Professional certification in Automation & Artificial Intelligence

MACHINE LEARNING TRAINING

Our IT training courses are developed with industry-standards and career-focused technologies.


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Duration & Study Mode:
Full time, Part-time, Evening and Weekends, Virtual online
Location:
London, Flexible online
Duration: 5 Days / 5 Weeks

Machine Learning is another subset of Artificial Intelligence (AI), Information Technology, and Computer Science that focuses on manipulating data and algorithms to mimic the learning process of the human being and enhance the accuracy of the learning process. We have already entered into a new era of science and technology where branches like AI, NL (Machine Learning), Deep Learning, Neural Network, and Natural Language Processing have started shaping our personal and professional life in a new manner. Knowing about the best practice of ML is now being considered compulsory for IT and Business people. 

Prerequisites

  • Participants of this course must have some basic knowledge of high-level programming languages like Python.
  • You are required to have a fundamental knowledge of statistics and high-school mathematics.

What will you gain after this course

  • With the help of Machine Learning, you can quickly identify the patterns and trends of ML.
  • The implementation of ML can ensure continuous improvement of the business process and explore a wide range of applications that are relevant to the business process.
  • It is possible to execute rapid analysis prediction and processing with the help of Machine Learning.

Jobs you can get
with a MACHINE LEARNING TRAINING

  • Machine Learning Engineer
  • Senior Machine Learning Engineer
  • Machine Learning Pipeline Engineer
  • Data Scientist – Defence Analytics
  • Machine Learning Engineer – Level II

Corporate Group Training

  • Customized Training
  • Onsite / Virtual
  • Instructor-led Delivery
  • For small to large groups

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Who is this certification for?

  • IT professionals or Business Individuals who have a keen interest may consider getting enrolled in this course
  • You are required to have some fundamental knowledge of ML
  • Fresh Graduates from Information Technology or Computer Science may consider getting enrolled in this course
  • IT professionals who already have some experience working with Machine Learning may consider getting enrolled in this course to get a better insight into the subject matter
  • Machine Learning Engineer and Data Scientists may also get benefitted by enrolling in this course


Syllabus

  • ML Techniques overview
  • Validation Techniques (Cross-Validations)
  • Feature Reduction/Dimensionality reduction
  • Principal components analysis (Eigen values, Eigen vectors, Orthogonality)
  • Framing
  • Descending into Machine Learning
  • Reducing Loss
  • First Steps with TF
  • Generalisation
  • Validation Set
  • Representation
  • Feature Crosses
  • Logistic Regression
  • Classification
  • Neutral Networks
  • Training Neutral Sets
  • Multiclass Neural Nets
  • Embeddings
  • Production ML Systems
  • Static vs Dynamic Training
  • Static vs Dynamic Interference
  • Data Dependencies
  • Fairness
  • Distance measures
  • Different clustering methods (Distance, Density, Hierarchical)
  • Iterative distance-based clustering;
  • Dealing with continuous, categorical values in K-Means
  • Constructing a hierarchical cluster
  • K-Medoids, k-Mode and density-based clustering
  • Measures of quality of clustering
  • Model Assumptions, Probability estimation
  • Required data processing
  • M-estimates, Feature selection: Mutual information
  • Classifier
  • Linear learning machines and Kernel space,
  • Making Kernels and working in feature space
  • SVM for classification and regression problems

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