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Machine Learning & Deep Learning in Python & R

Course Description:
Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R


Publisher:
Start-Tech Academy


Detailed Description of the Course:

You’re looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?

You’ve found the right Machine Learning course!

After completing this course you will be able to:

· Confidently build predictive Machine Learning and Deep Learning models to solve business problems and create business strategy

· Answer Machine Learning related interview questions

· Participate and perform in online Data Analytics competitions such as Kaggle competitions

Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn.

How this course will help you?

Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear regression.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses – with over 600,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman – Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

Table of Contents

  • Section 1 – Python basic

    This section gets you started with Python.

    This section will help you set up the python and Jupyter environment on your system and it’ll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

  • Section 2 – R basic

    This section will help you set up the R and R studio on your system and it’ll teach you how to perform some basic operations in R.

  • Section 3 – Basics of Statistics

    This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation

  • Section 4 – Introduction to Machine Learning

    In this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

  • Section 5 – Data Preprocessing

    In this section you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.

  • Section 6 – Regression Model

    This section starts with simple linear regression and then covers multiple linear regression.

    We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

    We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.

  • Section 7 – Classification Models

    This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.

    We have covered the basic theory behind each concept without getting too mathematical about it so that you

    understand where the concept is coming from and how it is important. But even if you don’t understand

    it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

    We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.

  • Section 8 – Decision trees

    In this section, we will start with the basic theory of decision tree then we will create and plot a simple Regression decision tree. Then we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python and R

  • Section 9 – Ensemble technique
    In this section, we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. We will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.
  • Section 10 – Support Vector Machines
    SVM’s are unique models and stand out in terms of their conceptIn this section, we will discussion about support vector classifiers and support vector machines.
  • Section 11 – ANN Theoretical Concepts

    This part will give you a solid understanding of concepts involved in Neural Networks.

    In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Section 12 – Creating ANN model in Python and R

    In this part you will learn how to create ANN models in Python and R.

    We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.

    We also understand the importance of libraries such as Keras and TensorFlow in this part.

  • Section 13 – CNN Theoretical Concepts

    In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.

    In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

  • Section 14 – Creating CNN model in Python and R
    In this part you will learn how to create CNN models in Python and R.

    We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.

  • Section 15 – End-to-End Image Recognition project in Python and R
    In this section we build a complete image recognition project on colored images.

    We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).

  • Section 16 – Pre-processing Time Series Data

    In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models

  • Section 17 – Time Series Forecasting

    In this section, you will learn common time series models such as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.

By the end of this course, your confidence in creating a Machine Learning or Deep Learning model in Python and R will soar. You’ll have a thorough understanding of how to use ML/ DL models to create predictive models and solve real world business problems.

Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Why use Python for Machine Learning?

Understanding Python is one of the valuable skills needed for a career in Machine Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

Why use R for Machine Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R

1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.

2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.

5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.


Take Course
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The Python Programming For Everyone Immersive Training

Course Description:
Learn, Practice, Master, Think like Python Professionals & Be A Certified Python Super Hero in short time!


Publisher:
SDE OCTOPUS by Ahmed I


Detailed Description of the Course:

Welcome to The Python Programming For Everyone Immersive Training.

This Ultimate Masterclass covers all the essential topics to become a Professional Python developer like: variables, data types, Strings, data structures,  functional programming, different types of modules, files handling, object-oriented programming and more.

You’ll get A demonstration of each point in this training and an explanation of all theoretical and practical aspects in an easy way and in an easy language for anyone.

Also, you can test your skills using quizzes and be a certified python developer that can be hired and you can upload the certificate of completion to your profile.
Python is one of the coolest,and best programming languages in terms of ease and features.

It is very easy for you to read the Python code, as if you were reading a regular English sentence.

The Python language can work with everything indisputably.

With Python, It is possible to do everything you imagine in the world of programming and data.

Python can work in areas such as:

Data Science.

Machine Learning.

Deep Learning.

Artificial intelligence.

Ethical Hacking.

Blockchain Applications.

Web Scraping.

Web Applications.

Mobile Applications.

Desktop Applications.

Games Applications.

Browser Extensions.

And many other fields.

And you’ll get a full support during this course by the instructor if you encounter any problems.


Solidity and Blockchain for beginners

Course Description:
Build 21 SQL Queries to solve business problems in Real World


Publisher:
Bluelime Learning Solutions


Detailed Description of the Course:

Solidity is an object-oriented, high-level language for implementing smart contracts. Smart contracts are programs which govern

the behaviour of accounts within the Ethereum state.

Solidity was influenced by C++, Python and JavaScript and is designed to target the Ethereum Virtual Machine (EVM).

Solidity is statically typed, supports inheritance, libraries and complex user-defined types among other features.

With Solidity you can create contracts for uses such as voting, crowdfunding, blind auctions, and multi-signature wallets.

A contract in the sense of Solidity is a collection of code (its functions) and

data (its state) that resides at a specific address on the Ethereum blockchain.

Blockchain is a very powerful technology that allows everyday users

to exchange sensitive data without a middleman. Programmers can leverage the blockchain in their

applications using Solidity, a programming language for the Ethereum platform.

This basic beginners course teaches you how to build a simple contract-based application with Solidity.

What You will learn include:

  • Blockchain terminology
  • Basic layout of a solidity source file
  • Importing  other source files
  • Using comments in solidity
  • Basic solidity syntax
  • Basic structure of a solidity contract
  • Solidity Types
  • Ethereum Virtual Machine -EVM
  • Functions and Function Modifiers
  • Solidity Function Syntax
  • Solidity Compiler
  • Solidity Events
  • Creating a solidity contract
  • Solidity Operators
  • Solidity Units
  • Special types of contract
  • Special variables and Functions
  • State variables

Take Course
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SQL Code Challenges: Use SQL to answer Business questions

Course Description:
Build 21 SQL Queries to solve business problems in Real World


Publisher:
Bluelime Learning Solutions


Detailed Description of the Course:

 

SQL is a standard language for storing, manipulating and retrieving data in databases.SQL is a standard language for storing, manipulating and retrieving data in databases.

The best way to build up your skills in any area is by practice. This course is a hands-on practical learn by doing course.

SQL ( SQL -Structural Query language ) is one of the most popular and sort after skills in the IT industry. SQL Skills is so important especially with big data and data dependent businesses and applications from Facebook to our local banks.

This course was designed with the beginner in mind. The course starts by walking you through how to setup a lab where you will download and install Microsoft SQL Server and the sample Northwind database.

In this course we will solve 21 business related problem questions by building SQL queries to interrogate the database and produce the answers to solving the business problems.

We will use the following while building our queries to solve the business problems :

Retrieve data with SELECT
Use Case Expressions
Eliminate duplicates
Filter data with equality filters
Filter data with basic comparisons
Filter data with string comparisons
Filter data with logical comparisons
Filter data with NULL comparisons
Use order by clause
Sort data by ascending and descending order
Use Table joins to extract data from multiple tables
Use UNION operator to combine results of two or more SELECT Statements
Aggregate functions to compute a single output value
GROUP BY Clause to group related data

By the end of this course you will be would have gained enough skills to interrogate Databases.


Take Course
Click Here


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