Machine Learning Certification Course:

Key features

  • 32 hours of instructor-led training
  • Gain expertise with 25+ hands-on exercises
  • Practical application of 15+ Machine Learning algorithms
  • Master the concepts of Supervised & Unsupervised Learning

Exam & certification

What do I need to do to unlock my Digital Evolution Orbit certificate?

To obtain the Machine Learning certification from Digital Evolution Orbit, you will need to: 



  • Attend one complete batch

  • Complete and attain evaluation of any one of the given projects



 

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Course Details

Course description


  • Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning

  • The machine learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period



A form of artificial intelligence, machine learning is revolutionizing the world of computing as well as all people’s digital interactions. By making it possible to quickly, cheaply and automatically process and analyze huge volumes of complex data, machine learning is critical to countless new and future applications. Machine learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.



This Machine Learning online course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in machine learning. The demand for machine learning skills is growing quickly. The median salary of a Machine Learning Engineer is $134,293 (USD), according to payscale.com.


 

By the end of this Machine Learning course, you will be able to accomplish the following: 


  • Master the concepts of supervised and unsupervised learning

  • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.

  • Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.

  • Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.

  • Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.

  • Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems


 

There is an increasing demand for skilled machine meaning engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course for the following professionals in particular:


  • Developers aspiring to be a data scientist or machine learning engineer

  • Analytics managers who are leading a team of analysts 

  • Business analysts who want to understand data science techniques

  • Information architects who want to gain expertise in machine learning algorithms 

  • Analytics professionals who want to work in machine learning or artificial intelligence

  • Graduates looking to build a career in data science and machine learning

  • Experienced professionals who would like to harness machine learning in their fields to get more insights

Digital Evolution Orbit's Machine Learning Training course is very hands-on and code-driven. The theoretical motivation and Mathematical problem formulation must be provided only when introducing concepts.



This course consists of one primary capstone project and 25+ ancillary exercises based on 17 machine learning algorithms. 



Capstone Project Details:

Project Name:
Predicting house prices in California

Description: The project involves building a model that predicts median house values in Californian districts.You will be given metrics such as population, median income, median housing price and so on for each block group in California.Block groups are the smallest geographical unit for which the US Census Bureau publishes sample data (a lock group typically has a population of 600 to 3,000 people).The model you build should learn from this data and be able to predict the median housing price in any district.

 

Concept covered: Techniques of Machine Learning


Case Study 1: Predict whether consumers will buy houses or not, from the given dataset,


provided with their age and salary 


Project 1: What issues do you see in the plot produced by the code in reference to the above problem statement?


Project  2: What are the approximate prices of the houses with areas 1700 and 1900?


 


Concept covered: Data Preprocessing


Case Study 2: Demonstrate methods to handle missing data, categorical data, and data standardization using the information provided in the dataset


Project 3: Review the training dataset (Excel file). Note that weight is missing for the fifth and eighth rows.What are the values computed by the imputer for these two missing rows?


Project 4: In the tutorial code, find the call to the Imputer class. Replace strategy parameter from “mean” to “median” and execute it again. What is the new value assigned to the blank fields Weight and Height for the two rows?


Project 5: In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as before?




Case Study 3: Demonstrate how to reduce data dimensions from 3D to 2D using the information provided


Project 6: What does the hyperplane shadow represent in the PCA output chart on random data?


Project 7: What is the reconstruction error after PCA transformation? Give interpretation.




Concept Covered: Regression


Case Study 4: Demonstrate how to reduce data dimensions from 3D to 2D using the information provided


Project 8: Modify the degree of the polynomial from Polynomial Features (degree = 1) to 1, 2, 3, and interpret the resulting regression plot. Specify if it is under fitted, right-fitted, or overfitted?


Project 9: Predict the insurance claims for age 70 with polynomial regression n with degree 2 and linear regression.


Project 10: In the code snippet given below in the tutorial, why does the array X have 5 columns instead of 3 columns as before?




Case Study 5: Predict insurance premium per year based on a person’s age using Decision Trees using the information provided in the dataset


Project 11: Modify the code to predict insurance claim values for anyone above the age of 55 in the given dataset.




Case Study 6: Generate random quadratic data and demonstrate Decision Tree regression 


Project 12: Modify the max_depth from 2 to 3 or 4, and observe the output.


Project 13: Modify the max_depth to 20, and observe the output


Project 14: What is the class prediction for petal_length = 3 cm and petal_width = 1 cm for the max_depth = 2?


