Introduction
The world is fascinated by the capabilities of artificial intelligence (AI). The constant hype surrounding AI and its innovations have generated interest across industries.
Today, AI is considered an asset with its automated solutions for faster outcomes and efficiency in performing complex activities. As artificial intelligence continues to flourish, there is a constant demand for professionals and immense career opportunities.
In recent years, the artificial industry is continuously being adopted for capturing value from its implementation at the enterprise level. Such AI-based adoptions in business have led to tremendous revenue generation at a reduced cost of operations, making AI very popular. According to a report by Grandview Research, the AI industry’s expected growth is USD 737 billion by 2027, roughly a 42% growth rate.
These staggering numbers suggest that the AI industry is evolving and will have constant demand in the next few decades. Therefore, the aspirants and professionals looking to gain entry into artificial intelligence must be equipped with the right skill set. Although several artificial intelligence courses are on the online platform today, it is equally challenging to find the appropriate course among several possibilities.
This article delves deep to list some of the best courses covering all the key topics of AI in-depth and offer an industry-recognized certificate for value addition.
Related Reading:Â Top 9 Machine Learning Courses
1. AI Fundamentals – DataCamp
The course is available on the DataCamp platform. The course is a beginner-oriented course suitable for people who want to learn about AI from the ground up. Although it aims to introduce the fundamentals, there is a strong focus on providing an in-depth understanding of AI’s essentials with sufficient practical exposure to build up the skills.
The course curriculum includes the following set of topics.
- Introduction to AI: General vs. Narrow AI, AI models, Parameters, Machine learning, Digit Recognition, Additional exercises.
- Supervised Learning: Supervised learning fundamentals, Regression and Classification, Training and evaluating classification models, Hold-out, Training and evaluation regression models, Going non-linear.
- Unsupervised Learning: Dimensionality reduction, Principal component analysis, Clustering, Practical exercises, Anomaly detection, the go-to algorithm, Selecting the right model, predicting the customer churn.
- Deep Learning and Beyond: Deep learning introduction, Nucleus, First neural network, Layers, Architecture, Convolutional neural network, One-liner modeling and evaluation, Deep learning for digit recognition.
COURSE DETAILS:
Instructor: Nemanja Radojkovic
Level: Introductory
Video Lectures: 14 videos (Self-paced)
User Review: NA
Price: Free
2. Professional Certificate in Applied AI – IBM – edX
The course is a six months self-paced course offered on the edX platform. This course is among the most trusted AI courses as IBM offers it. The course will provide industry related exposure in hands-on experience and a well-recognized certificate in the IT industry.
The takeaway from the AI training course for the learners is building and training the model with custom image classifiers, hands-on experience using Watson, Python, and OpenCV, creating chatbots, and deploying virtual assistants. Additionally, machine learning, deep learning, and neural network concepts are covered in-depth.
There is also an introductory portion on computer vision and its applications. Finally, the learners can understand how to build a computer vision-based web application and its deployment in the cloud platform. There are use cases that are covered in the course for building a strong knowledge base. The course curriculum includes:
- AI for Everyone: Master the Basics
- Introduction to Watson AI
- AI Chatbots without Programming
- Python Basics for Data Science
- AI Applications with Watson
- Computer Vision Fundamentals with Watson and OpenCV
COURSE DETAILS:
Instructor: Antonio Cangiano, Rav Ahuja, Yi Leng Yao, Sacchit Chadha, Joseph Santarcangelo, Nayef Abou Tayoun
Level: Introductory/ Intermediate
Video Lectures: NA (Self-paced)
User Review: NA
Price: $532 Approximately
3. Artificial Intelligence: The Big Picture of AI – Pluralsight
The AI training course is available on Pluralsight. It is a beginner-oriented course covering the fundamentals of artificial intelligence and the AI-drive technologies available today. The essential concepts of machine learning, deep learning, and reinforcement learning are covered in-depth as well.
Next, the usage of AI tools and the practical implementations, and the impact of AI across the industries are explored to provide a thorough understanding of the AI revolution. The course modules are.
- Introduction
- Artificial Intelligence: Types of AI, Components, Applications, and Summary
- History of AI: Introduction, Classical, The First AI winter, Knowledge-based AI, The Second AI winter, Data-driven AI, Third AI winter
- Modern AI: Machine learning, Deep learning, Reinforcement learning, AI trends, State of the art AI
- AI and IT: Training AI models, Building AI apps, Using AI tools
- Future of AI: AI and society, AI and labor, AI and ethics
- Conclusion
COURSE DETAILS:
Instructor: Matthew Renze
Level: Introductory
Video Lectures: 37 videos (Self-paced)
User Review: 5/5 (on Pluralsight)
Price: Available on Sign-up
4. Become a Deep Reinforcement Learning Expert (Nanodegree Program) – Udacity
The course is offered in collaboration with NVidia, Deep learning institute, and Unity on the Udacity platform. The course is a nano degree program, which is equivalent to a specialization program on the platform.
