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FolksIT Machine Learning online training course will help you master the skills required to become an expert in this domain. This Machine Learning course offers an in-depth knowledge of all the Machine Learning concepts. We provide a unique blend of theoretical and practical approach. We also create an environment for comfortable learning pace as we have global industry experts as trainers.
This Machine Learning certification online course is well-suited for consultants who are at the intermediate level. This course mainly requires an understanding of basic statistics and mathematics. In each session every module is delivered based on real-time experiences. We ensure to enable the consultants to get well-equipped with all the possible solutions.
- Flexible schedule you can opt for training sessions according your convenience
- We provide course completion certification and Job Assistance
- 24/7 support will be given if you have any queries regarding the training
- Real-time project based assessments for better understanding.
Who should take this course?
You can learn in-person from renowned industry leading experts
It covers a detailed overview of carious Algorithms and Techniques such as regression, classification, time series modelling, supervised learning and natural language processing etc.
You can also use Python programming language to write code for implementing various algorithms in this certification training.
You can gain a stronger understanding of the major Machine Learning projects with helpful examples.
- This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information.
- You can also learn how R can play an important role in solving complex analytical problems.
- This module tells you what is R and how it is used by giants like Google, Facebook, etc.
- Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.
- Business Analytics, Data, Information
- Understanding Business Analytics and R
- Compare R with other software in analytics
- Install R
- Perform basic operations in R using command line
- Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.
- Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.
- This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.
- What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression.
- This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.
- Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
- Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data.
- Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
- In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition.
- Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models.
- To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.
- Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice
- We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.
- In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets
- Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.
- When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.
- Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets.
- Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.
Machine Learning Online Training FAQ’s:
In current scenario, Machine Learning is a high-demanding course. Before the certification training, it’s essential for beginners to get familiar with the basics of Machine Learning first.
In the field of Machine Learning there are many job roles like Data Scientists, Machine Learning Engineers, NLP Scientists, Computer Vision Engineers and data architects. FolksIT Machine Learning course will help you gain all the necessary skills to become eligible for such roles.
We provide 24/7 support through chat, e-mail and telephone. We have a dedicated team which provides assistance through our forum.
FolksIT certification course is designed by industry best experts who know what skills are most valued by the employers. All the topics will be covered and a deep learning of the basics will be thoroughly covered which will allow you to start a great career in this field.