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h2kinfosys-blog · 4 years ago

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Best Platform to Learn Artificial Intelligence in 2021

You want to be an AI developer? Sign up for their AI training here

Artificial Intelligence has been the buzzword for the past years and is poised to remain so in the coming years. It is said that by 2030, AI will contribute about $30 trillion in total to the economy around the world. This is no fluke. AI has penetrated every field of human endeavor and will continue to do so. In healthcare, for instance, AI can create systems that detect diseases from an individual's picture. The famous DeepMind project, AlphaFold is set to make a massive revolution on how we understand diseases and tackle them. Not only in healthcare, but AI is also having a tremendous impact on finance, music, automobile, social media, cybersecurity, VR, and many more.

This explains why data science (and by extension Artificial Intelligence) is tagged the sexiest job of the 21st century. Being proficient in creating problem-solving AI systems will make you stand out in the labor market. The demand for AI experts is just simply outrageous.

What's more interesting is that you don’t necessarily have to get a degree in Artificial Intelligence to build AI systems. There are numerous online platforms where you can get everything you need to kickstart a career in Artificial Intelligence. We have compiled a list of the best online platforms to learn Artificial Intelligence. In this tutorial, we will share where you can get world-class resources online.

Coursera’s Machine Learning Course

This is perhaps one of the most popular AI courses on Coursera. It was taken by Coursera’s co-founder, Andrew Ng, a professor of Computer Science at Stanford University. Andrew Ng is also the founder of Google Brain; a research department in Google’s Deep Mind.

The machine learning course at Coursera begins with the foundation of machine learning and how it is changing the world in terms of speech recognition, computer vision, game development, and so on. Going ahead, you will be exposed to more technical concepts such as gradient descent, the working principle of linear regression, and all. If however, you do not have a decent knowledge of mathematics, you may struggle in this segment.

But overall, it is a good course with thousands of successful learners.

Udacity Machine Learning Nanodegree Program

Udacity Machine Learning Nanodegree program is another great platform to learn artificial intelligence. The program is split into various sections, based on the different kinds of machine learning projects: Supervised, Unsupervised, and Reinforcement Learning. After every section, there is a capstone project to demonstrate your understanding of what has been taught.

It is important however to note that the course assumes you have a decent knowledge of python and other machine learning algorithms. If you are completely new to programming, you may want to learn perhaps Python before jumping on this program. Generally speaking, it is a great place to learn Artificial Intelligence and get the certification at a cost.

Learn with Google AI

Google developers are behind the Tensorflow library used for creating deep learning models. They have now created a platform to learn about AI, especially creating models with Tensorflow. One thing about this platform is that the resources are mostly in written form. More like a blog post where different topics are added per time.

If you are the type that prefers reading, this platform is a great place as topics are well explained, with necessary diagrams. The course is designed such that a complete fresher can learn the very basics and get started with AI. It begins with a gentle introduction to machine learning and gradually moves to more advanced topics such as building neural networks with Tensorflow.

Coursera’s IBM Applied AI Professional Certificate

This is an AI-centric course offered by IBM and hosted on Coursera. The course is targeted at those who want to learn what it takes to become an AI developer. It is a conceptual and practical course, from explaining concepts such as computer vision, natural language processing to others like using APIs, creating image classifiers, building an AI-powered chatbot, and deploying your products on the web.

It is a beginner-friendly course with no prerequisite knowledge of Python. IBM’s course is also laced with hands-on projects.

H2kinfosys Artificial Intelligence Training Course

H2kinfosys is a reputable IT training platform in the US. They have successfully trained hundreds of thousands of trainees across various technologies such as AI, Software Testing, Business Intelligence, Big Data, etc. Their Artificial Intelligence Training Course is particularly unique. They offer live classes as opposed to the recorded classes you get on other platforms. This will allow you to ask questions and clarify areas that are not clear to you on the spot.

The course outline is also interesting. You first get to learn the basic statistics you’d require in AI, then you’d be exposed to the Python programming language as a beginner. So you have no fear if you have no maths or coding background. Once you get confident with doing cool stuff with Python, you learn how to build machine learning and deep learning models with real-life applications.

You also get to practice with real-time projects to get hands-on experience as the training progresses. The instructors who double as your mentors are the AI industry that rides on years of industry experience.

You want to be an AI developer? Sign up for their AI training here

#Ai online training #Artificial Intelligence online training

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h2kinfosys-blog · 4 years ago

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Learn Data Science from Scratch in 2021

According to reports, over 2.5 quantibytes of data is generated every single day. Putting that in perspective, every person of the over 7 billion persons in the world generates over 1.4 MB of data every second. But this data is as good as nothing when left bare. The onus is on data scientists to wrangle the data and distill actionable insights. Perhaps, this is why data science is called the sexiest job of this decade.

What's even more interesting is that the field is open to a lot of freshers. But if you're looking to start a career in data science, a mistake you don't want to make is not having a plan. There are a lot of materials on the web, and you just may get overwhelmed trying to consume all at once.