Some of the essential deep reinforcement learning skills and algorithms are covered that are powering advanced applications, video games, and robotics today. Additionally, the program offers real-world projects, technical mentor support, student community, resume support, GitHub review, and LinkedIn profile optimization.
The course is targeted at advanced learners who have prior knowledge of machine learning and deep learning concepts.
The course contents are:
- Foundations of Reinforcement Learning
- Value-Based Methods
- Policy-Based Methods
- Multi-Agent Reinforcement Learning
COURSE DETAILS:
Instructor: Alexis Cook, Arpan Chakraborty, Mat Leonard, Luis Serrano, Cezanne Camacho, Dana Sheahan, Chhavi Yadav, Juan Delgado, Miguel Morales
Level: Advanced
Video Lectures: NA (14-15 hours per week & total duration of 4 months)
User Review: 4.7/5
Price: $875 Approximately
5. Machine Learning for All – University of London – Coursera
The artificial intelligence classes are focused on beginner-level concepts. It is offered on the Coursera platform. The online course on artificial intelligence is suitable for covering the basic concepts of machine learning, training a machine learning model using a dataset, understanding the concepts of data and its effect on machine learning, and the benefits and dangers of machine learning. The course modules are.
- Machine learning: Introduction, AI, Machine learning and its algorithms, Interview with experts
- Data features: Bytes and numbers, Data types, Data features, Neural networks, Exercises
- Machine learning in practice: Introduction, Testing, Problems with machine learning, Dangers of machine learning, Dataset related exercises
- Machine learning project: Collection of the dataset, Additional information on using the dataset for the project, Building the first machine learning project
COURSE DETAILS:
Instructor: Dr. Marco Gillies
Level: Beginner
Video Lectures: 23 videos (Self-paced)
User Review: 4.7/5 (on Pluralsight)
Price: Free Enrollment (Additional prices may exist for obtaining the certificate)
6. Artificial Intelligence Foundations: Machine Learning – LinkedIn Learning
The course is available on LinkedIn Learning. It covers crucial fundamentals of artificial intelligence and machine learning. The course is beneficial for beginners as the course provides a comprehensive overview of all the essential concepts to master the machine learning techniques and algorithms for classifying images, sounds, and videos.
The types of machine learning and how to select the appropriate algorithm for specific tasks using a large dataset is covered in-depth. The course modules include.
- Introduction
- What is Machine learning? –Â What is learning, working with data, Applying machine learning, Different types of machine learning, Quiz
- Different Ways a Machine learns: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning
- Popular Machine learning algorithms: Problems using machine learning, Decision trees, k-nearest neighbor, K-mean clustering, Regression, Naïve Bayes
- Applying algorithms: Follow the data, fit the data, Select the best algorithm
- Common Challenges
- Conclusion
COURSE DETAILS:
Instructor: Doug Rose
Level: Beginner
Video Lectures: 24 videos (Self-paced)
User Review: NA
Price: Free Enrollment for a month (Charges apply after the first month)
7. Natural Language Processing Specialization – Coursera
The course is available on Coursera, and DeepLearning offers it. AI. It is among the most trusted platforms, and DeepLearning.AI is backed by Andrew Ng, a pioneer in the artificial intelligence industry today.
The specialization covers advanced NLP application building concepts and performing techniques such as sentiment analysis, language translation tools, text summarization, and chatbots.
The essential practical applications that are required to master the advanced concepts are provided with hands-on experience. The course is taught by the deep learning experts from Stanford University and offers an industry-recognized certification.
The essential programming languages and their libraries for building advanced applications are explored in detail and additional course materials to better understand. At the end of the course, the learners will use logistic regression and Naïve Bayes and word vectors for performing sentiment analysis and word translation, recurrent neural networks, LSTM, and GRU in TensorFlow.
Finally, dynamic programming, hidden Markov models for auto-corrections, and encoder and decoders for chatbots. The course curriculum includes.
- Natural language processing with classification and vector spaces: Natural language processing with probabilistic models
- Sequence models
- Attention models
It is important to note that each of the modules has four weeks of the program, and the total duration of the course is four months.
