New Year Resolution #2: Learn something new everyday

This is post is part of New Year Resolution and how to achieve them series..

According to Gartner Top 10 Strategic Technology Trends for 2019,Blockchain, quantum computing, augmented analytics and artificial intelligence will drive disruption and new business models.

Augmented analytics represents a third major wave for data and analytics capabilities as data scientists use automated algorithms to explore more hypotheses. Data science and machine learning platforms have transformed how businesses generate analytics insight.

Here’s best compilation that will boost your career and expand your knowledge. Check them out, and start learning today!

Udemy Courses

#1.Artificial Intelligence

There is huge enterprise-level interest in artificial intelligence projects and their potential to fundamentally change the dynamics of business value. However,biggest pain point that emerged from Gartner’s 2018 CIO survey was the lack of specialized skills in AI, with 47% of CIOs reporting that they needed new skills for AI projects.With Gartner predicting AI as #2 in Top 10 Strategic Technology Trends for 2019.There is need for AI engineers to build, implement, and maintain AI projects.Here’s best compilation of 15 Udemy Artificial Intelligence Courses.

TOP 15 Udemy Artificial Intelligence Courses 

#2.Machine Learning

Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms therefore learn from test data that has not been labeled, classified or categorized.

Ninety percent of all the world’s data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 exabytes a day. That number doubles every month.That means,there is always exponential need for Machine learning engineers to build, implement, and maintain machine learning systems,algorithms in technology products with focus on machine learning system reliability, performance, and scalability.

TOP 25 Udemy Machine Learning courses 

TOP 25 Udemy Machine Learning courses (Level – Intermediate) 

17 Algorithms Machine Learning Engineers Need to Know

TOP 22 Most Popular Deep Learning Courses on Udemy

#3.Data Science

Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. Data Scientists perform sophisticated empirical analysis to understand and make predictions about complex systems. They draw on methods and tooling from probability and statistics, mathematics, and computer science and primarily focus on extracting insights from data. They communicate results through statistical models, visualizations, and data products.

Per IBM study, By 2020 the number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000.The following summary graphic from the study highlights how in-demand data science and analytics skill sets are today and are projected to be through 2020.

ULTIMATE Guide to Data Science Courses (Over 65+ courses covered)

TOP 35 Most Popular Data Science courses on Udemy

TAKE ACTION AND START ENROLLING TODAY! All of the below listed courses are running at discount of about 90%. And for any reason you are unhappy with the course, Udemy has a 30 day Money Back Refund Policy, So no questions asked and no Risk to you. You got nothing to lose.

Coursera Courses

#1.Machine Learning

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

#2.Neural Networks and Deep Learning

Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new “superpower” that will let you build AI systems that just weren’t possible a few years ago. In this course, you will learn the foundations of deep learning.

When you finish this class, you will:

• Understand the major technology trends driving Deep Learning
• Be able to build, train and apply fully connected deep neural networks
• Know how to implement efficient (vectorized) neural networks
• Understand the key parameters in a neural network’s architecture

This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.

#3.Convolutional Neural Networks

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

You will: –

• Understand how to build a convolutional neural network, including recent variations such as residual networks.
• Know how to apply convolutional networks to visual detection and recognition tasks.
• Know to use neural style transfer to generate art.
• Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

This is the fourth course of the Deep Learning Specialization.

#4.Introduction to Data Science in Python

This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it,  manipulate it, and run basic inferential statistical analyses.

This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.

#5.R Programming

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

#6.The Data Scientist’s Toolbox

In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

#7.Python Data Structures

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3.

#8.Algorithmic Toolbox

The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).

Programmers with basic experience looking to understand the practical and conceptual underpinnings of algorithms, with the goal of becoming more effective software engineers. Computer science students and researchers as well as interdisciplinary students (studying electrical engineering, mathematics, bioinformatics, etc.) aiming to get more profound understanding of algorithms and hands-on experience implementing them and applying for real-world problems. Applicants who want to prepare for an interview in a high-tech company.

#9.Functional Programming Principles in Scala

Functional programming is becoming increasingly widespread in industry. This trend is driven by the adoption of Scala as the main programming language for many applications. Scala fuses functional and object-oriented programming in a practical package. It interoperates seamlessly with both Java and Javascript. Scala is the implementation language of many important frameworks, including Apache Spark, Kafka, and Akka. It provides the core infrastructure for sites such as Twitter, Tumblr and also Coursera.

In this course you will discover the elements of the functional programming style and learn how to apply them usefully in your daily programming tasks. You will also develop a solid foundation for reasoning about functional programs, by touching upon proofs of invariants and the tracing of execution symbolically.

The course is hands on; most units introduce short programs that serve as illustrations of important concepts and invite you to play with them, modifying and improving them. The course is complemented by a series programming projects as homework assignments.

Learning Outcomes. By the end of this course you will be able to:

• understand the principles of functional programming,
• write purely functional programs, using recursion, pattern matching, and higher-order functions,
• combine functional programming with objects and classes,
• design immutable data structures,
• reason about properties of functions,
• understand generic types for functional programs

Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line.

#10.Machine Learning Foundations: A Case Study Approach

In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.

This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.

Learning Outcomes: By the end of this course, you will be able to:

• Identify potential applications of machine learning in practice.
• Describe the core differences in analyses enabled by regression, classification, and clustering.
• Select the appropriate machine learning task for a potential application.
• Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
• Represent your data as features to serve as input to machine learning models.
• Assess the model quality in terms of relevant error metrics for each task.
• Utilize a dataset to fit a model to analyze new data.
• Build an end-to-end application that uses machine learning at its core.
• Implement these techniques in Python.

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Article Name

New Year Resolution #2: Learn something new everyday

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This is post is part of New Year Resolution and how to achieve them series..

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Karthik

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Upnxtblog

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