Project 15: Explain the Decision Tree regression graphs produced when max_depths are 2 and 3. How many leaf nodes exist in the two cases? What does average value represent these two situations? Use the information provided


Project 16: Modify the regularization parameter min_sample_leaf from 10 to 6, and check the output of Decision Tree regression. What is the result and why?




Case Study 7: Predict insurance per year based on a person’s age using Random Forests.


Project 17What is the output insurance value for individuals aged 60 and with n_estimators = 10?




Case Study 8:  Demonstrate various regression techniques over a random dataset using the information provided in the dataset


Project 18: The program depicts a learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Give your interpretation of these charts?


Project 19The program depicts the learning process when the values of the learning rate η are 0.02, 0.1, and 0.5. Try changing the values to 0.001, 0.25, and 0.9 and check the results? Provide interpretation.


 


Concept Covered: Classification


Case Study 9: Predict if the consumers will buy houses, given their age and salary.  Use the information provided in the dataset


Project 20: Typically, the value of nearest_neighbors for testing class in KNN is 5. Modify the code to change the value of nearest_neighbours to 2 and 20, and note the observations. 


 


Case Study 10Classify IRIS dataset using SVM, and demonstrate how Kernel SVMs can help classify non-linear data.


Project 21: Modify the kernel trick from RBF to linear to see the type of classifier that is produced for the XOR data in this program. Interpret the data. 


Project 22:  For the Iris dataset, add a new code at the end of this program to produce classification for RBF kernel trick with gamma = 1.0. Explain the output.


 


Case Study 11: Classify IRIS flower dataset using Decision Trees. Use the information provided


Project 23: Run decision tree on the IRIS dataset with max depths of 3 and 4, and show the tree output. 


Project 24:  Predict and print class probability for Iris flower instance with petal_len 1 cm and petal_width 0.5 cm.




Case Study 12: Classify the IRIS flower dataset using various classification algorithms. Use the information provided


Project 25: Add Logistic Regression classification to the program and compare classification output to previous algorithms?




Concept Covered: Unsupervised Learning with Clustering


Case Study 13Demonstrate Clustering algorithm and the Elbow method on a random dataset.


Project 26:  Modify the number of clusters k to 2, and note the observations.


Project 27:  Modify the n_samples from 150 to 15000 and the number of centres to 4 with n_clusters as 3. Check the output, and note your observations.


Project 28:  Modify the code to change the n_samples from 150 to 15000 and number of centres to 4, keeping n_clusters at 4. Check the output.


Project 29: Modify the number of clusters k to 6, and note the observations.