COURSE DETAILS:
Instructor: Younes Bensouda Mourri, Eddy Shyu and Lukasz Kaiser
Level: Intermediate/Advanced
Video Lectures: 24 videos (Self-paced)
User Review: 4/6/5
Price: Free Enrollment (Additional charges may apply for certification)
8. Artificial Intelligence Foundations: Thinking Machines – LinkedIn Learning
The course is among the best choices available for beginner-level artificial intelligence courses. It is available on the LinkedIn Learning platform. Some of the critical concepts focused upon in this AI training course include the essentials of artificial intelligence, the difference between strong and weak AI, different approaches to AI, practical uses of AI technologies, and AI integration with other big data technologies. Another critical aspect covered is how to avoid the common pitfalls associated with AI-related coding for building models. The course modules include.
- Introduction
- What is Artificial Intelligence?
- The Rise of Machine learning: Artificial neural networks, Perceptron
- Finding the right approach: Match patterns, data vs. reasoning, Unsupervised learning, Backpropagation, Regression
- Common AI programs: Robotics, Natural language processing, The Internet of things
- Mixing with other technologies: Big data and Data science
- Avoiding pitfalls
- Conclusion
COURSE DETAILS:
Instructor: Doug Rose
Level: Beginner
Video Lectures: 20 videos (Self-paced)
User Review: NA
Price: Free Enrollment for a month (Charges apply after the first month)
9. Reinforcement Learning Foundations – LinkedIn Learning
The course is offered on the LinkedIn Learning platform. It focuses on introducing the basic concepts to build valuable skills to gain expertise in the reinforcement learning environment.
The primary role of reinforcement learning in AI evolution is when reinforcement learning is used, and the critical algorithms are crucial for reinforcement learning models. The course curriculum includes.
- Introduction
- Getting started with reinforcement learning: Terms in reinforcement learning, Basic problem, Markov decision process, and Basic reinforcement learning solution
- Reinforcement learning algorithms: Monte Carlo method, Temporal difference methods, Other reinforcement learning algorithms
- Monte Carlo method: Setting, Exploration and exploitation, Monte Carlo prediction, First visit and every visit Monte Carlo, Monte Carlo control, Additional modifications
- Temporal difference method: Setting, SARSA, SARSAMAX, Expected SARSA
- Modified Forms of Reinforcement: Deep reinforcement learning, Multi-agent reinforcement learning, Inverse reinforcement learning
- Conclusion
COURSE DETAILS:
Instructor: Khaulat Abdul Hakeem
Level: Beginner/ Intermediate
Video Lectures: 22 videos (Self-paced)
User Review: NA
Price: Free Enrollment for a month (Charges apply after the first month)
10. Artificial Intelligence: Reinforcement Learning in Python – Udemy
The course is offered by one of the highly-rated institutions on Udemy. The course covers essential concepts of reinforcement learning to implement them for advanced AI projects.
At the end of the course, the learners will have a thorough understanding of applying gradient-based machine learning methods for reinforcement learning tasks, understand the relationship between reinforcement learning and psychology, implement 17 types of reinforcement learning algorithms, and understand the necessary technical requirements.
The learners will also develop proficiency in the python programming language while building the reinforcement learning models. The tutorials are beneficial for intermediate and advanced level learners.
The course prerequisite includes calculus and derivatives, probability and Markov models, Numpy, Matplotlib, gradient descent, supervised learning, and good object-oriented programming skills. The course contents are as follows.
- Introduction
- Getting the code
- Return of the multi-armed bandit
- A high-level overview of reinforcement learning
- Markov decision processes
- Dynamic programming
- Monte Carlo
- Temporal difference learning
- Approximation methods
- Stock Trading project with Reinforcement learning
- Setting up the environment
- Extra Python coding help for beginners
- Effective learning strategies for machine learning
- Conclusion
COURSE DETAILS:
Instructor: Lazy Programmer Inc.
Level: Intermediate/ Advanced
Video Lectures: 108 videos (Self-paced)
User Review: 4.6/5
Price: $25 (Varies according to the region)
11. Modern Deep Learning in Python – Udemy
The course is available on the Udemy platform. The artificial intelligence classes are focused on essential deep learning concepts and building libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. The learners will also understand how to train the deep learning models faster using GPU on AWS.
At the end of the course, the learners will have a good understanding of applying backpropagation for training neural networks, understand the building blocks of Theano, basics of TensorFlow, building a neural network using MNIST dataset, implementation of dropout regularization, building a neural network with different libraries, adaptive learning and the understanding of gradient descent. The course modules are.
