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.
Course Outline :- Machine Learning Training In Nepal
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?
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
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
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
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
Regularisation of Generalised Linear Models
Ridge and Lasso Regression
Methods of threshold determination and performance measures for classification score models
Tree Models using Python
Introduction to decision trees
Tuning tree size with cross validation
Introduction to bagging algorithm
Grid search and randomized grid search
ExtraTrees (Extremely Randomised Trees)
Partial dependence plots
Case Study & Assignment
Boosting Algorithms using Python
Concept of weak learners
Introduction to boosting algorithms
Extreme Gradient Boosting (XGBoost)
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
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
Unsupervised learning in Python
Need for dimensionality reduction
Principal Component Analysis (PCA)
Difference between PCAs and Latent Factors
Hierarchical, K-means & DBSCAN Clustering
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
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
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 café 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 n 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 p 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.