Machine Learning Certification Training Course Online

Course Syllabus of Machine Learning Certification Training

Module 1 – Introduction to Machine Learning

Learning Objective: In this module, you will learn about the basic concept of Machine Learning. You will also learn about types of Machine Learning i.e. Supervised and Unsupervised Learning. You will gain an understanding of the applications of Machine Learning. Topics:

• Traditional Programming
• Introduction to Machine Learning
• Applications of Machine Learning
• Types of Machine Learning
• Machine Learning vs Artificial Intelligence

Hands-On:

• Foundational Machine Learning skills
• Python Machine Learning Skills
• Python libraries used in the implementation of Machine Learning ideas

Module 2 – Regression

Learning Objective: In this module, you will learn about the Regression Model of the Machine Learning Algorithm. This model represents the Supervised way of Machine Learning. You will also gain knowledge of linear and non-linear regression along with methods of model evaluation. Topics:

• Supervised Machine Learning
• Regression Machine Learning
• Simple Linear Regression
• Decision Tree
• Polynomial Regression
• Random Forest
• Applications of Regression Machine Learning

Hands-On: Implementation of Machine Learning Regression-Based Algorithm

Module 3 – Classification

Learning Objective: In this module, you will learn about the Classification Model of Machine Learning Algorithm. This also represents one of the Supervised Machine Learning strategies in Machine Learning. Classification models in Machine Learning are K-Nearest Neighbour, Support Vector Machines, Logistic Regression, Decision Trees. You will also learn about Model Evaluation. Topics:

• Classification
• Nearest Neighbor
• Support Vector Machine
• Boosted Trees
• Neural Network
• Linear Classifiers
• Applications of Classification Machine Learning

Hands-On: Implementation of Machine Learning Classification Based Algorithms

Module 4 – Unsupervised Learning

Learning Objective: In this module, you will learn about Unsupervised Machine Learning. Unsupervised Machine Learning is implemented without supervising machines in a specific way. Unsupervised Machine Learning involves clustering mechanisms such as K-Means Clustering, Density-Based Clustering, Hierarchical Clustering. Topics:

• Unsupervised Learning
• Clustering
• Hierarchical Clustering
• Independent Component Analysis
• K-means Clustering
• Applications of Unsupervised Learning

Hands-On: Implementing Unsupervised Machine Learning Algorithm

Module 5 – Natural Language Processing

Learning Objective: In this module, you will learn about programming computers to analyze natural language data. You will gain an understanding of communication with an intelligent system. You will also understand the role of NLP in Machine Learning. Topics:

• Natural Language Processing
• Steps in Natural Langage Processing
• Popular NLP techniques
• Applications of NLP
• Text Summarization
• Sentiment Analysis
• Aspect Mining
• Modeling

Hands-On: Build an NLP based Application using Python modules

Module 6 – Deep Learning

Learning Objective: In this module, you will learn about the techniques used to mimic neurons in the brain. You will learn about deep neural networks. You will learn how to train neural networks. Topics:

• Deep Learning
• Use of deep learning in Machine Learning
• Introduction to TensorFlow
• Convolutional Neural Network
• Recurrent Neural Network

Hands-On: Applying strategies of deep learning in Machine Learning Deep Learning in Python

Module 6 – Case Studies

Case Study 1: Representing an algorithm as a tree (Decision Tree) Learning Objective: You will learn how a tree has many purposes in real life. You will also understand the ways it helps in representing information in a concise and clear way through various examples such as Retail Store.

Case Study 2: Logistic Regression Learning Objective: You will learn how analysis helps in decision making with binary variables. It is used to explain the relationship between a dependent and ordinal binary variable. You will also learn about its use in biological sciences. Real-life examples such as banking, credit risk estimation, etc. will be taken into consideration.

Case Study 3: Linear Regression Learning Objective: Linear regression is used to explain the relationship between dependent and independent variables. You will learn how it helps in finding a linear relationship between given variables and how the change in one affects the others. The Scikit library of Python will be used to understand the algorithm.

Case Study 4: Principal Component Analysis Learning Objective: Principal Component Analysis is used to convert correlated variables into principal components. It helps in understanding the present structure of data and the parts where the most variance is found. Behavioral Data will be used as a case study to be discussed.

Case Study 5: Predictive data mining using Random Forest Learning Objective: The creation of many decision trees is the basis for the Random Forest algorithm. You will understand how many trees help in making a better decision as compared to a single tree. Every single tree samples from the data set, as a result, creating different trees.

Case Study 5: K-means Clustering Learning Objective: One of the simplest yet powerful unsupervised learning algorithms, k-means clustering, works with clusters. Points are randomly initiated in the data and assigned to the centroids. This algorithm helps in observation analysis without any prior observations.

Module 7- Projects

Project 1: Sales Prediction at BigMart Learning Objective: Using Regression Algorithm, build a model to predict a year’s sales based on different outlets. For different stores and products, different attributes will be taken into consideration. An understanding of the properties of stores and products helps in increasing the overall sales. Concepts of Regression Machine Learning with Python Libraries will be applied in the project.

Project 2: Sentiment Analysis Model Learning Objective: To understand the trends in the market, data mined from social media platforms can be put to great use. It helps in business, marketing, and branding. This project aims at making you understand the role of classifiers too, in-depth. This model will help the user understand what the world is engaging in more. It helps in the insightful analysis.

Project 3: Digit Classification Learning Objective: Digit classification model comes to play where it is not easy to work with image data. It usually happens with flat relational data. This project will help you take up some complex projects such as image classification in the future.

Project 4: Stock Price Predictor Learning Objective: For data scientists interested in finances, this project will pave the way along with helping you to understand how machine learning can help you decide on the stocks. Different types of data used in the project will be prices, indices, etc. By strategically thinking about trading strategies you will build a model making predictions on new data.

Other Project Ideas 1: Uber Customer Support

Other Project Ideas 2: Brain Barbie Students are encouraged to come up with their own innovative Project ideas too.

Trainer Profile of Machine Learning Certification Training

Trainers of Machine Learning Training Course at GangBoard are experts from industries. They have worked in the industry for more than 12 years. They understand what an organization and market needs. They will help you learn and understand the complex topics of Machine Learning in a very easy manner. Training classes are conducted in a student-friendly environment where students are encouraged to ask questions. They will help you prepare for the certification exam and earn yourself Machine Learning certification.

The selection process of trainers of GangBoard

GangBoard trainers have gone through a screening process. We make sure that the training course at GangBoard provides an excellent learning experience to the learners. Hence, trainers had gone through many rounds of the selection process. They have proved their worth of having competence and knowledge for the training courses.

Meet our Course Advisor

We at GangBoard understand that picking up the right training course is a daunting process for anyone. It requires complete and consistent knowledge about the subject, scope, and trend in the market. GangBoard has worked with Karthick for many years. He possesses skills, expertise and required a level of authority. He has been a go-to guide for our students, helping them select the right training classes, career, and programs for career growth. With 10+ number of years experience in Machine Learning, he/she understands the present career options and concepts popular in the industry.

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