- Introduction
- Neuron predictions
- Artificial neural network
- Review codes
- Stochastic gradient descent and mini-batch gradient descent
- Momentum and adaptive learning rates
- Choosing hyperparameters
- Grid search and cross-validation
- Weight Initialization
- Vanishing and exploding gradients
- Theano, TensorFlow Keras, PyTorch
- GPU speedup
- Miscellaneous topics
- Modern regularization techniques
- Batch normalization
- Deep Learning review topics
- Environmental setup
- Extra Help for Python for beginners
- Effective learning strategies for machine learning
- Conclusion
COURSE DETAILS:
Instructor: Lazy Programmer Inc.
Level: Intermediate/ Advanced
Video Lectures: 89 videos (Self-paced)
User Review: 4.6/5
Price: $6.2 (Varies according to the region)
12. Deep Learning.AI: TensorFlow Developer Professional Certificate – Coursera
The course is an artificial intelligence program that aims to build knowledge of the programming language and the critical libraries, and the essential AI tools. It is a hands-on professional certificate program that will allow the learners to build scalable AI-powered applications using TensorFlow. The program can also help the learners to prepare for the Google TensorFlow certificate exam.
Additionally, the learners can understand how to deploy the models. By the end of the course, the learners will be equipped to handle the building and training of neural networks using TensorFlow, improve the network’s performance using convolutions to identify real-world images, work with natural language processing systems, and process text, sentences using vectors. The training program is of 4 months in duration. The course curriculum includes.
- Introduction to TensorFlow for artificial intelligence, machine learning, and deep learning
- Convolutional neural networks in TensorFlow
- Natural language processing in TensorFlow
- Sequences, times series, and prediction
COURSE DETAILS:
Instructor: Laurence Moroney
Level: Intermediate
Video Lectures: NA (Self-paced)
User Review: 4.7/5
Price: Free Enrollment (Additional charges may apply for certification)
13. IBM AI Engineering Professional Certificate – Coursera
The course is available on the Coursera platform in collaboration with IBM. The course is designed to cover the critical components of machine learning techniques and deep learning and the tools necessary to succeed as an AI engineer. The learners will master the fundamentals of supervised and unsupervised learning using the programming languages with Python’s likes.
The popular libraries for building AI models such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow are covered in-depth. Some of the targeted problems in this course include object recognition, computer vision, image and video processing, text analytics, and natural language processing.
The learners will also understand how to build recommender systems and the relevant classifiers. The learners will work on projects that will require incorporating data science skills to scale machine learning algorithms on big data using Apache Spark for the hands-on experience.
The projects will also allow the learners to build, train, and deploy different types of deep learning architectures, including CNN, RNN, and autoencoders. After the course, there will be a digital badge from IBM that recognizes AI engineering proficiency. There will be an additional course completion certificate provided jointly by Coursera and IBM. The entire duration of the course is eight months. The course curriculum includes.
- Machine Learning with Python
- Scalable machine learning on big data using Apache Spark
- Introduction to Deep learning and neural networks with Keras
- Deep neural networks with PyTorch
- Building Deep learning models with TensorFlow
- AI Capstone project with deep learning
Instructor: Saeed Aghabozorgi, Joseph Santarcangelo, Romeo Kienzler, Alex Aklson, Samaya Madhavan, Jeremy Nilmeier
Level: Intermediate
Video Lectures: NA (Self-paced)
User Review: 4.4/5
Price: Free Enrollment (Additional charges may apply for certification)
Conclusion
It is the artificial intelligence era that is evolving how organizations approach their business operations. With rapid development in this industry, the job market is filled with numerous career opportunities.
As per Glassdoor reports, an artificial intelligence engineer’s average base salary is $138,000, and it can go as high as $181,000. The report also suggested very high confidence, which signifies that the statistics are based on real employees working in the industry who participated in the survey.
Therefore, there is tremendous job satisfaction from the entry-level positions to the most experienced positions. With constant development in the industry, it is expected that the AI industry will continue to produce challenging roles across industries in the next decade.
The growth of AI engineers is significantly higher than that of other IT-based roles. Hence it is the opportune moment for the aspirants to deep dive into this field and maximizes their career growth. As per 2020 LinkedIn reports, AI engineer job roles are featured at the top of the list of emerging job roles. Thus, it is assumed that the job market in the AI industry is future proof.
There are some critical aspects that aspirants must look to build while looking for an entry into this market. The role of AI engineers is challenging and highly competitive. Therefore any person looking to grab a lucrative job offer in this industry must be equipped with theoretical and hands-on experience.
The online platform is a convenient way to upskill, and the added benefits include the possibility of acquiring a certificate from top academic institutions and industry professionals from the comfort of the home. However, there are many online market offerings with several competitors, but all courses do not meet the desired expectations.
Thus, the article delved into fetching some of the best possible offerings in the market today and provided the list of courses that can ensure learners embark on an extraordinary career as AI engineers.
