About the Machine Learning workshop
Machine learning is an integral part of the digital landscape, revolutionizing how we interact with technology on a daily basis. It is a subfield of artificial intelligence (AI) that empowers software applications to predict outcomes with high accuracy without being explicitly programmed. This dynamic field is behind many of the conveniences and advanced technologies we often take for granted when surfing the web, placing ads, evaluating creditworthiness, trading stocks, and more.
At its core, machine learning involves creating algorithms that process input data, perform statistical analysis, and predict outcomes. These algorithms learn from patterns and features in the data they process, making them adept at handling a wide range of tasks, from simple to complex. The power of machine learning lies in its ability to improve over time, adjusting its models as it receives new data.
One common use of machine learning is in search engines. Algorithms analyze user input and past search history to deliver more relevant results. This not only makes searching faster but also more efficient, as the system learns to prioritize results based on user preferences and behaviors.
Advertising is another area where machine learning shines. Ad placement algorithms analyze user demographics, browsing history, and other digital footprints to serve targeted advertisements. This not only enhances user experience by making ads relevant but also increases the effectiveness of ad campaigns.
In the financial sector, machine learning is used for credit scoring. Traditional credit scoring methods are often limited to a few financial indicators. Machine learning algorithms, however, can analyze a vast array of factors, including transaction history, social media activities, and even the device used for banking. This results in more accurate credit profiles, helping financial institutions manage risk more effectively.
Stock trading has also been transformed by machine learning. Algorithms can predict stock movements based on historical data, market conditions, and news articles. High-frequency trading algorithms can execute trades at speeds and volumes unattainable by human traders, capitalizing on minute price changes for profitable opportunities.
Machine learning is not limited to economic activities; it also extends to social applications. For example, dating apps use machine learning to analyze user preferences, interests, and interaction patterns to suggest potential matches. The recommendations become more personalized over time, increasing the likelihood of successful connections.
Despite its many benefits, machine learning also poses challenges. One of the biggest is the quality of data. Machine learning models are only as good as the data they are trained on. Poor-quality or biased data can lead to inaccurate or unfair outcomes. Therefore, ensuring data integrity is a crucial part of developing machine learning systems.
Another challenge is the complexity of models. As machine learning solutions become more advanced, they also become more difficult to understand and manage. This can lead to what is known as “black box” models, where the decision-making process is not transparent, making it hard to troubleshoot or explain.
Furthermore, ethical concerns are at the forefront of discussions about machine learning. Issues such as privacy, surveillance, and control over AI technologies are increasingly critical as these systems become more pervasive in our lives.
In conclusion, while machine learning offers significant advantages, it also brings forth challenges that require careful consideration. Just as these advanced algorithms assist in various sectors, services like facharbeit schreiben lassen are invaluable for students, helping them craft detailed, well-researched academic papers with expertise, thus enhancing their learning experience and academic success. As we continue to integrate machine learning into more aspects of our lives, we must address these challenges to ensure it contributes positively to society.
The Outcomes of Machine Learning Workshop
This workshop will cover the basic algorithm that helps us to build and apply prediction functions with an emphasis on practical applications. attendees, at the end of this workshop, will be technically competent in the basics and the fundamental concepts of Machine Learning such as:
- Understand components of a machine learning algorithm.
- Apply machine learning tools to build and evaluate predictors.
- How machine learning uses computer algorithms to search for patterns in data
- How to uncover hidden themes in large collections of documents using topic modeling.
- How to use data patterns to make decisions and predictions with real-world examples
- from healthcare involving genomics and preterm birth
- How to prepare data, deal with missing data and create custom data analysis solutions for different industries
- Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming
The Objective of Machine Learning Workshop
To expose the Faculty/ Research Scholars/ Students in emerging technologies in the areas of Data Science & analytics. This workshop provides practical foundation level training that enables immediate and effective participation in Big data And Data Science and other Analytics projects.
This data science course is an introduction to machine learning and algorithms. Participants will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.
Workshop Duration: 2-Days (7-8 hours each day)
Workshop Certification
- Certificate of Participation from AppsFluxus’18 in Association With EDC IIT-Roorkee.
- Certificate of merit from AppsFluxus’18 in Association With EDC IIT-Roorkee.
- Certificate of Coordination from AppsFluxus’18 in Association With EDC IIT-Roorkee.
- College will get Center of Excellence form AppsFluxus’18 in Association With EDC IIT-Roorkee.
Who should go for this workshop?
Predictions say 2019 will be the year Data Science finally becomes a cornerstone of business technology agenda. To stay ahead in the game, Data Science has become a must-know technology for the Graduates aiming to build a career in Data analyst.
What are the pre-requisites for this Course?
