Python Machine Learning Training in Saudi Arabia and India

Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need for explicit programming.Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc.
Image recognition is one of the most common uses of machine learning. There are many situations where you can classify the object as a digital image. For example, in the case of a black and white image, the intensity of each pixel is served as one of the measurements. In coloured images, each pixel provides 3 measurements of intensities in three different colours – red, green and blue (RGB).
At Visible Stars Training Institute, our Machine learning course will help you to master this science and understand Machine Learning algorithms, which include Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-Supervised Learning algorithms.

Our Machine Learning Course with Python helps you to understand and learn:

• The basic concepts of the Python Programming language
• About Python libraries (Scipy, Scikit-Learn, TensorFlow, Numpy, Pandas,)
• The data structure of Python
• Machine Learning Techniques
•  Basic Descriptive and Inferential Statistics before advancing to serious Machine learning development.
• Different stages of Data Exploration/Cleaning/Preparation in Python

The Machine Learning Course with Python by Visible Stars is a 48 hour, instructor-led live training course. It also includes 45 hours of the hands-on practical session, along with 10 live projects.

Our Machine Learning course with Python will help you get hands-on experience of the following:
 

• Learn to implement statistical operations in Excel.
• Get a taste of how to start work with data in Python.
• Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.
• Learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies.
• Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real-Life Case Study on K-means Clustering.
• Learn about Decision Trees for regression & classification problems through a real-life case study.
• Get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID.
• Learn the implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines.

 For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.

The algorithm of machine learning is trained using a training data set so that a model can be created. With the introduction of any new input data to the ML algorithm, a prediction is made based on the model.
The accuracy of the prediction is checked and if the accuracy is acceptable, the ML algorithm is deployed. For cases where accuracy is not acceptable, the Machine Learning algorithm is trained again with supplementary training data set.
There are various other factors and steps involved as well. This is just an example of the process.

•  It is used in multifold applications such as financial and banking sectors, healthcare, publishing, retail, social media, etc.
• Machine learning can handle multi-variety and multi-dimensional data in an uncertain or dynamic environment.
• Machine learning algorithms are used by Facebook and Google to push advertisements which are based on past search patterns of a user.
• In large and complex process environments, Machine Learning has made tools available which provide continuous improvement in quality.
• Machine learning has reduced the time cycle and has led to the efficient utilization of resources.

Various industries work with Machine Learning technology and have recognized its value. It has helped and continues to help organisations to work in a more effective manner, as well as gain an advantage over their competitors. 

Financial services:

Machine Learning technology is used in the financial industry due to two key reasons: to prevent fraud and to identify important insights in data. This helps them in deciding on investment opportunities, that is, helps the investors with the process of trading, as to identify clients with high-risk profiles. 

Government:

Machine learning is finding varied uses in running government initiatives. It helps in detecting fraud and minimizes identity theft. It’s also used to filter and identify citizen data. 

Health Care: 

Machine Learning in the health care sector has introduced wearable devices and sensors that use data to assess a patient’s health in real-time, which might lead to improved treatment or diagnosis. 

Oil and Gas:

There are numerous use cases for the oil and gas industry, and it continues to expand. A few of the use cases are: finding new energy sources, predicting refinery sensor failure, analyzing minerals in the ground, etc. 

Retail:

Websites use Machine Learning to recommend items that you might like to buy based on your purchase history.

Machine learning has transformed various sectors of industries including retail, healthcare, finance, etc. and continues to do so in other fields as well. Based on the current trends in technology, the following are a few predictions that have been made related to the future of Machine Learning.
Personalization algorithms of Machine Learning offer recommendations to users and attract them to complete certain actions. In future, the personalization algorithms will become more fine-tuned, which will result in more beneficial and successful experiences.
With the increase in demand and usage for Machine Learning, the usage of Robots will increase as well.
Improvements in unsupervised machine learning algorithms are likely to be observed in the coming years. These advancements will help you develop better algorithms, which will result in faster and more accurate machine learning predictions.
Quantum machine learning algorithms hold the potential to transform the field of machine learning. If quantum computers integrate to Machine Learning, it will lead to faster processing of data. This will accelerate the ability to draw insights and synthesize information.