Introduction
Machine learning is the latest buzzword in the technical sphere. Artificial intelligence is fascinating, and the sub-fields such as machine learning and deep learning are predominant technologies behind the innovations today.
As defined by the tech giants-
“Machine learning is a method of data analysis that automates analytical model building. 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”. –SAS
“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” – NvidiaÂ
The AI industry’s growth is accelerating at an unprecedented rate, surpassing significant businesses in the market today. According to a report for industrial growth trends by Gartner, suggests that in 2021, AI will create $2.9 trillion of business value and 6.2 billion hours’ worth of productivity for work across the globe.
While LinkedIn reported AI jobs as the top emerging job in 2020, whereas Grandview research has stated an expected growth rate of 42.2% from 2020 to 2027.
It is evident that the AI industry prospects as machine learning experts have a high demand and are rightfully considered future-proof jobs in the job market.
Professionals need to shift their careers into the AI industry and aspirants to be equipped with the right skills to be relevant in this highly competitive industry. With that in mind, the article looks to delve deep and list some of the top courses available for machine learning.
Related reading: Top 15 Online Courses to Learn Python
9 Best Machine Learning Courses Worth Taking
1. Machine Learning Fundamentals UCSan Diego – edX
The course is offered on the edX platform. Among the machine learning online courses for beginners, this course stands out. Although the course is a part of the micro master’s program by the same institute, this course can be enrolled separately. The course focuses on a variety of machine learning techniques and the theory behind the algorithms.
With a prime focus on case studies, the learners will be introduced to different descriptive and predictive models. The concepts are taught with hands-on experience using Python, which is among the most popular programming languages for machine learning tasks. Some of the core concepts that are covered in this course are as follows.
- Classification
- Regression
- Conditional Probability Estimation
- Generative and discriminative Models
- Linear Models and Extensions to Nonlinearity using Kernel Methods
- Supervised and Unsupervised Algorithms
- Ensemble Methods: Boosting, Bagging, and Random Forests
- Representation Learning: Clustering, Dimensionality Reduction, Autoencoders, and Deep Nets
COURSE DETAILS:
Instructor: Sanjoy Dasgupta (Professor of CSE, UCSan Diego)
Level: Introductory
Video Lectures: NA (10 Weeks Duration)
User Review: NA
Price: Free (Added Certificate Value: $350 Approximately)
2. Become a Machine Learning Engineer for Microsoft Azure – Udacity
The course is available on Udacity in collaboration with Kaggle and Amazon AWS. The course’s focus is to introduce the advanced machine learning techniques and algorithms and the packages and the deployment of the models in a production environment.
By the end of the course, the learners can gain a significant amount of practical experience with Amazon SageMaker for deploying trained machine learning models to a web application and learn how to conduct a performance evaluation of the concerned models.
Additionally, A/B test models and the process to update models for gathering data is covered as well. The course’s additional benefits include real-world projects from industry experts, technical mentoring support, and career-oriented services. The course is suitable for people who have prior knowledge of machine learning algorithms.
The course modules are.
- Software Engineering Fundamentals
- Machine Learning in Production
- Machine Learning Case Studies
- Machine Learning Capstone
COURSE DETAILS:
Instructor: Cezanne Camacho, Mat Leonard, Luis Serrano, Dan Romuald Mbanga, Jennifer Staab, Sean Carrell, Josh Bernhard, Jay Alammar, Andrew Paster
Level: Intermediate/ Advanced
Video Lectures: NA (3 Months Duration)
User Review: 4.85/5
Price: $657 Approximately
3. Deep Learning Nanodegree Program – Udacity
The course is offered on the Udacity platform in collaboration with Amazon AWS and Facebook Artificial Intelligence. The course touches upon some of the vital neural network concepts of machine learning. However, the course emphasizes deep learning concepts. It is equivalent to a specialization that is offered on some of the deep learning online courses.
The learners will master the concepts of neural networks and implement deep learning frameworks using PyTorch. The essential concepts on convolutional neural networks for image recognition, recurrent neural networks for sequence generation, and generative adversarial networks for image generation are covered in-depth.