In this post, I will give you a practical guide on how to learn data science from scratch. Let's get started.

The 3 Important Things You'd Be Needing

There are 3 vital ingredients in becoming a top data scientist:

Some programming knowledge for wrangling data and creating machine learning models

SQL for managing databases, and

A decent knowledge of statistics to understand the concepts that underpin the data transformation process and machine learning algorithms.

Let's take each of them.

1. Programming

If you'd be getting into data science, you'd need to have some programming skills to create models. There are two popular programming languages used for data science; R and Python. I personally am biased towards Python, not just because it is what I use but Python is fairly easy to learn.

Besides, Python is used for many other applications besides data science. This is what makes it popular. Python packs a lot of packages, APIs, and modules for data science.

When learning Python for data science, you may not need all the knowledge of the programming language as it is quite broad. However, there are some concepts in Python you must master. They include

Data types and data structures (lists, dictionaries, tuples, etc)

List slicing and comprehensions

Using os and pickle library

Conditional statements and control flow

Object-Oriented Programming

Once, you’re comfortable with these concepts in Python, you can move on to the next stage.

In the next step, you will need to master libraries for machine learning. Specifically, you will need to fully grasp the use of the following.

Numpy and Pandas for Data Preprocessing

Matplotlib and Seaborn for Data Visualization

Sklearn, Pytorch, Keras, Tensorflow for machine learning model building

When practicing these skills, the place of quality data cannot be overemphasized. You can get interesting datasets from platforms such as Kaggle or UCI Machine Learning Repository. Kaggle and GitHub are also great places to find machine learning models you can practice and use on-the-go.

You will also find great competitions you can jump on in Kaggle. Engaging in these competitions would help you build a solid data science workforce. Furthermore, you’d be exposed to a better approach to solving problems from fellow competitors.

Datasets on Kaggle are however fairly clean data. You may take it a step further by scraping the web for data yourself. These kinds of data are mostly unclean. But it is good practice as this is the nature of data you’d be faced with when solving novel problems. Don’t be scared to try your hands on them.

When you encounter problems with your code, Stack Overflow is a great platform to get help. Finally, H2kinfosys offers a complete course in Data Science that would guide you through this entire process and make you excellent in the skill explained above.

2. SQL

SQL is another vital skill a data scientist must matter. In fact, it is asked the most in most interview sessions. This of course is no coincidence; it goes to show how the skill is in demand in the industry. It is a given that the robustness of your machine learning model is hinged on the kind of data at your disposal. But have you thought about how the data is managed and extracted? SQL.

SQL helps you create a dataset pipeline that is used to organize and sort data based on its relationship with the various features. It can also do extract, transform and load (ETL) operations.

Databases can be classified as relational and nonrelational databases. For relational databases, you will need tools such as MySQL, PostgreSQL, Oracle dataset, etc. MongoDB, Neo4j are common tools to deal with non-relational databases.

The best way to master SQL is by practice. You will need to play around with a lot of datasets and see how you can manage them using SQL. You can start with SQLite as it provides a beginner-friendly experience with its support for a small dataset and less intensive efforts. You may, however, have issues with sources for datasets to practice with. This is the major bottleneck with SQL.

3. Statistics and Linear algebra

Statistics and linear algebra and what kinds of the underlying principles of most machine learning models. What’s more? To build great intuition on how to wrangle data, your statistics and algebra must be impeccable. Well, I’m not saying you've got to get a degree in mathematics. What I'm saying however is that you can't afford to be oblivious of some concepts in mathematics such as linear algebra and statistics.

If you are adventurous, you may want to try to build some of the common ML algorithms such as Linear Regression from scratch. Attempting to build the code from scratch would particularly require a decent knowledge of mathematics.

Let me point out that your efforts would not go unrewarded. Coding machine learning algorithms would give you a high-level intuition on how to optimize the algorithm’s hyperparameter for better performance, making you a unique data scientist.

Again, H2kinfosys has a wide variety of courses that specializes in learning Python, Data Science, and Artificial Intelligence. You will be given all the key resources to jump-start your career in the field. If you want to learn from the best in the field, I would recommend H2kinfosys particularly given that their instructors ride on years of field experience.

Wrapping up,

Let’s talk about mentorship. Mentorship plays a fundamental role in skyrocketing your progress in Data Science. You can approach folks you find fascinated on social media and send them a friendly text, stating that you’d like to be under their tutelage. Be polite, yet confident when addressing them. It is a good idea to highlight some of their previous works that jumped out at you and how you want to give it a shot. You could also get mentorship from the experienced instructors at H2kinfosys once enroll in their class.

Finally, learning data science can be a daunting task as there are a lot of things to cover. However, following this guide would help you cushion the overwhelmingness of its demands and prepare you for what you should expect going forward. Be resolute. Be consistent. Start now.

#Data science online training

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