The above mentioned content is with respect to software part of machine learning and this requires perquisites like contestant should be aware of mathematics (example Engineering students or MBA Students who have stats or statistics as a subject or MCA)
Workshop Fee:
Rs.1200/- Per Participant Only (The fee includes training, certification, and Event registration and a Free Machine Learning software Tools to each Participant)
In case of any queries | please feel free to contact
Finland Labs
Phone : +1 -850 583 8080
(M), (011) 65544708(O)
Email : info@finlandlabs.com
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2-Days Machine Learning For Data Science Course Content (20% Theory & 80% Hands-On Session)
Introduction to Data Science
- types of Data
- Why Data
- Different types of Data
- Data Quality
- Law of Diminishing Returns
- Design for Scalability
Note: Extraction of Data
Introduction to analytics
Different Types of Analytics
Introduction to Intelligence
- Business Intelligence
- Artificial Intelligence (with respect to the software)
- Defining AI(Artificial Intelligence) and ML(Machine Learning)
- Expert System
Neural Networks
- How predictions are made in neural network
- How Back propagation Learning works
- Data Preparation
- Combating Over fitting
- Applying and training the neural network
- Explaining the network
- Case Study Using Weka Machine Learning Tool and Using R Language
Genetic Algorithm
- What are genetic algorithm
- Where to Use Genetic algorithm in Analytics
Business Analysis
- Reporting
- Managing
- Olap Tools
- Applications
- Power Builder
R
- Introduction to R
- Why R Language
- Basic Math
- Variable assignment
- Removing Variables with respect to R
- Numeric Data ( WRT to R)
- Character Data
- Dates
- Logical (with respect to R)
- Vectors
Machine learning
- Introduction to Data Mining
- What is data mining?
- Related technologies – Machine Learning, DBMS, OLAP, Statistics
- Data Mining Goals
- Stages of the Data Mining Process
- Data Mining Techniques
- Knowledge Representation Methods
- Applications
- Extracting : – Structered or Unstructured Data
Data preprocessing
- Data cleaning
- Data transformation
- Data reduction
- Discretization and generating concept hierarchies
- Installing Weka 3 Data Mining System
- Experiments with Weka – filters, discretization
Data mining implementation for machine learning
- Task relevant data
- Background knowledge
- Interestingness measures
- Representing input data and output knowledge
- Visualization techniques
- Experiments with Weka – visualization
Attribute-oriented analysis
- Attribute generalization
- Attribute relevance
- Class comparison
- Statistical measures
- Experiments with Weka – using filters and statistics
Attribute-oriented analysis
- Attribute generalization
- Attribute relevance
- Class comparison
- Statistical measures
- Experiments with Weka – using filters and statistics
Mining algorithms: Association rules
- Motivation and terminology
- Example: mining weather data
- Basic idea: item sets
- Generating item sets and rules efficiently
- Correlation analysis
- Experiments with Weka – mining association rules
Mining algorithms: Classification
- Basic learning/mining tasks
- Inferring rudimentary rules: 1R algorithm
- Decision trees
- Covering rules
- Experiments with Weka – decision trees, rules
Mining algorithms: Prediction
- The prediction task
- Statistical (Bayesian) classification
- Bayesian networks
- Instance-based methods (nearest neighbor)
- Linear models
- Experiments with Weka – Prediction
Mining algorithms: Prediction
- The prediction task
- Statistical (Bayesian) classification
- Bayesian networks
- Instance-based methods (nearest neighbor)
- Linear models
- Experiments with Weka – Prediction
Mining real data
Applying various data mining techniques to create a comprehensive and accurate model of the data. which could be analyzed and implemented for Machine learning Using R
Documentation
- ID – Parent Database, with M-n relationship
- that is one to many
- Partitioning methods: k-means, expectation maximization (EM)
- Hierarchical methods: distance-based agglomerative and divisible clustering
- Conceptual clustering: Cobweb
- Experiments with Weka – k-means, EM, Cobweb
Advanced techniques, Clustering, Machine Learning software and applications Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).Bayesian approach to classifying text
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1st Stage (To be held at Zonal Centers)
- Competitions will be held at different Zonal centers through workshop on Data Analysis Using R .
- All the participants who want to participate in AppsFluxus-2018 Championship are required to attend the workshop at any zonal center.
2nd Stage (Prelims rounds to be held at zonal center):
- Just after the workshop a Prelims Rounds of AppsFluxus-2018 will held at zonal center.
- Winners will be awarded with Certificate of Merit and will be eligible to participate in Final Rounds which will be held at IIT-Roorkee.
3rd Stage (FINAL Round to be held will be March 2018 at IIT-Roorkee):
- Winners of zonal centers will be called to participate in the biggest final round of AppsFluxus’18 championship will held at IIT-Roorkee
- Top 3 Teams will be awarded as winners of The AppsFluxus’18 championship & will be awarded & honored by EDC,IIT-Roorkee.
Grand Final Competition
Grand finale of all zonal center winners will be in March 2018 at IIT-Roorkee
Zonal Level Competition Prelims
Rounds will be held just after the completion of workshop at zonal center
Zonal Level Workshop
2-Days workshop on Data Analysis Using R will be organized by AppsFluxus’18 at zonal center
Free Machine Learning Software tool Kit to Each Participant
- Sample Codes
- PPTs
- Projects
- Software
- R Studio
- Tutorials
- Study Materials
Click Here to Download Complete Proposal
Zonal Competition:
- After the hand on theory and practical experience from the workshop, Zonal Round Competition will be conducted for the participants.
Benefits to the participants
- Learn & Interact with renowned Industry Experts.
- The Certificate of Participation in association with EDC IIT Roorkee.
- Free CD/DVD containing Software Resource Toolkit.
- Zonal center’s winners will be called to participate in the final round will be held at IIT-Roorkee.
- Top Teams will be awarded as winners with the certificate of honor & respected prizes at IIT-Roorkee
Benefits of association with APPSFLUXUS-2018
- Name and Logo including website link will be published on our official website mentioning that “You are our Official Zonal Partner”.
- Authorized Team will visit your College to organize the entire event.
- The chance to get signs the MOU between Finland labs New Delhi & Your estimated college.
- An email will be sent to more than 1 lack users of our web partners about your college publicity.
- Posters and Flexes will be sent to you for effective regional publicity.
- All India publicity through Web marketing will also be done.