Machine Learning Certification Training

Machine Learning Certification Training

OL Tech Edu's Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR.

Data Scientist has been named the best job in America for 2018 with median base salary of $242,000.

Roles like Chief Data Scientist have emerged to ensure that analytical insights drive business strategies.

Businesses Will Need One Million Data Scientists by 2020 – Kdnuggets.

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Course Curriculum

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Machine Learning Certification Training using Python

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OL Tech Edu's Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR.

  • WEEK 5-6
  • 10 Modules
  • 6 Hours
Machine Learning Certification Training Using Python
Goal: Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools.

Objectives:

At the end of this Module, you should be able to:

  • Define Data Science.
  • Discuss the Era of Data Science.
  • Describe the Role of a Data Scientist.
  • Illustrate the Life Cycle of Data Science.
  • List the Tools used in Data Science.
  • State what role Big Data and Hadoop, Python, R and Machine Learning play in Data Science.
Topics:
  • What is Data Science?
  • What does Data Science Involve?
  • Era of Data Science.
  • Business Intelligence vs Data Science.
  • Life Cycle of Data Science.
  • Tools of Data Science.
  • Introduction to Python.
Goal: Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.

Objectives:

At the end of this Module, you should be able to:

  • Discuss Data Acquisition Techniques.
  • List the Different Types of Data.
  • Evaluate Input Data.
  • Explain the Data Wrangling Techniques.
  • Discuss Data Exploration.

Topics:
  • Data Analysis Pipeline.
  • What is Data Extraction?
  • Types of Data.
  • Raw and Processed Data.
  • Data Wrangling.
  • Exploratory Data Analysis.
  • Visualization of Data.

Hands-On/Demo:
  • Loading different types of Dataset in Python.
  • Arranging the Data.
  • Plotting the Graphs.
Goal: In this module, you will learn the concept of Machine Learning and it’s types.

Objective:

At the end of this module, you should be able to:

  • Essential Python Revision.
  • Necessary Machine Learning Python Libraries.
  • Define Machine Learning.
  • Discuss Machine Learning Use Cases.
  • List the categories of Machine Learning.
  • Illustrate Supervised Learning Algorithms.
  • Identify and Recognize machine learning algorithms around us.
  • Understand the various elements of machine learning algorithm like Parameters, Hyper Parameters, Loss Function and Optimization.
Topics:
  • Python Revision (Numpy, Pandas, Scikit Learn, Matplotlib).
  • What is Machine Learning?
  • Machine Learning Use-Cases.
  • Machine Learning Process Flow.
  • Machine Learning Categories.
  • Linear Regression.
  • Gradient Descent.
Hands On:
  • Linear Regression – Using Boston Dataset.
Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Objective:

At the end of this module, you should be able to:

  • Understand What is Supervised Learning?
  • Illustrate Logistic Regression.
  • Define Classification.
  • Explain different Types of Classifiers such as Decision Tree and Random Forest.
Topics:
  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction.
  • Creating a Perfect Decision Tree.
  • Confusion Matrix.
  • What is Random Forest?
Hands On:
  • Implementation of Logistic Regression, Decision Tree, Random forest.
Goal: In this module you will learn about impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.  

Objective: 

At the end of this module, you should be able to:

  • Define the Importance of Dimensions.
  • Explore PCA and its Implementation.
  • Discuss LDA and its Implementation.
Topics:
  • Introduction to Dimensionality.
  • Why Dimensionality Reduction?
  • PCA.
  • Factor Analysis.
  • Scaling Dimensional Model.
  • LDA.

Hands On:
  • PCA.
  • Scaling.
Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

Objective:

At the end of this module, you should be able to:

  • Understand What is Naïve Bayes Classifier.
  • How Naïve Bayes Classifier works?
  • Understand Support Vector Machine.
  • Illustrate How Support Vector Machine works?
  • Hyperparameter Optimization.

Topics:
  • What is Naïve Bayes?
  • How Naïve Bayes Works?
  • Implementing Naïve Bayes Classifier.
  • What is Support Vector Machine?
  • Illustrate how Support Vector Machine works?
  • Hyperparameter Optimization.
  • Grid Search vs Random Search.
  • Implementation of Support Vector Machine for Classification.

Hands On:
  • Implementation of Naïve Bayes.
  • SVM.
Goal: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.

Objective:

At the end of this module, you should be able to:

  • Define Unsupervised Learning.
  • Discuss the following Cluster Analysis.
  • K - Means Clustering. 
  • C - Means Clustering. 
  • Hierarchical Clustering.

Topics:
  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How K-means Algorithm Works?
  • How to do Optimal Clustering?
  • What is C-means Clustering?
  • What is Hierarchical Clustering?
  • How Hierarchical Clustering Works?
Hands On:
  • Implementing K-means Clustering.
  • Implementing Hierarchical Clustering.
Goal: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.

Objective:

At the end of this module, you should be able to:

  • Define Association Rules.
  • Learn the backend of recommendation engines and develop your own using python.
Topics:
  • What are Association Rules?
  • Association Rule Parameters.
  • Calculating Association Rule Parameters.
  • Recommendation Engines.
  • How Recommendation Engines work?
  • Collaborative Filtering.
  • Content Based Filtering.
Hands On:
  • Apriori Algorithm.
  • Market Basket Analysis.
Goal: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent environment interaction.