Course Preview

  • Artificial Intelligence
  • Machine Learning
  • Machine Learning algorithms
  • Applications of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning and Reinforcement Learning
  • Some Important Considerations in Machine Learning
  • Data Preparation
  • Feature engineering
  • Feature scaling
  • Datasets
  • Dimensionality reduction
  • Eigenvalues, Eigenvectors, and Eigendecomposition
  • Concepts of Linear Algebra
  • Introduction to Calculus
  • Probability and Statistics
  • Regression and Its Types
  • Linear Regression: Equations and Algorithms
  • Classification
  • Logistic regression
  • K-nearest neighbours
  • Support Vector Machines
  • Kernel SVM
  • Naive Bayes
  • Decision tree classifier
  • Random forest classifier
  • K-Means Clustering
  • K-means Clustering
  • Clustering Algorithms
  • Meaning and importance of deep learning
  • Artificial Neural networks
  • TensorFlow
  • 1.1 Getting Started with Python
  • 1.2 Print and Strings
  • 1.3 Math
  • 2.1 Variables, Loops and Statements
  • 2.2 While Loops
  • 2.3 For Loops
  • 2.4 If Statments
  • 2.5 If Else Statements
  • 2.6 If Elif Else Statements
  • 3.1 Functions And Variables
  • 3.2 Function Parameters
  • 3.3 Global And Local Variables
  • 4.1 Understanding Error Detection
  • 5.1 Working With Files And Classes
  • 5.2 Appending To A File
  • 5.3 Reading From A File
  • 5.4 Classes
  • 6.1 Intermediate Python
  • 6.2 Import Syntax
  • 6.3 Making Modules
  • 6.4 Error Handling - Try And Accept
  • 6.5 Lists vs Tuples And List Manipulation
  • 6.6 Dictionaries
  • 1.1 Course Introduction
  • 1.2 Overview of Final Project
  • 2.1 Introduction
  • 2.2 Django Installation And Configuration
  • 2.3 MVC Applied To Django Plus Git
  • 2.4 Basic Views, Templates And Urls
  • 2.5 Models, Databases, Migrations and the Django Admin
  • 2.6 Section Recap
  • 2.7 Quiz
  • 3.1 What You Will Learn In This Section
  • 3.2 Setting Up A Simple User Authentication System
  • 3.3 Login and Session Variables
  • 3.4 Social Registration
  • 3.5 Review
  • 3.6 Quiz
  • 4.1 What You Will Learn In This Section
  • 4.2 Template Language and Static Files
  • 4.3 Twitter Bootstrap Integration
  • 4.4 Static File Compression And Template Refactoring
  • 4.5 Review
  • 4.6 Quiz
  • 5.1 What You Will Learn In This Section
  • 5.2 Preparing The Storefront
  • 5.3 Adding A Shopping Cart
  • 5.4 Paypal Integration
  • 5.5 Stripe Integration With Ajax
  • 5.6 Review
  • 5.7 Quiz
  • 6.1 What You Will Learn In This Section
  • 6.2 File Upload
  • 6.3 Forms
  • 6.4 Advanced Emailing
  • 6.5 Review
  • 6.6 Quiz
  • 7.1 What You Will Learn In This Section
  • 7.2 Adding A Map Representation With Geolocation
  • 7.3 Advanced Map Usage
  • 7.4 Review
  • 7.5 Quiz
  • 8.1 What You Will Learn In This Section
  • 8.2 Building A Web Service With Tastypie
  • 8.3 Signals
  • 9.1 What You Will Learn In This Section
  • 9.2 Adding The Django Debug Toolbar
  • 9.3 Unit Testing
  • 9.4 Logging
  • 9.5 Review
  • 9.6 Quiz
  • 1.1 Introduction to the Course and the Game
  • 1.2 Introduction to PyGame and Initial Coding
  • 1.3 Time Clock and Game Over
  • 1.4 Graphics Setup
  • 1.5 Background and Adding Graphics to the Screen
  • 1.6 Working with Coordinates
  • 1.7 Creating Input Controls
  • 1.8 Boundaries, Crash Events and Menu Creation
  • 1.9 Part 2
  • 1.10 Part 3
  • 1.11 Part 4
  • 1.12 Creating Obstacles Using Polygons
  • 1.13 Completing Our Obstacles
  • 1.14 Game Logic Using Block Logic
  • 1.15 Game Logic Success Or Failure
  • 1.16 Hitting Obstacles Part 2
  • 1.17 Creating the Score Display
  • 1.18 Adding Colors and Difficulty Levels
  • 1.19 Adding Colors Part 2
  • 1.20 Adding Difficulty Levels
  • 0.1 Course Overview
  • 1.1 Introduction to Data Science
  • 1.2 Different Sectors Using Data Science
  • 1.3 Purpose and Components of Python
  • 1.4 Quiz
  • 1.5 Key Takeaways
  • 2.1 Data Analytics Process
  • 2.2 Knowledge Check
  • 2.3 Exploratory Data Analysis(EDA)
  • 2.4 EDA-Quantitative Technique
  • 2.5 EDA - Graphical Technique
  • 2.6 Data Analytics Conclusion or Predictions
  • 2.7 Data Analytics Communication
  • 2.8 Data Types for Plotting
  • 2.9 Data Types and Plotting
  • 2.10 Knowledge Check
