MACHINE LEARNING FOUNDATION

• What is Machine Learning
• Applications of Machine Learning
• Machine Learning vs Artificial Intelligence
• Machine Learning Languages and platforms
• Machine Learning vs Statistical Modelling

• Popular Machine Learning Algorithms
• Clustering, Classification and Regression
• Supervised vs Unsupervised Learning
• Application of Supervised Learning Algorithms
• Application of Unsupervised Learning Algorithms
• Overview of modeling Machine Learning Algorithm : Train , Evaluation and Testing.
• How to choose Machine Learning Algorithm?

• Simple Linear Regression : Theory, Implementing in Python (and R), Working on use case.
• Multiple Linear Regression : Theory, Implementing in Python (and R),

• Working on use case.

• K-Nearest Neighbors : Theory, Implementing in Python (and R), KNN advantages, Working on use case.
• Decision Trees : Theory, Implementing in Python (and R), Decision |Tree Pros and Cons, Working on use case.
• Random Forests : Theory, Implementing in Python (and R), Reliability of Random Forests, Working on Use Case.

• Naive Bayes Classifier: Theory, Implementing in Python (and R), Why Naive Bayes is simple yet powerful, Working on use case.
• Support Vector Machines: Theory,Support vector machines with Python and R, Improving the performance with Kernals, Working on Use Case.
• Association Rules: Theory, Implementing in Python (and R),Working on use case.
• Model Evaluation: Overfitting & Underfitting
• Understanding Different Evaluation Models

• • K-Means Clustering: Theory, Euclidean Distance method.
  • K-Means hands on with Python (and R)
  • K-Means Advantages & Disadvantages
  • Hierarchical Clustering : Theory
  • Hierarchical Clustering with Python (and R)
  • Hierarchical Advantages & Disadvantages

• • Dimensionality Reduction: Feature Extraction & Selection
  • Principal Component Analysis (PCA) : Theory, Eigen Vectors
  • PCA example with Python (and R) with Use case
  • Advantages of Dimensionality Reduction
  • Application of Dimensinality Reduction with case study.
  • Collaborative Filtering & Its Challenges

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

This course - Machine Learning Foundation, is designed to provide a holistic understanding of various ML algorithms with high level theory and hands on application of ML algorithms to classis data sets.

• Introduce Machine Learning with a holistic approach.
• Discuss high-level theory of popular Machine Learning Algorithms
• Hands-on coding of popular ML algorithms on classic data sets
• Access the knowledge through the International Association of Business Analytics (IABAC™) framework.

• Introduce Machine Learning with wholistic approach.
• Discuss high level theory of popular Machine Learning Algorithms
• Hands on coding of popular ML algorithms on classic data sets
• Access the knowledge through International Association of Business Analytics (IABAC™) framework.

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 lays a solid foundation for ML aspirants with high-level theory and concept along with hands-on coding of popular Machine Learning algorithms: Linear and Logistic Regression, K-means clustering, SVM (Support Vector Machines), KNN (K -Nearest Neighbours) and Neural Networks.

This course is a foundation level course, so most of the aspiring Machine Learning candidates can opt for this course

• Professionals aspiring to pursue a career in Machine Learning or Data Science in general
• Fresh college graduates, who are looking to career options in Data Science
• Senior professionals, who want to gain a solid foundation on Machine Learning to manager Data Science projects
• Candidates pursuing Data Scientist tracks

This course provides a solid foundation in Machine Learning as the syllabus is aligned with international market requirements. The candidates attending this course gain the right perspective on ML rather than getting lost in the ocean of articles and tutorials on the internet. As a part of the course, a certification assessment is conducted and candidates achieving minimum qualifying score receive a global certification, carrying immense value of the testimony of their 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 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 the 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 market requirements and be a part of this phenomenal Data Science era.

• PASSIONATE: We are passionate about enabling professionals with best practice Data Science skills
• ACCREDITED: DataMites™ is accredited with "International Association of Bussiness 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 Science lab with popular platforms, R, Python, Tensorflow etc.,

DataMites™ provide flexible learning options from traditional classroom training, lastest virtual live classroom to distance course. Based on your location preference, you may have one or more learning options

This course is perfectly aligned to the current industry requirements and gives exposure to all latest techniques and tools. The course curriculum is designed by specialists in this field and monitored improved by industry practitioners on continual basis.

All certificates can be validated with your unique certification number at IABAC.org portal. You also get candidate login at exam.iabac.org , where can find your test results and other relevant validation details.

The results of the Exam are immediate, if you take online test at exam.iabac.org portal. The certificate issuance, as per IABAC™ terms, takes about 7-10 bussiness days for e-certificate.

No, the exam fees are already included in the course fee and you will not be charged extra.

Course fee needs to be paid in one payment as it is required to block your seat for the entire course as well as book the certification exams with IABAC™. In case, if you have any specific constrains, your relation manager at DataMites™ shall assist you with part payment agreements

DataMites™ has a dedicated Placement Assistance Team(PAT), who work with candidates on individual basis in assisting for right Data Science job.

You get 100% refund training fee if you the training is not to your satisfaction but the exam fee will not be refunded as we pay to accreditation bodies. If the refund is due to your availability concerns, you may need to talk to the relationship manager and will be sorted out on case to case basis

DataMites™ provides loads of study materials, cheat sheets, data sets, videos so that you can learn and practice extensively. Along with study materials, you will get materials on job interviews, new letters with latest information on Data Science as well as job updates.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

• 1. Job connect
• 2. Resume Building
• 3. Mock interview with industry experts
• 4. Interview questions