About Machine Learning

Machine learning, is a sub-set of artificial intelligence. In layman’s language, machine learning is the capability of machines to learn from new data and make appropriate decisions. The machine unlearns and relearns from new data sets. Machine learning uses algorithms and statistical models to execute specific tasks. The tasks are performed not on instructions but on patterns and inferences. It is an application of artificial intelligence which gives the ability to machines to learn automatically and show improvement with experience.

Machine learning is the most popular career choice. Many industries are investing in machine learning to make smart decisions for their business. You don’t have to know advanced statistics or mathematics to understand the machine learning concepts. You should understand how an algorithm works and how a few lines of code are written. Our step-by-step tutorial will help you to step into the amazing world of machine learning. This machine learning for beginner’s course will impart the fundamental knowledge to understand all about machine learning.

 Why should you learn machine learning?

Machine learning is booming and there is a possibility of huge investment in the machine learning industry by 2020. Machine learning has application in many domains. Industries are gaining insights about their domain by leveraging machine learning and gain advantage over their competitors.

Health care domain: Machine learning has become popular in health care domain after the advent of wearable devises.  Patients are using sensors and wearable devises. A lot of data is generated which needs analysis. Machine learning helps the medical professionals to get data analysis which helps in the patient care and effective treatment.

Financial services industry: The Banking and financial sector utilized machine learning for fraud detection and for customer data analysis. The data is useful to identify investment options for the BFSI sector. The sector identifies customers with high-risk profiles with machine learning techniques.

The retail industry: Retailors can use machine learning techniques to personalize shopping experience for their customers. Retailors can get customer data and analyze their shopping behavior, customer preferences, changing consumer behaviors and patterns. Retailors can customize their products based on the consumer behavior, provide them with offers, discount coupons, send newsletters etc.

Automotive industry:  Automotive industry leverages on data analytics and machine learning techniques for marketing activities, improve operations, and improve customer experience before and after the purchase. Machine learning also helps auto motive industry to predict the spare parts and machine parts failure and keep the dealers informed. Operational costs and inventory costs can be reduced. Customer satisfaction can be enhanced.

Federal agencies: Government or Federal agencies receive data from multiple sources. The data from multiple sources should be mined to get insights to take action. Sensex data, population data, etc. helps in taking decisions regarding fraud detection, reduce identity theft etc.

Transport sector: Machine learning helps transportation companies and the industry as a whole to predict new routes, reduce costs, predict the potential problems in a given route and make transport industry more profitable.

Oil and Gas sector: The oil and gas corporations are relying heavily on the machine learning and data analysis. The data is analyzed to increase efficiency, reduce costs, improve safety measures etc.

Traffic predictions: Machine learning helps to predict the traffic congestion areas and send signals to the centralized server.  The saved data helps to predict the traffic congestion areas and send via GPS to the customers.

Customer support: The online customer support is possible to extend to all the customers navigating the websites. It is practically not possible to have an executive to answer all the customer queries 24/7. A chatbot facility helps to answer the queries of the customer. The machine learning algorithms pull data from the website and answer the queries of the customer.

Machine Learning Fundamentals Course Overview

This machine learning tutorial for beginners is a step by step tutorial that will take you to the amazing world of machine learning. There are many text books and videos available to impart knowledge which only leaves an impression that machine learning is hard to learn. We have simplified your learning by creating a step-by step tutorial which will help you to assimilate the subject. We have trainers with industry expertise who will help you to understand the subject easily. You are going to start learning machine learning from the best machine learning course for beginners that will impart knowledge of fundamentals of machine learning, how to install, concepts of algorithms and how to use them, supervised and unsupervised learning, some statistical techniques used in machine learning.

We provide access to our LMS 24/7 to all the students who enrolled for the basic machine learning course. You can clarify your doubts with our expert trainers at any point of time. We also share in interactive machine learning videos for beginners to better understanding of the subject. We will also give you case-studies and live projects for you to gain hands-on experience on the machine learning concepts. In case you have missed a class, you can follow the training videos to fill the gap. This course is the best way to start learning machine learning because the entire course is designed by industry experts. The course is current as per the demands of the corporate world.

This is the best course to start machine learning for all those who want to switch their career from traditional programming to begin machine learning career.

