[100% OFF] Machine Learning & Deep Learning in Python & R


Publisher : Start-Tech Academy

Price : $109

Course Language : English

Description

You’re in search of an entire Machine Learning and Deep Learning course that may assist you launch a flourishing profession in the sphere of Data Science & Machine Learning, proper?

You’ve discovered the proper Machine Learning course!

After finishing this course it is possible for you to to:

· Confidently construct predictive Machine Learning and Deep Learning fashions to resolve enterprise issues and create enterprise technique

· Answer Machine Learning associated interview questions

· Participate and carry out in on-line Data Analytics competitions comparable to Kaggle competitions

Check out the desk of contents under to see what all Machine Learning and Deep Learning fashions you’re going to study.

How this course will assist you?

A Verifiable Certificate of Completion is introduced to all college students who undertake this Machine studying fundamentals course.

If you’re a enterprise supervisor or an govt, or a pupil who desires to study and apply machine studying in Real world issues of enterprise, this course gives you a stable base for that by educating you the preferred methods of machine studying.

Why do you have to select this course?

This course covers all of the steps that one ought to take whereas fixing a enterprise drawback by means of linear regression.

Most programs solely concentrate on educating tips on how to run the evaluation however we imagine that what occurs earlier than and after working evaluation is much more vital i.e. earlier than working evaluation it is rather vital that you’ve the proper information and do some pre-processing on it. And after working evaluation, you need to be capable to choose how good your mannequin is and interpret the outcomes to truly be capable to assist your online business.

What makes us certified to show you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting agency, we’ve got helped companies remedy their enterprise drawback utilizing machine studying methods and we’ve got used our expertise to incorporate the sensible features of knowledge evaluation in this course

We are additionally the creators of among the hottest on-line programs – with over 600,000 enrollments and 1000’s of 5-star critiques like these ones:

This is excellent, i like the very fact the all clarification given could be understood by a layman – Joshua

Thank you Author for this excellent course. You are the most effective and this course is price any worth. – Daisy

Our Promise

Teaching our college students is our job and we’re dedicated to it. If you might have any questions in regards to the course content material, follow sheet or something associated to any matter, you’ll be able to all the time put up a query in the course or ship us a direct message.

Download Practice information, take Quizzes, and full Assignments

With every lecture, there are class notes connected so that you can comply with alongside. You can even take quizzes to examine your understanding of ideas. Each part accommodates a follow project so that you can virtually implement your studying.

Table of Contents

  • Section 1 – Python fundamental

    This part will get you began with Python.

    This part will assist you arrange the python and Jupyter setting in your system and it will train

    you tips on how to carry out some fundamental operations in Python. We will perceive the significance of various libraries comparable to Numpy, Pandas & Seaborn.

  • Section 2 – R fundamental

    This part will assist you arrange the R and R studio in your system and it will train you tips on how to carry out some fundamental operations in R.

  • Section 3 – Basics of Statistics

    This part is split into 5 completely different lectures ranging from sorts of information then sorts of statistics

    then graphical representations to explain the info after which a lecture on measures of middle like imply

    median and mode and lastly measures of dispersion like vary and commonplace deviation

  • Section 4 – Introduction to Machine Learning

    In this part we are going to study – What does Machine Learning imply. What are the meanings or completely different phrases related to machine studying? You will see some examples so that you simply perceive what machine studying truly is. It additionally accommodates steps concerned in constructing a machine studying mannequin, not simply linear fashions, any machine studying mannequin.

  • Section 5 – Data Preprocessing

    In this part you’ll study what actions you must take a step-by-step to get the info after which

    put together it for the evaluation these steps are crucial.

    We begin with understanding the significance of enterprise data then we are going to see tips on how to do information exploration. We learn to do uni-variate evaluation and bi-variate evaluation then we cowl matters like outlier remedy, lacking worth imputation, variable transformation and correlation.

  • Section 6 – Regression Model

    This part begins with easy linear regression after which covers a number of linear regression.

    We have coated the essential idea behind every idea with out getting too mathematical about it so that you simply

    perceive the place the idea is coming from and the way it is vital. But even if you happen to do not perceive

    it,  will probably be okay so long as you learn to run and interpret the outcome as taught in the sensible lectures.

    We additionally take a look at tips on how to quantify fashions accuracy, what’s the which means of F statistic, how categorical variables in the impartial variables dataset are interpreted in the outcomes, what are different variations to the odd least squared technique and the way will we lastly interpret the outcome to search out out the reply to a enterprise drawback.

  • Section 7 – Classification Models

    This part begins with Logistic regression after which covers Linear Discriminant Analysis and Okay-Nearest Neighbors.

    We have coated the essential idea behind every idea with out getting too mathematical about it so that you simply

    perceive the place the idea is coming from and the way it is vital. But even if you happen to do not perceive

    it,  will probably be okay so long as you learn to run and interpret the outcome as taught in the sensible lectures.

    We additionally take a look at tips on how to quantify fashions efficiency utilizing confusion matrix, how categorical variables in the impartial variables dataset are interpreted in the outcomes, check-practice cut up and the way will we lastly interpret the outcome to search out out the reply to a enterprise drawback.

  • Section 8 – Decision bushes

    In this part, we are going to begin with the essential idea of determination tree then we are going to create and plot a easy Regression determination tree. Then we are going to develop our data of regression Decision tree to classification bushes, we may even learn to create a classification tree in Python and R

  • Section 9 – Ensemble method
    In this part, we are going to begin our dialogue about superior ensemble methods for Decision bushes. Ensembles methods are used to enhance the soundness and accuracy of machine studying algorithms. We will focus on Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.

