Advanced Machine Learning Course

About the course

Machine learning is the most trending technology. Machine learning is a study which helps machines to learn without being manually programmed. Machine learning has its applicability in many fields and many organizations are investing heavily in machine learning. We acknowledge the industry demand for machine learning and offer an advanced course to all the machine learning and data science professionals who want to move ahead in their career in this dynamic domain. To learn our course, you must have some experience with machine learning, data science or statistical modeling is expected.

Training in advanced machine learning will make use of advanced mathematics, including advanced statistics for machine learning, linear algebra, calculus and information theory. If you have prior knowledge of these areas, particularly the first two, you will obtain additional insights into the methods used. If you do not have this prior knowledge, you will still be able to achieve the learning outcomes of the course.

Advanced Machine Learning Course overview

Advanced course in machine learning is designed for all those who want to master their skills and take their career to next level. Course will appeal to all the aspirants who have basic knowledge of machine learning. With the help of training, you will learn to apply advanced machine learning concepts, supervised modelling algorithms, they are applied, and understand how the regression problems are solved. You will learn to explore the advanced data science projects.

Machine Learning course curriculum is designed by industry experts. To enhance your learning experience, we will provide interactive videos which will cover all the modules of the course. Even after you complete the course, we offer lifetime access to an upgraded latest version of videos, course materials, 24/7 Support free of cost. With the help of this training course, you can easily extract patterns and make meaningful insights from the data. By the end of the course, you will be comfortable in working with advanced machine learning techniques. After the course completion, you will have better career prospects will more salary.

The learning outcomes of this course:

• Learn to formalize tasks in computer vision via neural networks
• Learn to design neural networks to solve tasks involving huge data
• Learn to build datasets, with advanced deep learning libraries
• Learn to tune and train neural networks with advanced deep learning libraries
• Learn how the neural network mechanisms aid in machine learning
• Learn to analyze the neural networks performance.

Prerequisites

To learn the most advanced concepts of machine learning, you should have the basic knowledge of Python, basic knowledge of machine learning, understand the concepts of linear regression, mean square error and arrive at an analytical solution. It is desired that you have knowledge of logistic regression in model, cross-entropy loss, class probability estimation. You should also have basic knowledge of data science and statistical modelling.

We will provide learning material of basics of machine learning as course syllabus, videos or as some learning links.

Advanced Machine Learning Course content

Introduction to mathematical concepts

• Overview of Python 3.5
• Understand the basic concepts of Linear Algebra
• Understand Statistics and Probability
• Learn Numpy, Scipy, and Scientific computation with Python

Introduction to probability and statistics

• Understand: random variables, expectations and variance
• Use chain rule, marginalization rule and Bayes' rule
• Make use of conditional independence, and understand "explaining away"
• Compute maximum likelihood solutions for Bernoulli and Gaussian distributions

Introduction to advanced machine learning

• Gain understanding of neural networks
• Understand the application of neural network in computer vision and natural language understanding
• Learn the concepts of linear models and stochastic optimization methods

Introduction to Algorithms and data handling

• Learn the concepts of Nearest Neighbor search and K-means clustering
• Learn to work with Decision trees and Naive Bayes.
• Understand and learn to work with Data Scraping, Handling, Cleaning
• Define and describe Random Forest Classifiers technique.

Introduction to features in machine learning

• Over view of the Features of advanced machine learning and understand the Importance
• Understand the Feature scaling
• Understand the Curse of Dimensionality
• Define and describe the SVD and Principal Component Analysis

Introduction to regression techniques

• Understand the Regression Techniques
• Learn the Numerical Optimization
• Introduction to Neural Networks

Introduction to deep learning

• Define and describe Neural Architectures and Training
• Understand the Deep learning methods
• Convolutions and the Google Net
• Describe and define Dimensions revisited: The Auto-encoder
• Define and describe Recurrent and Combined Architectures
• Define and describe Support Vector Machines
• Introduction to Unsupervised and Reinforcement Learning
• Define and describe Transfer Learning
• Define and describe Optimization
• Get deep insights if end-to-end learning of deep learning
• Describe and define recurrent networks
• Describe and define generative models
• Understand the concept of variational inference
• Understand the natural language processing

Reinforcement learning

• Define and describe Markov decision processes
• Understand the concept of Dynamic programming
• Understand model-free prediction and control
• Learn to work with value function approximation and policy gradients
• Learn to work with deep reinforcement learning
• Understand the concept of integrating learning and planning
• Understand the concepts of Exploration and Exploitation