Finally, the students can expect to be experts in the deployment of the models that are accessible from a website as well. Some prerequisite courses require the learners to have intermediate experience with Python, and basic knowledge of machine learning is beneficial but not mandatory.
Apart from the Python requirements, the course is also beneficial for beginners. The course also provides exposure to real-world projects from industry experts and technical mentoring, and career-oriented services. The course curriculum includes the following.
- Introduction
- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Generative Adversarial Networks
- Deploying a Sentiment Analysis Model
Related Reading: Udacity Review
COURSE DETAILS:
Instructor: Cezanne Camacho, Mat Leonard, Luis Serrano, Alexis Cook, Jennifer Staab, Sean Carrell, Jay Alammar, Ortal Arel
Level: Intermediate
Video Lectures: NA (4 Months Duration)
User Review: 4.85/5
Price: $876 Approximately
4. Complete Machine Learning and Data Science Bootcamp 2022 – Udemy
The course is ideal for learners that are looking to gain exposure to machine learning from scratch. It is a recent course. Therefore all the latest versions and updated content with the latest developments in the current artificial intelligence industry are included.
The course is available on Udemy. It is a comprehensive course covering the essential concepts of machine learning and data science, and real-world projects. The learners can expect a lot of practical examples for building upon their hands-on skills. All the access to the codes, workbooks, and templates are provided.
Additionally, there is extra content on Python from scratch that is included along with this course. Industry experts teach the course with the experience of working for tech giants. Therefore quality content can be expected as well.
The learners can gain a deeper understanding of deep learning and transfer learning using Tensorflow 2.0, build on project management skills for data science, implementation of machine learning algorithms, modern tools, selection of the right machine learning model, and essential concepts of Python and its libraries for performing machine learning tasks efficiently. The course modules include.
- Introduction
- Machine Learning 101
- Machine Learning and Data Science Framework
- The Two Paths
- Data Science Environmental Setup
- Pandas: Data Analysis
- NumPy
- Matplotlib: Plotting and Data Visualization
- Scikit-Learn: Creating Machine Learning Models
- Supervised Learning
- Milestone Projects
- Data Engineering
- Neural Networks
- Storytelling and Communication for Machine Learning Work
- Career Advice
- Python Part Two
- Advanced Statistics and Mathematics
- Where to Go from Here?
- Bonus
COURSE DETAILS:
Instructor: Andrei Neagoie and Daniel Bourke
Level: Beginner
Video Lectures: 21 Sections and 372 Video Lectures
User Review: 4.6/5
Price: $6.23 Approximately (Varies according to region)
5. IBM Machine Learning Professional Certificate – Coursera
The professional certificate program on machine learning is offered on Coursera in collaboration with IBM. The course focuses on leveraging machine learning’s primary topics, such as supervised and unsupervised learning, deep learning, and reinforcement learning. There are additional topics on time series analysis and survival analysis.
The course comprises six modules that provide a solid theoretical understanding with the practical experience of the main algorithms’ hands-on experience. The course has a follow-along approach that will enable learners to code their projects eventually with relevant open-source frameworks and libraries.
Along with the certificate, the learners will also be provided with a digital badge from IBM to recognize the learners’ proficiency in machine learning. The course modules include the following.
- Exploratory Data Analysis for Machine Learning
- Supervised Learning: Regression
- Supervised Learning: Classification
- Unsupervised Learning
- Deep Learning and Reinforcement Learning
- Specialized Models: Time Series and Survival Analysis
Related reading: Coursera Review
COURSE DETAILS:
Instructor: Mark J Grover (IBM Data & AI Learning, Digital Content Delivery Lead)
Level: Intermediate
Video Lectures: NA (6 Months Duration)
User Review: 4.7/5
Price: Free Enrollment (Additional charges may be applicable for certification)
6. Deep Learning Specialization – Deeplearning.ai – Coursera
The course is offered by deeplearning.ai on the Coursera platform. It is taught by Andrew Ng, who is one of the pioneers in the field of artificial intelligence today. The course is ideal for learning the foundations of deep learning, understanding how to build neural networks, and the idea to lead a successful machine learning project.
