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.
MACHINE LEARNING TRAINING
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admin
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- Curriculum
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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
Select your preferred training delivery mode
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Foundations for ML
<ul>
<li> ML Techniques overview</li>
<li> Validation Techniques (Cross-Validations)</li>
<li> Feature Reduction/Dimensionality reduction</li>
<li> Principal components analysis (Eigen values, Eigen vectors, Orthogonality)</li>
<li> Framing</li>
<li> Descending into Machine Learning</li>
<li> Reducing Loss</li>
<li> First Steps with TF</li>
<li> Generalisation</li>
<li> Validation Set</li>
<li> Representation</li>
<li> Feature Crosses</li>
<li> Logistic Regression</li>
<li> Classification</li>
<li> Neutral Networks</li>
<li> Training Neutral Sets</li>
<li> Multiclass Neural Nets</li>
<li> Embeddings</li>
</ul>
<li> ML Techniques overview</li>
<li> Validation Techniques (Cross-Validations)</li>
<li> Feature Reduction/Dimensionality reduction</li>
<li> Principal components analysis (Eigen values, Eigen vectors, Orthogonality)</li>
<li> Framing</li>
<li> Descending into Machine Learning</li>
<li> Reducing Loss</li>
<li> First Steps with TF</li>
<li> Generalisation</li>
<li> Validation Set</li>
<li> Representation</li>
<li> Feature Crosses</li>
<li> Logistic Regression</li>
<li> Classification</li>
<li> Neutral Networks</li>
<li> Training Neutral Sets</li>
<li> Multiclass Neural Nets</li>
<li> Embeddings</li>
</ul>
Machine Learning Engineering
<ul>
<li> Production ML Systems</li>
<li> Static vs Dynamic Training</li>
<li> Static vs Dynamic Interference</li>
<li> Data Dependencies</li>
<li> Fairness</li>
</ul>
<li> Production ML Systems</li>
<li> Static vs Dynamic Training</li>
<li> Static vs Dynamic Interference</li>
<li> Data Dependencies</li>
<li> Fairness</li>
</ul>
Clustering
<ul>
<li> Distance measures</li>
<li> Different clustering methods (Distance, Density, Hierarchical)</li>
<li> Iterative distance-based clustering;</li>
<li> Dealing with continuous, categorical values in K-Means</li>
<li> Constructing a hierarchical cluster</li>
<li> K-Medoids, k-Mode and density-based clustering</li>
<li> Measures of quality of clustering</li>
</ul>
<li> Distance measures</li>
<li> Different clustering methods (Distance, Density, Hierarchical)</li>
<li> Iterative distance-based clustering;</li>
<li> Dealing with continuous, categorical values in K-Means</li>
<li> Constructing a hierarchical cluster</li>
<li> K-Medoids, k-Mode and density-based clustering</li>
<li> Measures of quality of clustering</li>
</ul>
Classification
<ul>
<li> Model Assumptions, Probability estimation</li>
<li> Required data processing</li>
<li> M-estimates, Feature selection: Mutual information</li>
<li> Classifier</li>
</ul>
<li> Model Assumptions, Probability estimation</li>
<li> Required data processing</li>
<li> M-estimates, Feature selection: Mutual information</li>
<li> Classifier</li>
</ul>
Support Vector Machines
<ul>
<li> Linear learning machines and Kernel space,</li>
<li> Making Kernels and working in feature space</li>
<li> SVM for classification and regression problems</li>
</ul>
<li> Linear learning machines and Kernel space,</li>
<li> Making Kernels and working in feature space</li>
<li> SVM for classification and regression problems</li>
</ul>
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Course details
Duration
Full time, Part-time, Evening and Weekends, Virtual online
Video
London, Flexible online
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