MACHINE LEARNING COURSE IN VIJAYAWADA

The following topics are covered in "Machine Learning"

Foundation:

Machine Learning Introduction: Supervised and Unsupervised Learning

• Linear Regression Theory
• Linear Regression Programming with R
• Working on Case Study

Multiple Linear Regression

• Theory behind multiple linear regression
• Multiple Linear Regression with R
• Working on Case Study

Decision Tree:

• Theory Behind Decision Tree
• Decision Tree with R
• Working on Case Study

Naive Bayes:

• Theory behind Naïve Bayes classifiers
• Naive Bayes Classifiers with R
• Working on Case Study

Support Vector Machines:

• Theory behind Support Vector Machines
• Support vector machines with R
• Improving the performance with Kernals
• Working on Case Study

Association Rule:

• Theory behind Association Rule
• Working on Case Studies

Expert:

Neural Net:

• Artificial Neural Network
• Connection Weights in Neural Network
• Generating Neural Network with R
• Improving Neural Network Accuracy with Hidden Layers
• Working on Case

Random Forest:

• Theory behind Random Forest
• Random Forest with R
• Improving performance of Random Forest
• Working on Case Study

Recommendation Engine:

• Theory behind Recommendation Engines
• Working on Case Study with R

Dimension Reduction:

• Theory behind Recommendation Engine
• Working on Case Studies

• Popular Machine Learning Algorithms
• Clustering, Classification and Regression
• Supervised vs Unsupervised Learning
• Choice of Machine Learning

• Simple and Multiple Linear Regression
• KNN etc...

• Theory of Linear Regression
• Hands on with use Cases

• Naïve Bayes for text classification
• New Articles Tagging

• K-means Clustering

• Tuning with Hyper Parameters
• Popular ML Algorithms
• Clustering, Classification and Regression
• Supervised vs Unsupervised
• Choice of ML Algorithm

• Ensemble Theory
• Random Forest Tuning

• Simple and Multiple Linear regression
• KNN

• Text Processing with Vectorization
• Sentiment analysis with TextBlob
• Twitter sentiment analysis.

• Basic ANN network for regression and classification

• Tensorflow work flow demo and intro to deep learning

Machine Learning (ML) is a different approach where computer learns the rules of solving complex problems without explicitly programmed. Machine Learning algorithms are at the core and important piece of data science.

Machine Learning Expert Course is designed in accordance with IABAC™ (International Association of Business Analytics Certifications) to provide a theory and application of popular Machine Learning algorithms in Supervised, Unsupervised and Deep Learning. Major algorithm are discussed in more details about how they work and apply these algorithms with realworld data.

Finally, we create ML models with Python Scikit-Learn package in solving business problems and case studies. This will give you a complete knowledge on Machine Learning algorithm, how they work, how to optimize, advantages and disadvantages of each algorithm along with practical application.

• Detailed Knowledge of popular Machine Learning algorithms: Supervised and Unsupervised
• Designing Machine Learning models from scratch to solve business problems with real world data
• Python Machine Learning package - Scikit-Learn
• Creating Machine Learning models with various ML algorithms and optimizes parameters for best performance
• Introduction to Deep Learning
• Preparing for IABAC™ Certification Exam

• Basic Statistics Knowledge
• Basic Python programming along with knowledge on Python packages Numpy and Pandas
• Machine Learning Foundation course from IABAC or similar demonstrable ML skills.

In recent years, Machine Learning has taken over a mainstream business and evolved has a career track by itself. A quick search in job portals reveals about 20,000 Machine Learning job opportunities on a daily basis in the USA alone. This course provides you with practical knowledge in Machine Learning on choosing ML algorithms to optimization Machine Learning models, which enables to deliver data science projects effectively.

This course is an expert level course and candidate seriously pursuing a career in Data Science can opt.

• Professionals aspiring to pursue a career in Machine Learning or Data Science in general
• Fresh colleague graduates with basic Machine Learning knowledge, who are looking to gain more expertise
• Candidates pursuing Data Scientist tracks

This course provides an expert level knowledge in Machine Learning opening a world of opportunities in the domain of Data Science, Artificial Intelligence, Robots, etc. This course enables candidates to further master the topic with various real-world project available in crowd sourced platforms such as Kaggle. As a part of the course, a certification assessment is conducted and candidate achieving minimum qualifying score receive a global certification, carrying immense value of the testimony of Expert Level ML knowledge.

At DataMites™, we truly believe and very excited about this big wave of Data Science. DataMites™ work with globally renowned Machine Learning experts in designing as well delivering a training course. There are millions of jobs and business opportunities in Data Science across the globe as of today and this is only going to increase exponentially in coming years.

DataMites is founded by a group of passionate Data Science evangelists with decades of experience in Analytics, big data and Data Science working with fortune 100 companies, across the globe. The mission of DataMites™ is to enable data science professionals with strong data science skills aligned to market requirements and be a part of this phenomenal Data Science era.

• PASSIONATE: We are passionate in enabling professionals with best practice Data Science skills
• ACCREDITED: DataMites™ is accredited with "International Association of Business Analytics Certifications (IABAC™)", aligning the syllabus with global market requirements
• HANDS ON PROJECTS: On course completion, worthy candidates are involved in consulting assignments at building block levels to provide real-time exposure, thus supporting them to gain the confidence to work in real-world Data Science projects
• JOB ASSISTANCE: A dedicated team, Placement Assistance Team (PAT), is tasked to assist candidates in preparing for the first Data Science job and mapping the job requirements to the individual candidate profile.
• FLEXIBLE LEARNING: DataMites™ provides flexible learning options from traditional classroom to Virtual classroom, Instructor led online and self-learning.
• LIBRARY: A Data Science library with a collection of valuable Data Science books and publications, assisted check-in/check-out options.
• DATA SCIENCE LAB: Access to Cloud Data S

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