Enrol now to become a Certified Machine Learning expert with EDTIA Machine Learning Masters Program and upgrade your skills.
This Machine Learning Program makes you trained in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. Our Machine learning course contains training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning, such as Deep Learning, Graphical Models and Reinforcement Learning.
A master's in Machine Learning (ML) coursework explores the fundamental mathematics of artificial intelligence and machine learning while enabling students to develop related tools and apply AI and ML to various real-world problems.
Our Machine Learning Course Learning track has been curated after thorough research and recommendations from industry experts. It will help you differentiate yourself with multi-platform fluency and have real-world experience with the essential tools and platforms.
There are no prerequisites for enrolment in the Machine learning Masters Program.
Experienced professional working in the IT industry. An aspirant is planning to enter the data-driven world of Machine Learning.
A machine learning engineer is an engineer that utilizes programming languages such as Python, Java, Scala, etc., to run experiments with the correct machine learning libraries.
As a machine learning engineer working in this branch of artificial intelligence, you'll be accountable for developing programmes and algorithms that allow machines to take steps without being directed.
Machine-learning jobs have jumped by almost 75 per cent over the past four years and are poised to keep growing. Pursuing a machine learning position is a solid choice for a high-paying profession that will be in demand for decades.
understand the fundamental concepts of Python.
learn various types of sequence structures, their use, and perform sequence operations.
learn about different types of Functions and various Object-Oriented concepts such as Abstraction, Inheritance, Polymorphism, Overloading, Constructor, and so on.
Discover how to make generic python scripts, address errors/exceptions in code, and extract/filter content using regex.
basics of Data Analysis utilizing two essential libraries: NumPy and Pandas, the concept of file handling using the NumPy library.
gain in-depth knowledge about exploring datasets and data manipulation utilizing Pandas.
you will learn Data Visualization using Matplotlib.
you will learn GUI programming using the ipywidgets package.
you will get to learn to design Python Applications.
you will learn to design Python Applications.
Understand Machine learning with Python training and see how Data Science helps analyze large and unstructured data with various tools.
structured form, analyzing the data, and representing the data in a graphical format.
you will learn the concept of Machine Learning with Python and its types.
know Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress sizes. Also, you will be developing an LDA model.
learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
learns about Unsupervised Learning and the various types of clustering used to analyze and analyze the data.
understand Association rules and their extension towards recommendation engines with the Apriori algorithm.
learn about developing an intelligent learning algorithm such that the Learning becomes more and more accurate as time passes.
know about Time Series Analysis to predict dependent variables based on time. You will be taught different models for time series modelling such that you analyze accurate time-dependent data for forecasting.
learn about selecting one model over another. You will understand how to transform weaker algorithms into stronger ones.
know how to approach and execute a Project end to end, we will be having a Q&A and doubt clearing session.
learn about text mining and how to extract and read data from common file types, including NLTK corpora.
comprehend some ways of text extraction and Cleaning utilizing NLTK.
learn how to analyze and analyze a sentence structure using a group of words to create phrases and sentences using NLP and English grammar rules.
explore text classification, vectorization techniques and processing utilizing scikit-learn
comprehend to create a Machine Learning classifier for text classification
learn Sentiment Classification on Movie Rating Dataset
understand the concepts of Deep Learning and learn how it differs from machine learning. This Deep Learning Certification module will also brief you on implementing the single-layer perceptron concept.
Learn TensorFlow 2. x. You will install and validate TensorFlow 2. x by building a Simple Neural Network to predict handwritten digits and using Multi-Layer Perceptron to improvise the model's accuracy.
comprehend how and why CNN came into existence after MLP and learn about Convolutional Neural Network (CNN) by studying the theory behind how CNN is used to predict 'X' or 'O'. You will also use CNN VGG-16 utilizing TensorFlow 2 and indicate whether the given image is of a 'cat' or a 'dog' and save and load a model's weight.
understand the concept and working of RCNN and figure out why it was developed in the first place.
Understand what a Boltzmann Machine is and its implementation, learn what an Autoencoder is, its various types, and how it works.