Machine Learning Master Program

Our Machine learning Master's Program training course includes training on the latest advancements and technical approaches in Machine Learning such as Graphical Models and Reinforcement Learning.

Machine Learning Master’s Program Training Course aims to insight the candidates on the Data Preprocessing, Clustering: K-Means, Hierarchical Clustering, Reinforcement Learning: Upper Confidence Bound, Thompson Sampling, Dimensionality Reduction: PCA, LDA, Kernel PCA, Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost, Reinforcement Learning: Upper Confidence Bound, Thompson Sampling, Deep Learning: Artificial Neural Networks, Convolutional Neural Networks, etc.

Machine Learning is basically the process to collect real-world data, extract useful information from it, and then take actions to perform certain tasks without manual programming.

It helps systems improve over time on their own by exploring various types of real-world data. It also allows organizations to improve their business strategies by knowing the insights that are extracted from the given business data.

All these helps the candidates in building their career as a ML Engineer professional, who can create a strong added value to your business for sure.Machine Learning mainly focuses on the enhancement and development of the computer programs, which has the property to get changed when it comes in the interaction to the new data.

However, this is a kind of artificial intelligence, the Introduction to Machine Learning course enlightens the candidates with the algorithms that proves to be helpful for the IP professionals in analyzing the data set with ease.

In the training modules algorithms such as: regression, clustering, classification,  and recommendation have been introduced, all these helps the candidates in supervising the advanced data programing techniques.

What Will I Learn?

• Understand how to make an accurate predictions
• Learn how to deal with the advanced techniques like Dimensionality Reduction
• Develop an understanding on the issues of specific topics like Reinforcement Learning, NLP and Deep Learning & how to handle it
• Develop understanding of many of the Machine Learning models
• Develop understanding on all the essentials such as: Data Preprocessing, Regression, Classification, Clustering, Association Rule Learning, Reinforcement Learning
• Determine the various applications of machine learning algorithms
• Learn the how to implement the unsupervised learning algorithms, which includes deep learning, clustering, and recommendation systems