Objective:

At the end of this module, you should be able to:

  • Explain the Concept of Reinforcement Learning.
  • Generalize a Problem using Reinforcement Learning.
  • Explain Markov’s Decision Process.
  • Demonstrate Q Learning.

Topics:
  • What is Reinforcement Learning?
  • Why Reinforcement Learning?
  • Elements of Reinforcement Learning.
  • Exploration vs Exploitation Dilemma.
  • Epsilon Greedy Algorithm.
  • Markov Decision Process (MDP).
  • Q Values and V Values.
  • Q – Learning.
  • α Values.

Hands On:
  • Calculating Reward.
  • Discounted Reward.
  • Calculating Optimal Quantities.
  • Implementing Q Learning.
  • Setting up an Optimal Action.
Goal: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modelling such that you analyse a real time dependent data for forecasting.

Objective:

At the end of this module, you should be able to:

  • Explain Time Series Analysis (TSA).
  • Discuss the Need of TSA.
  • Describe ARIMA Modelling.
  • Forecast the Time Series Model.
Topics:
  • What is Time Series Analysis?
  • Importance of TSA.
  • Components of TSA.
  • White Noise.
  • AR Model.
  • MA Model.
  • ARMA Model.
  • ARIMA Model.
  • Stationarity.
  • ACF & PACF.

Hands on:
  • Checking Stationarity.
  • Converting a Non-stationary Data to Stationary.
  • Implementing Dickey Fuller Test.
  • Plot ACF and PACF.
  • Generating the ARIMA plot.
  • TSA Forecasting.
Goal: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms to stronger ones.

Objective:

At the end of this module, you should be able to:

  • Discuss Model Selection.
  • Define Boosting.
  • Express the need of Boosting.
  • Explain the working of Boosting Algorithm.

Topics:
  • What is Model Selection?
  • Need of Model Selection.
  • Cross – Validation.
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms.
  • Adaptive Boosting.
Hands on:
  • Cross Validation.
  • AdaBoost.
Goal: In this module, you will learn how to approach and implement a Project end to end, and a Subject Matter Expert will share his experience and insights from the industry to help you kickstart your career in this domain. Finally, we will be having a Q&A and doubt clearing session.

Objectives:

At the end of this module, you should be able to:

  • How to Approach a Project?
  • Hands-On Project Implementation.
  • What Industry Expects?
  • Industry Insights for the Machine Learning Domain.
  • QA and Doubt Clearing Session.

Program Syllabus

Curriculum

You can also view the program syllabus by downloading this program Curriculum.

Curriculum

Projects

How will I execute the practicals of Kubernetes certification course?

All the Case Studies and Demos will run on Ubuntu 17.10 VMs. The pre-built VMs and their Installation Guide will be present on LMS once you enroll for the course. Kubernetes is an open-source tool therefore anybody can use it for their lab exercises.

What are the system requirements for this Kubernetes Certification Training?

Hardware Requirement(s): Memory – Minimum 16 GB RAM, processor – Intel Core i5 CPU @2.00 GHz or above, Storage – 250 GB HDD/SDD or above, Software Requirement(s): Operating System – Windows 7 or above, Ubuntu 14 or above, Latest Version of Oracle Virtual Box Installed, Windows PowerShell 4.0 or above (Install Azure Module), Microsoft Azure SDK for .NET v2.9 (prefer latest)

How will I execute the practicals of Kubernetes certification course?

The system requirements include Minimum 4 GB RAM, i3 processor or above, 20 GB HD.

Course Description

About The Course
About the Machine Learning Course using Python. OL Tech Edu’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
Objectives Of Our Online PySpark Training Course
Why Learn Machine Learning using Python?
  • Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed.
  • It Employs Techniques and Theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science.
  • This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms.
  • This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
Go For Online Spark Training
What are the objectives of our Machine Learning Certification Training using Python?After completing this Machine Learning Certification Training using Python, you should be able to:
  • Gain insight into the 'Roles' played by a Machine Learning Engineer.
  • Automate data analysis using python.
  • Describe Machine Learning.
  • Work with Real-time Data.
  • Validate Machine Learning Algorithms.
  • Explain Time Series and it’s Related Concepts.
  • Gain expertise to handle business in future, living the present.
Learning With Our PySpark Certification Training
Who should go for this Machine Learning Certification Training using Python?
  • Python Machine Learning Certification Course is a good fit for the below professionals.
  • Developers aspiring to be a ‘Machine Learning Engineer'.
  • Analytics Managers who are leading a team of analysts.
  • Business Analysts who want to understand Machine Learning (ML) Techniques.
  • Information Architects who want to gain expertise in Predictive Analytics.
  • 'Python' professionals who want to design automatic predictive models.
Who Should Go For Our PySpark Training Course
What are the pre-requisites for this course?
  • The pre-requisites for the Machine Learning Certification Training using Python includes development experience with Python.
    Fundamentals of Data Analysis practised over any of the data analysis tools like SAS/R will be a plus. However, Python would be more advantageous. You will be provided with complimentary “Python Statistics for Data Science Course” as a self-paced course once you enrol for the course.

Course Certification

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Features

Explore step by step paths to get started on your journey to Jobs of Today and Tomorrow.

Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes.
Weekend Class : 10 sessions of 3 hours each.

Real Life Case Studies

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various real life solutions / services.

Assignments

Assignments

Each class will be followed by practical assignments.

24 x 7 Expert Support

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Certification

Certification

Towards the end of the course, OL Tech Edu certifies you for the course you had enrolled for based on the project you submit.

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