Additionally, the learners will be introduced to the convolutional neural network concepts, RNN, LSTM, Adam, Dropout, BatchNorm, Xavier, and He Initialization topics in the deep learning domain.
There are case studies for learners on various topics such as healthcare, autonomous driving, sign language reading, music generation, and natural language processing.
Along with the theory, the course has a strong emphasis on building the learners’ practical knowledge using Python and TensorFlow. Additional contents include career advice from top leaders in the deep learning industry. The course curriculum is listed in the following section.
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
COURSE DETAILS:
Instructor: Andrew Ng, Younes Bensouda Mourri and Kian Katanforoosh
Level: Intermediate
Video Lectures: NA (4 Months Duration)
User Review: 4.8/5
Price: Free Enrollment (Additional charges may be applicable for certification)
7. Production Machine Learning Systems – Google Cloud – Pluralsight
The course is offered on the Pluralsight platform. It is in collaboration with Google Cloud. The course aims to provide the components and best practices required to achieve a high-performing machine learning system in a production environment. The program is essential for advanced learners looking to level up their skills to tackle a challenging production environment for building advanced machine learning models. The course modules are.
- Introduction
- The Components of an ML System
- Data Analysis and Validation
- Data Transformation and Trainer
- System Tuner, Model Evaluation and Validation
- Serving
- Orchestration and Workflow
- Integrated Frontend and Storage
- Training Design Decisions
- Serving Design Decisions
- Lab Introduction: Serving on Google Cloud AI Platform
- Designing from Scratch
- Ingesting Data for Cloud-Based Analytics and ML: Introduction, Data On-Premise, Large Datasets, Data on Other Clouds, Existing Databases, Load Data into BigQuery, ETL Pipelines into GCP
- Designing Adaptable ML Systems: Introduction. Adapting to Data, Changing Distributions, Decisions, System Failure, Mitigating Training, Predictions in Batch and Real-Time, Debugging a Production Model and Summary
- Designing High-Performance ML Systems: Introduction, Training, Prediction, Distributed Training, and its Architectures, Faster Input Pipelines, Native TensorFlow Operations, TensorFlow Records, Parallel Pipelines, Data Parallelism, Parameter Server Approach, and Inference
- Hybrid ML Systems: Introduction, Machine Learning on Hybrid Cloud, KubeFlow, KubeFlow End to End, Embedded Models, TensorFlow Lite, Optimizing for Mobile, Summary
- Course Summary
COURSE DETAILS:
Instructor: Google Cloud
Level: Advanced
Video Lectures: 57 Video Lectures
User Review: NA
Price: Free 10-Day Trial (Fee Available on Sign Up)
8. Machine Learning – Columbia University – edX
The course is offered on the edX platform. The program is part of the artificial intelligence micro masters that are provided by the same institution. However, the learners can enroll in this course separately. The course aims to introduce the essentials of machine learning, the relevant models and methods, and applying them in a real-world scenario.
The course explores the concepts of probabilistic versus non-probabilistic modeling, supervised and unsupervised learning, classification, regression, and sequential models.
Some of the methods that are going to be covered in this course include linear and logistic regression, support vector machines, tree classifiers, boosting, k-means, maximum likelihood, to name a few. The course modules include.