  • Section 10 – Support Vector Machines
    SVM’s are distinctive fashions and stand out in phrases of their idea. In this part, we are going to dialogue about help vector classifiers and help vector machines.

  • Section 11 – ANN Theoretical Concepts

    This half gives you a stable understanding of ideas concerned in Neural Networks.

    In this part you’ll study in regards to the single cells or Perceptrons and the way Perceptrons are stacked to create a community structure. Once structure is about, we perceive the Gradient descent algorithm to search out the minima of a operate and learn the way that is used to optimize our community mannequin.

  • Section 12 – Creating ANN mannequin in Python and R

    In this half you’ll learn to create ANN fashions in Python and R.

    We will begin this part by creating an ANN mannequin utilizing Sequential API to resolve a classification drawback. We learn to outline community structure, configure the mannequin and practice the mannequin. Then we consider the efficiency of our skilled mannequin and use it to foretell on new information. Lastly we learn to save and restore fashions.

    We additionally perceive the significance of libraries comparable to Keras and TensorFlow in this half.

  • Section 13 – CNN Theoretical Concepts

    In this half you’ll find out about convolutional and pooling layers that are the constructing blocks of CNN fashions.

    In this part, we are going to begin with the essential idea of convolutional layer, stride, filters and have maps. We additionally clarify how grey-scale photographs are completely different from coloured photographs. Lastly we focus on pooling layer which deliver computational effectivity in our mannequin.

  • Section 14 – Creating CNN mannequin in Python and R
    In this half you’ll learn to create CNN fashions in Python and R.

    We will take the identical drawback of recognizing trend objects and apply CNN mannequin to it. We will examine the efficiency of our CNN mannequin with our ANN mannequin and see that the accuracy will increase by 9-10% once we use CNN. However, this isn’t the top of it. We can additional enhance accuracy by utilizing sure methods which we discover in the subsequent half.

  • Section 15 – End-to-End Image Recognition mission in Python and R
    In this part we construct an entire picture recognition mission on coloured photographs.

    We take a Kaggle picture recognition competitors and construct CNN mannequin to resolve it. With a easy mannequin we obtain practically 70% accuracy on check set. Then we study ideas like Data Augmentation and Transfer Learning which assist us enhance accuracy stage from 70% to almost 97% (nearly as good because the winners of that competitors).

  • Section 16 – Pre-processing Time Series Data

    In this part, you’ll learn to visualize time sequence, carry out characteristic engineering, do re-sampling of knowledge, and numerous different instruments to investigate and put together the info for fashions

  • Section 17 – Time Series Forecasting

    In this part, you’ll study widespread time sequence fashions comparable to Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.

By the top of this course, your confidence in making a Machine Learning or Deep Learning mannequin in Python and R will soar. You’ll have an intensive understanding of tips on how to use ML/ DL fashions to create predictive fashions and remedy actual world enterprise issues.

Below is an inventory of well-liked FAQs of scholars who need to begin their Machine studying journey-

What is Machine Learning?

Machine Learning is a area of pc science which supplies the pc the power to study with out being explicitly programmed. It is a department of synthetic intelligence based mostly on the concept that methods can study from information, determine patterns and make selections with minimal human intervention.

Why use Python for Machine Learning?

Understanding Python is likely one of the helpful expertise wanted for a profession in Machine Learning.

Though it hasn’t all the time been, Python is the programming language of alternative for information science. Here’s a short historical past:

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

    In 2017, it overtook R on KDNuggets’s annual ballot of knowledge scientists’ most used instruments.

    In 2018, 66% of knowledge scientists reported utilizing Python every day, making it the primary device for analytics professionals.

Machine Learning consultants anticipate this pattern to proceed with rising improvement in the Python ecosystem. And whereas your journey to study Python programming could also be simply starting, it’s good to know that employment alternatives are plentiful (and rising) as nicely.

Why use R for Machine Learning?

Understanding R is likely one of the helpful expertise wanted for a profession in Machine Learning. Below are some explanation why you need to study Machine studying in R

1. It’s a well-liked language for Machine Learning at prime tech companies. Almost all of them rent information scientists who use R. Facebook, for instance, makes use of R to do behavioral evaluation with consumer put up information. Google makes use of R to evaluate advert effectiveness and make financial forecasts. And by the best way, it’s not simply tech companies: R is in use at evaluation and consulting companies, banks and different monetary establishments, tutorial establishments and analysis labs, and just about all over the place else information wants analyzing and visualizing.

2. Learning the info science fundamentals is arguably simpler in R. R has an enormous benefit: it was designed particularly with information manipulation and evaluation in thoughts.

3. Amazing packages that make your life simpler. Because R was designed with statistical evaluation in thoughts, it has a incredible ecosystem of packages and different sources which are nice for information science.

4. Robust, rising group of knowledge scientists and statisticians. As the sphere of knowledge science has exploded, R has exploded with it, turning into one of many quickest-rising languages in the world (as measured by StackOverflow). That means it’s straightforward to search out solutions to questions and group steering as you’re employed your method by means of tasks in R.

5. Put one other device in your toolkit. No one language goes to be the proper device for each job. Adding R to your repertoire will make some tasks simpler – and naturally, it’ll additionally make you a extra versatile and marketable worker once you’re in search of jobs in information science.

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

Put merely, machine studying and information mining use the identical algorithms and methods as information mining, besides the sorts of predictions differ. While information mining discovers beforehand unknown patterns and data, machine studying reproduces identified patterns and data—and additional routinely applies that data to information, determination-making, and actions.

Deep studying, then again, makes use of superior computing energy and particular sorts of neural networks and applies them to massive quantities of knowledge to study, perceive, and determine sophisticated patterns. Automatic language translation and medical diagnoses are examples of deep studying.


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