  • 2.11 Quiz
  • 2.12 Key Takeaways
  • 3.1 Introduction to Statistics
  • 3.2 Statistical and Non-statistical Analysis
  • 3.3 Major Categories of Statistics
  • 3.4 Statistical Analysis Considerations
  • 3.5 Population and Sample
  • 3.6 Statistical Analysis Process
  • 3.7 Data Distribution
  • 3.8 Dispersion
  • 3.9 Knowledge Check
  • 3.10 Histogram
  • 3.11 Knowledge Check
  • 3.12 Testing
  • 3.13 Knowledge Check
  • 3.14 Correlation and Inferential Statistics
  • 3.15 Quiz
  • 3.16 Key Takeaways
  • 4.1 Anaconda
  • 4.2 Installation of Anaconda Python Distribution (contd.)
  • 4.3 Data Types with Python
  • 4.4 Basic Operators and Functions
  • 4.5 Quiz
  • 4.6 Key Takeaways
  • 5.1 Introduction to Numpy
  • 5.2 Activity-Sequence it Right
  • 5.3 Demo 01-Creating and Printing an ndarray
  • 5.4 Knowledge Check
  • 5.5 Class and Attributes of ndarray
  • 5.6 Basic Operations
  • 5.7 Activity-Slice It
  • 5.8 Copy and Views
  • 5.9 Mathematical Functions of Numpy
  • 5.10 Assignment 01
  • 5.11 Assignment 01 Demo
  • 5.12 Assignment 02
  • 5.13 Assignment 02 Demo
  • 5.14 Quiz
  • 5.15 Key Takeaways
  • 6.1 Introduction to SciPy
  • 6.2 SciPy Sub Package - Integration and Optimization
  • 6.3 Knowledge Check
  • 6.4 SciPy sub package
  • 6.5 Demo - Calculate Eigenvalues and Eigenvector
  • 6.6 Knowledge Check
  • 6.7 SciPy Sub Package - Statistics, Weave and IO
  • 6.8 Assignment 01
  • 6.9 Assignment 01 Demo
  • 6.10 Assignment 02
  • 6.11 Assignment 02 Demo
  • 6.12 Quiz
  • 6.13 Key Takeaways
  • 7.1 Introduction to Pandas
  • 7.2 Knowledge Check
  • 7.3 Understanding DataFrame
  • 7.4 View and Select Data Demo
  • 7.5 Missing Values
  • 7.6 Data Operations
  • 7.7 Knowledge Check
  • 7.8 File Read and Write Support
  • 7.9 Knowledge Check-Sequence it Right
  • 7.10 Pandas Sql Operation
  • 7.11 Assignment 01
  • 7.12 Assignment 01 Demo
  • 7.13 Assignment 02
  • 7.14 Assignment 02 Demo
  • 7.15 Quiz
  • 7.16 Key Takeaways
  • 8.1 Machine Learning Approach
  • 8.2 Steps 1 and 2
  • 8.3 Steps 3 and 4
  • 8.4 How it Works
  • 8.5 Steps 5 and 6
  • 8.6 Supervised Learning Model Considerations
  • 8.7 Knowledge Check
  • 8.8 Scikit-Learn
  • 8.9 Knowledge Check
  • 8.10 Supervised Learning Models - Linear Regression
  • 8.11 Supervised Learning Models - Logistic Regression
  • 8.12 Unsupervised Learning Models
  • 8.13 Pipeline
  • 8.14 Model Persistence and Evaluation
  • 8.15 Knowledge Check
  • 8.16 Assignment 01
  • 8.17 Assignment 01
  • 8.18 Assignment 02
  • 8.19 Assignment 02
  • 8.20 Quiz
  • 8.21 Key Takeaways
  • 9.1 NLP Overview
  • 9.2 NLP Applications
  • 9.3 Knowledge check
  • 9.4 NLP Libraries-Scikit
  • 9.5 Extraction Considerations
  • 9.6 Scikit Learn-Model Training and Grid Search
  • 9.7 Assignment 01
  • 9.8 Demo Assignment 01
  • 9.9 Assignment 02
  • 9.10 Demo Assignment 02
  • 9.11 Quiz
  • 9.12 Key Takeaway
  • 10.1 Introduction to Data Visualization
  • 10.2 Knowledge Check
  • 10.3 Line Properties
  • 10.4 (x,y) Plot and Subplots
  • 10.5 Knowledge Check
  • 10.6 Types of Plots
  • 10.7 Assignment 01
  • 10.8 Assignment 01 Demo
  • 10.9 Assignment 02
  • 10.10 Assignment 02 Demo
  • 10.11 Quiz
  • 10.12 Key Takeaways
  • 11.1 Web Scraping and Parsing
  • 11.2 Knowledge Check
  • 11.3 Understanding and Searching the Tree
  • 11.4 Navigating options
  • 11.5 Demo3 Navigating a Tree
  • 11.6 Knowledge Check
  • 11.7 Modifying the Tree
  • 11.8 Parsing and Printing the Document
  • 11.9 Assignment 01
  • 11.10 Assignment 01 Demo
  • 11.11 Assignment 02
  • 11.12 Assignment 02 demo
  • 11.13 Quiz
  • 11.14 Key takeaways
  • 12.1 Why Big Data Solutions are Provided for Python
  • 12.2 Hadoop Core Components
  • 12.3 Python Integration with HDFS using Hadoop Streaming
  • 12.4 Demo 01 - Using Hadoop Streaming for Calculating Word Count
  • 12.5 Knowledge Check
  • 12.6 Python Integration with Spark using PySpark
  • 12.7 Demo 02 - Using PySpark to Determine Word Count
  • 12.8 Knowledge Check
  • 12.9 Assignment 01
  • 12.10 Assignment 01 Demo
  • 12.11 Assignment 02
  • 12.12 Assignment 02 Demo
  • 12.13 Quiz
  • 12.14 Key takeaways
  • Project 1 Stock Market Data Analysis
  • Project 1 Demo
  • Project 02
  • Main project 02
  • Course Feedback