea of observation based learning
  • Distances and similarities
  • k Nearest Neighbours (kNN) for classification
  • Brief mathematical background on SVM/li>
  • Regression with kNN & SVM
  • Case Study

Unsupervised learning in Python

  • Need for dimensionality reduction
  • Principal Component Analysis (PCA)
  • Difference between PCAs and Latent Factors
  • Factor Analysis
  • Hierarchical, K-means & DBSCAN Clustering
  • Case study

Text Mining in Python

  • Gathering text data using web scraping with urllib
  • Processing raw web data with BeautifulSoup
  • Interacting with Google search using urllib with custom user agent
  • Collecting twitter data with Twitter API
  • Naive Bayes Algorithm
  • Feature Engineering with text data
  • Sentiment analysis
  • Case study

Version Control using Git and Interactive Data Products

  • Need and Importance of Version Control
  • Setting up git and github accounts on local machine
  • Creating and uploading GitHub Repos
  • Push and pull requests with GitHub App
  • Merging and forking projects
  • Introduction to Bokeh charts and plotting
  • Examples of static and interactive data products
  • Case study

Project Work
  • Project Work
  • Project Evaluation and Feedback.
  • Deploying Project

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