Machine Learning for Engineers using Python

Machine Learning for Engineers using Python

  • Domain : ELECTRICAL, DATA SCIENCE, MECHANICAL, ELECTRONICS
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A Quick Overview

Machine Learning and Artificial Intelligence is a booming field. Machine learning gives the ability to machine to learn and behave in a particular manner without being explicitly programmed and learn from their experience with the help of data. You will see the application of machine learning and Artificial intelligence everywhere whether you talk about product recommendation by Amazon, Flipkart, etc. or movie recommendation by Youtube, Netflix, etc.  You can also see the applications of Machine Learning and Artificial Intelligence in customer segmentation, fraud detection, google map, Facebook auto-tagging, spam email detection, etc. This is all done by data engineers and data scientists sitting at the workplace.

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COURSE SYLLABUS

1Introduction to data science and programming languages (tools) for data science

Introduction about data science and big data and its importance and will introduce about various programming languages (tools) used for data science.

2Basics of programming

Explanation about variables, operators, data types, data structure, control structure in Python, Function file in Python

3Essential Python libraries

Include Numpy, Scipy, Pandas, Matplotlib, Seaborn, etc.

4Introduction to machine learning Cross- validation and bias variance tradeoff

This module will include basics of machine learning and its classification and include fitting of model with cross validation and bias variance trade off.

5Evaluation metrics

Evaluation metrics for model validation

6Importing data and hands on imported data

EDA/ correlation/ feature extraction/ hyper parameters

7Univariate and multivariate linear regression

This module will introduce univariate and multivariate linear regression and will explain how it can be implemented in Python

8Principal component analysis

Explanation about Eigen values and Eigen vectors and singular value decomposition and then PCA

9Logistic regression and k-nearest neighbor

Explanation and implementation of logistic regression and k-nearest neighbor in Python

10Decision tree and Random forest

Explanation and implementation of Decision tree and Random forest in Python

11K-mean and Hierarchical clustering

Clustering of data using clustering algorithm

12Neural network

Explanation of logistic regression with neural network mindsets


Projects Overview

Project 1

Highlights

 “EarlySalary” a stock forecasting company has employed you as a Data Scientist. As a first job, the manager has provided you with stock market data and asked you to check the quality of data. There are two files “Stock_File_1.csv” and “Stock_File_2.txt”. Some details of the data shared with you are

(a) The data set consists of six variables namely-date, Open, High, Low, Close, Volume

(b) The stock market opens at 9:15 am and closes at 3:30 pm.

Each stock is defined by an opening price and closing price i.e. the price at which it open and the price at which it closes. During operating regime, the stock prices touches maximum and minimum price. You have an access to tens years of monthly stock price data with the open, high, low, close and the volume that signifies the number of stocks traded. On some day there is no trading, the open, high, low, close remains constant and the volume is zero. Now, you go to your manager after data visualization and exploratory analysis and model building (do not build a model, assume you built it for sake), your manager says the model predictions are poor as the data is polluted (reported by manager that that instant). Now try to impress your boss by doing some data preprocessing. Assume that you fill missing values by mean of the data corresponding to each feature. Remenber: please merge the data before preprocessing. You can use pandas.concat (read) to merge your data

Project 2

Highlights

 “Sandman” is willing to do some manufacturing analytics for their manufacturing plant.

Description: The datasets consists of 13 variables and 388 samples. The table below gives the description of the dataSSandman corporation continuously looks to improve its manufacturing quality. Sandman monitors quality of input materials (physical and chemical properties) on a daily basis. Also, it monitors quality of output material for every batch and output rejection rates are available on daily basis. Sandman wishes to develop a early warning system (EWS) to predict likely rejection for the new day, given the input quality for that day. They hired you to develop EWS. Put all the knowledge gathered in the course to achieve your objective.


WHO IS THIS COURSE FOR ?


  • Students in Electrical Engineering
  • Freshers looking to gain project experience on Machine Learning & Artificial Intelligence

SOFTWARE COVERED


Flexible Course Fees

Choose the plan that’s right for you

Basic

2 Months Access

$94.99

Per month for 3 months

  • Access Duration : 2 months
  • Mode of Delivery : Online
  • Project Portfolio : Available
  • Certification : Available
  • Email Support : Available
  • Forum Support : Available
Premium

Lifetime Access

$203.55

Per month for 3 months

  • Access Duration : Lifetime
  • Mode of Delivery : Online
  • Project Portfolio : Available
  • Certification : Available
  • Individual Video Support : 12/month
  • Group Video Support : 12/month
  • Email Support : Available
  • Forum Support : Available
  • Telephone Support : Available
  • Dedicated Support Engineer : Available

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Certification

  • Top 5% of the class will get a merit certificate
  • Course completion certificates will be provided to all students
  • Build a professional portfolio
  • Automatically link your technical projects
  • E-verified profile that can be shared on LinkedIn

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Frequently Asked Questions

1Who can take your course?

Any enthusiastic student with some knowledge of statistics and programming and who is willing to learn machine learning and build career in it.

2What is included in your course?

Complete overview of the machine learning and its application

3What will the student gain from your course?

Students can able to develop codes in Python to solve machine learning problems.

4What software skills are you teaching and how well are these tools used in the industry?

I am teaching Python, which is freeware and are most widely used in industries to solve machine learning problems.

5What is the real world application for the tools and techniques will you teach in this course?

Now a days, data is the fuel for everything. To extract meaningful insights from data, tools developed in Python will assist the participants to achieve this goal.

6Which companies use these techniques and for what?

As there are almost too many companies that uses data science and machine learning to extract meaningful insight from data, therefore, it is very difficult to name such companies. Some of the field where data science and machine learning are used include hospital, pharma companies, manufacturing and process plants, etc.

7How is your course going to help me in my path to MS or PhD?

If you are looking to do your MS or PhD in data science field, then absolutely this course is meant for you.

8How is this course going to help me get a job?

Since, data scientists, data engineer and data analyst are in demand in 20th century. There is huge scope that you will land up in high paying job after learning this course.

9How much time should I spend each day to complete the course?

A dedicated 2 hours of time is more than enough to understand the concepts, noting the concepts down and performing the challenges.


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