Prerequisites

To learn machine learning fundamentals course, a basic programming language knowledge is necessary. A knowledge in C, or C++ or Java programming is desired. You should have knowledge of data analysis and a flair to dig deep into it.

Machine Learning Fundamentals Course Content               

Introduction to machine learning

• Overview of machine learning
• Introduction to machine learning
• Machine learning fundamentals
• Learn the various approaches to machine learning
• Understand the various machine learning techniques
• Understand the application of machine learning techniques

Installations for machine learning

• Learn to install Anaconda
• Learn to install Spyder
• Learn to install Keras
• Learn to install TensorFlow

Introduction to Artificial intelligence

• Overview of artificial intelligence
• Emergence and history of artificial intelligence
• Understand the relation between artificial intelligence and machine learning
• Understand the application of artificial intelligence in different industries and domains

Linear Regressions

• Introduction to the fundamental concepts of Linear regression
• Learn the Linear regression theory
• Learn optimization in linear regression
• Learn gradient descent in linear regression

Logistic regression

• Introduction to logistic regression
• Learn logistic regression with Sigmoid function
• Learn logistic regression with credit scoring
• Understand cross-validation with logistic regression
• Understand the explore loss function using logistic regression
• Understand the regularization function using logistic function

KNN classifier

• Introduction to K nearest neighbor classifier
• Introduction to K-nearest neighbor using Euclidean-distance
• Introduction to KNN using Hamming Distance
• Introduction to KNN using Manhattan Distance
• Introduction to KNN using Minkowski Distance
• Learn KNN using Lazy learning
• Learn KNN using instance-based learning
• Learn KNN using Non-parametric
• Learn KNN for working on regression
• Learn KNN for working on classification

Naive Bayes classifier

• Introduction to Naïve Bayes classifier
• Understand the relationship between Naive Bayes classifier and logistic regression
• Understand the Gaussian Naive Bayes Classifier
• Learn to work with Naive Bayes Classifier for continuous inputs
• Learn to work with Naive Bayes Classifier for discrete-valued inputs
• Understand the fundamentals of text clustering

Supervised learning

• Understand the overview of classification
• Understand the basics of supervised learning
• Understand the classification of algorithms
• Introduction to support vector machine
• Understand the applications of support vector machines
• Understand the SVM Kernel functions
• Learn the decision tree classifier
• Understand about random forest classifier

Unsupervised learning

• Understand the concept of unsupervised learning
• Learn the concepts of clustering
• Define and describe hierarchical clustering
• Learn K-Means clustering

Decision trees

• Introduction to decision trees
• Learn the concept of Gini index approach
• Learn the concept of entropy
• Understand the concept of classification error

Time series modelling

• Overview and Introduction to time series modelling
• Understand the concept of white noise and its application in machine learning
• Learn about stationarity
• Understand the time series models and steps in time series forecasting

Ensemble learning

• Overview of ensemble learning
• Understand the working of adaboost
• Understand the concept of Gradient boosting
• Learn about Xboost
• Learn to tune classifier model with XGBoost

Jobs and placements

There’s always two-phase to everything so the learner always has to be upskilled with the latest tools because the market is highly competitive dynamic and ever-changing. Thus, machine learning tools are also very dynamic and keep updating at a regular interval. Every good company requires a good machine learning professional and hence this will never go down and machine learning is already a boom in the market globally. There will be a recurring demand for the machine learning professionals. Software professionals specializing in machine learning draw higher salaries compared to other IT professionals.

According to indeed.com, machine learning engineers have a lot of demand and they have a growth with 345%. Machine learning is the most popular career choice for many IT professionals. The average salary of a machine learning professional is $145,000 per year. Once you complete the course and get the certification, you may get placed in organizations like IBM, Accenture, Cognizant, Capgemini, Deloitte etc.

Certification

By the end of our training, we will give Machine learning- fundamental course certificate. You will also be given to work on real-time projects. Once you successfully completely complete the course training and the project, you will provide a test. Upon the test clearance, you will be awarded a certificate of completion. In case you are not successful in the first attempt, we will extend complete support to retake the exam. You will be given a quiz which perfectly reflects the certification exam. If you can clear the quiz successfully, you are already halfway towards achieving the certificate. Our certificate has wide acceptance in fortune 500 companies.