Machine Learning with Python/R
Machine Learning with Python/R
August 20, 2018

Machine Learning with Python/R

Machine Learning course will make you an expert in machine learning, a form of artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer.

Why learn Machine learning?

  • 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

What are the course objectives?

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 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.


COURSE SYLLABUS


Module 1 - Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
  • Machine Learning Languages, Types, and Examples 
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning 
  • Supervised Learning Classification 
  • Unsupervised Learning 

Module 2 - Supervised Learning I

  • K-Nearest Neighbors 
  • Decision Trees 
  • Random Forests
  • Reliability of Random Forests 
  • Advantages & Disadvantages of Decision Trees 

 Module 3 - Supervised Learning II

  • Regression Algorithms 
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models 

 Module 4 - Unsupervised Learning

  • K-Means Clustering plus Advantages & Disadvantages 
  • Hierarchical Clustering plus Advantages & Disadvantages 
  • Measuring the Distances Between Clusters - Single Linkage Clustering 
  • Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
  • Density-Based Clustering 
Module 5 - Dimensionality Reduction & Collaborative Filtering
  • Dimensionality Reduction: Feature Extraction & Selection 
  • Collaborative Filtering & Its Challenges 

REQUIREMENTS

  • R programming

What skills will you learn with our Machine Learning Course?

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

  • Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
  • 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

Who should take this Machine Learning Training Course?

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

What projects are included in this Machine Learning Training Course?

This 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 17: What 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 19: The 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 10: Classify 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 13: Demonstrate 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.

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