Data Wrangling

• Introduction to APIs and Web Scraping
• Understand the key concepts of data Wrangling with Pandas
• Learn to import Data from Web
• Learn to implement SQL For Data Analytics
• Learn to implement SQL at Scale Using Spark
• Learn to handle Unstructured Data
• Learn to work with Big Data Analytics using Spark Data Frames | PySpark | Dask

Image Processing and Computer Vision

• Learn about advanced Computer Vision concepts and applied computer vision concepts
• Understand the concepts of Deep Learning in Computer Vision
• Learn to create Image Processing Pipelines
• Understand about image Compressing with Color Quantization
• Learn Image Clustering and Classification with deep CNNs, GANs
• Learn object detection using TensorFlow and SSDs
• Learn to apply Semantic Segmentation for Deep Learning
• Understand the applications and Trends of image processing and computer vision
• Work on exercises to gain Hands on experience and learn Best Practices

Deep Neural Networks

• Understand the back propagation and Mathematics Behind deep neural network
• CNNs, RNNs, LSTMs, GANs and the Maths Behind Them
• Understand the Stochastic Gradient Descent
• Understand the Deep Learning concepts with (Keras, Tensor Flow, Pytorch)
• Lean to build and deploy deep learning apps.
• Learn to use Kears, TensorFlow and Pytorch.
• Learn to build a CNN using TensorFlow
• Understand the concept of Deep Learning using Pytorch
• Define and describe Deep Learning Framework
• Research work on Transfer Learning
• Learn about Auto Machine Learning and Deep Learning Optimization
• Gain hands on experience and understand the best practices Deep Learning

Machine Learning at Scale

• Learn about Advanced Data Wrangling at Scale (Big Pandas, Advanced SQL)
• Understand Scalable Machine Learning with Dask
• Learn to work on Distributed Computing with Dask
• Understand how to compute Parallelly with Dask
• Learn to work with Machine Learning at Scale on Spark Machine Learning
• Understand to work on Supervised Learning with Spark Machine Learning
• Recommendation Engines at Scale with Spark
• Monitoring and Debugging Scalable Machine Learning Systems
• Building, Debugging and Tuning Spark Machine Learning Pipeline
• Best Practices for Machine Learning at Scale

Deploying Artificial Intelligence Systems: From Model to Production

• Understand Production Data Science with Git
• Learn to build Quality Swagger APIs 
• Learn to test postman APIs
• Learn to design and deploy Solution Architecture
• Understand the Technical Considerations of Productionizing Models
• Learn to build Robust Machine Learning systems
• Learn to deploy Python Models to Production
• Learn to deploy Large Spark Models to Production

Advanced Machine Learning Certification

Machine learning jobs are trending in the IT field. Knowledge of advanced machine learning techniques will help you to stay ahead of the competition. Certification in an advanced machine learning course will sharpen your machine learning skills and help you to apply the advanced concepts in the real world. A certification will endorse your skills before the employers. After every module, we will conduct an exam to test your theoretical knowledge. You will also be given case studies and real-life scenario projects to gain practical experience. At the end of the course completion, you will be a live wire in the job market, we will award a certificate of completion, once you complete the training. This certification has acceptance in many organizations, as we are the preferred partners for many organizations.

Jobs and placement

Businesses are benefitting by implementing deep learning, effective pattern recognition, fraud detection, translation services, recommendation engines, etc. Trendy techniques like transfer learning, voice user interface (VUI), open neural network architecture (ONNX) architecture, edge intelligence, etc. play a dominant role in redefining the way businesses are done. This training will help you to stay updated with the latest trendy advanced machine learning technologies. The trend of using advanced machine learning techniques and deep machine learning techniques will see a steep growth in the future too.

According to Indeed research, the salary increase from 2015 is 344%. The average salary is $145,000 for an advanced machine learning professional. According to the IEEE recent release in September 2019, the average salary for a machine learning professional is $185,000. Advanced machine learning professionals who work on smartphones and wearable devises draw an average salary of $215,771 per year.

You may be placed as a deep learning research engineer, machine learning engineer, Data scientist, Senior scientist, data scientist, senior technical product manager, etc. You may be placed in Qualcomm, Apple, Bosch, Expedia, Amazon, GE, etc.