Today’s Free Udemy Courses are; Adobe Photoshop CC Crash Course Learn Photoshop In Two Hour, Complete Machine Learning with R Studio – ML for 2021, Product Marketing Strategy & Tactics That Really Works and Time Series Analysis and Forecasting using Python
Coupons are limited. So It may run out early
Free Udemy Course – 1 June 2021
Adobe Photoshop CC Crash Course Learn Photoshop In Two Hour
Hi There, I am Stephen Koel Soren and I am a Graphics and Web Expert. This is a crash course for Adobe Photoshop and You will learn only the essential part that is really required for design in Photoshop within hour. You will learn from this course:
- Color and Adjustment
- Content-Aware & Cropping
- Text Style
- Shadow
- Selection & Masking
- Blur & Filters
- Retouch
and many more.
This course is especially designed for beginner those who want to learn photoshop basic and essential part within a short time. You will have class projects in this course so you can apply your skill you have learned from lessons in class project. I have given an exercise file along with this course so you can practice along with me during learning period.
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Complete Machine Learning with R Studio – ML for 2021
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 of machine learning, R and predictive modelling. Each section contains a practice assignment for you to practically implement your learning on machine learning, R and predictive modelling.
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.
What are the steps I should follow to be able to build a Machine Learning model?
You can divide your learning process into 3 parts:
Statistics and Probability – Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.
Understanding of Machine learning – Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model
Programming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python
Understanding of models – Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.
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 than Python. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. As compared to Python, 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, usage of R and Python 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. Like Python, 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 are the major advantages of using R over Python?
- As compared to Python, R has a higher user base and the biggest number of statistical packages and libraries available. Although, Python has almost all features that analysts need, R triumphs over Python.
- R is a function-based language, whereas Python is object-oriented. If you are coming from a purely statistical background and are not looking to take over major software engineering tasks when productizing your models, R is an easier option, than Python.
- R has more data analysis functionality built-in than Python, whereas Python relies on Packages
- Python has main packages for data analysis tasks, R has a larger ecosystem of small packages
- Graphics capabilities are generally considered better in R than in Python
- R has more statistical support in general than Python
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.
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Product Marketing Strategy & Tactics That Really Works
This course is covered with Business to Business (B2B) Marketing and Business to Consumer (B2C) Marketing as well as Best Marketing Tactics. You will learn about Paid Marketing, Pay Per Click, Sponsorship, Endorsement and Influencer, Referral Program, Affiliate, Research, Search Engine Optimization (SEO), Blogging, Email Marketing Tactics for Product Marketing.
What Is Product Marketing? Product marketing actually starts with the customer. It is closely concerned with understanding the buyer and their journey, using this information to build a blueprint for positioning, targeting, launching, promoting, driving demand, encouraging adoption, and ensuring success of a product. Product marketing is the driving force behind getting products to market – and keeping them there. Product marketers are the overarching voices of the customer, masterminds of messaging, enablers of sales, and accelerators of adoption. All at the same time.
Who this course is for:
- You are considering promote your product or store
- You work as a Marketing Officer in B2B, or B2C company.
- You already have many years of experience in the role and are looking for new inspiration and strategies.
- You are interested in learning how to collaborate with brand.
- You want to learn how to make branding
- You are looking to start new business and create market your product.
- You have a small business but you want to grow your business and looking for increase sales.
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Time Series Analysis and Forecasting using Python
You’re looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right?
You’ve found the right Time Series Forecasting and Time Series Analysis course using Python Time Series techniques. This course teaches you everything you need to know about different time series forecasting and time series analysis models and how to implement these models in Python time series.
After completing this course you will be able to:
- Implement time series forecasting and time series analysis models such as AutoRegression, Moving Average, ARIMA, SARIMA etc.
- Implement multivariate time series forecasting models based on Linear regression and Neural Networks.
- Confidently practice, discuss and understand different time series forecasting, time series analysis models and Python time series techniques used by organizations
How will this course help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Time Series Forecasting course on time series analysis and Python time series applications.
If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it. You will also learn time series forecasting models, time series analysis and Python time series techniques.
Why should you choose this course?
We believe in teaching by example. This course is no exception. Every Section’s primary focus is to teach you the concepts through how-to examples. Each section has the following components:
- Theoretical concepts and use cases of different forecasting models, time series forecasting and time series analysis
- Step-by-step instructions on implement time series forecasting models in Python
- Downloadable Code files containing data and solutions used in each lecture on time series forecasting, time series analysis and Python time series techniques
- Class notes and assignments to revise and practice the concepts on time series forecasting, time series analysis and Python time series techniques
The practical classes where we create the model for each of these strategies is something which differentiates this course from any other available online course on time series forecasting, time series analysis and Python time series techniques.
.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 Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this course. They also have an in-depth knowledge on time series forecasting, time series analysis and Python time series techniques.
We are also the creators of some of the most popular online courses – with over 170,000 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 on time series forecasting, time series analysis and Python time series techniques.
Each section contains a practice assignment for you to practically implement your learning on time series forecasting, time series analysis and Python time series techniques.
What is covered in this course?
Understanding how future sales will change is one of the key information needed by manager to take data driven decisions. In this course, we will deal with time series forecasting, time series analysis and Python time series techniques. We will also explore how one can use forecasting models to
- See patterns in time series data
- Make forecasts based on models
Let me give you a brief overview of the course
- Section 1 – Introduction
In this section we will learn about the course structure and how the concepts on time series forecasting, time series analysis and Python time series techniques will be taught in this course.
- Section 2 – Python basics
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.
The basics taught in this part will be fundamental in learning time series forecasting, time series analysis and Python time series techniques on later part of this course.
- Section 3 – Basics of Time Series Data
In this section, we will discuss about the basics of time series data, application of time series forecasting, and the standard process followed to build a forecasting model, time series forecasting, time series analysis and Python time series techniques.
- Section 4 – 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 and execute time series forecasting, time series analysis and implement Python time series techniques.
- Section 5 – Getting Data Ready for Regression Model
In this section you will learn what actions you need to take a 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 bi-variate analysis then we cover topics like outlier treatment and missing value imputation.
- Section 6 – Forecasting using 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.
- Section 7 – 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 8 – Creating Regression and Classification ANN model in Python
In this part you will learn how to create ANN models in Python.
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. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.
I am pretty confident that the course will give you the necessary knowledge and skills related to time series forecasting, time series analysis and Python time series techniques to immediately see practical benefits in your work place.