- Week 1: Maximum likelihood estimation, linear regression, and least squares
- Week 2: Ride regression, Bias-Variance, Bayes rule, Maximum a posteriori inference
- Week 3: Bayesian linear regression, Sparsity, Subset selection for linear regression
- Week 4: Nearest neighbor classification, Bayes classifiers, Linear classifiers, Perceptron
- Week 5:Â Logistic regression, Laplace approximation, Kernel methods, Gaussian processes
- Week 6: Maximum margin, Support vector machines, Decision trees, Random Forests, Boosting
- Week 7: Clustering, k-means, EM algorithm, Missing data
- Week 8: Mixtures of Gaussians, Matrix factorization
- Week 9: Non-negative matrix factorization, Latent factor models, PCA, and Variations
- Week 10: Markov models, Hidden Markov models
- Week 11: Continuous state-space models, Association analysis
- Week 12: Model selection and Next steps
COURSE DETAILS:
Instructor: John Paisley (Department of Electrical Engineering, Columbia University)
Level: Advanced
Video Lectures: NA (12 Weeks Duration)
User Review: NA
Price: Free (Added Certificate Value: $250 Approximately)
9. Deep Learning (with Keras and TensorFlow) Certification Training – Simplilearn
The course is offered on the Simplilearn platform. It is in collaboration with IBM. The course aims to make learners familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and several other crucial concepts in the deep learning domain.
At the end of the course, the learners will build deep learning models, interpret and validate the results, and develop deep learning projects efficiently.
The course also provides real-life industry-based projects and dedicated mentoring sessions from industry experts. Additional topics covered in the course include Keras and TensorFlow frameworks, PyTorch and its elements, and image classification.
The prerequisites for the course are familiarity with programming fundamentals, a basic understanding of mathematics and statistics, and basic machine learning concepts. The course curriculum includes the following.
- Deep Learning with TensorFlow: Introduction and Objectives
- Introduction to TensorFlow: TensorFlow Hello World, Linear Regression with TensorFlow, Logistic Regression, Activation Functions, Introduction to Deep Learning, Deep Neural Networks
- Convolutional Networks: Objectives, Introduction, CNN Classification, CNN Architecture, Understanding Convolutions, CNN with MNIST Dataset
- Recurrent Neural Network: Objectives, Sequential Problem, RNN Model, LSTM, RNN for Language Modeling, LSTM Basics, Data Classification with RNN and LSTM, Character Modeling
- Restricted Boltzmann Machines: Objectives, RBMs, Training RBMs, RBM MNSIT, Collaborative Filtering with RBM
- Autoencoders: Objectives, Autoencoders Introduction, RNN for Language Modeling, DB MNSIT
- Live Classes: AI and Deep Learning Introduction, Artificial Neural Network, Deep Neural Network Tools, Optimization, Tuning and Interpretability, Convolutional Neural Network, Recurrent Neural Network, Autoencoders
- Projects
- Math Refresher
- Certificate Unlock: IBM Certification
COURSE DETAILS:
Instructor: NA
Level: Advanced
Video Lectures: NA (34 Hours Blended Learning)
User Review: 4.6/5
Price: $273 Approximately
An Overview of Machine Learning
Machine learning is a subfield of artificial intelligence. Machine learning applications are computationally equipped to learn from the data and experience to improve the decision-making accuracy and predictions.
Today, machine learning applications are scattered across industries that range from digital assistants, search engines, voice commands, language detection, health predictions, to name a few. Ideally, machine learning involves four primary and essential steps, namely
- Selection of data
- Prepare the dataset for training by splitting it into test and training data.
- Choosing the appropriate algorithm as per the problem at hand.
- Training the algorithm
- Model building
- Validation of results and improving the model.
Types of Algorithms as per the Data Type
Labeled Data: Regression, Decision Trees and Instance-Based algorithms
Unlabeled Data: Clustering, Association, and Neural Networks
Difference between Machine Learning and Deep Learning
Manual Intervention
The key difference between machine learning and deep learning is the manual intervention. With machine learning applications, there is a need for manually feeding the required features in the data for it to perform the task. On the other hand, deep learning applications can learn the features to feed them separately.
Hardware Type
The data needed for training and processing the tasks for deep learning is enormous. As the deep learning systems can identify the features themselves, the need for training such systems to perform complex operations are enormous data sets.
Therefore, using such data requires a tremendous amount of processing power. The most common type of hardware for deep learning tasks are graphical processing units (GPU). On the other hand, machine learning can operate on low-powered machines instead of deep learning GPUs.
Processing Time
Although there are extremely powerful GPUs available today, deep learning tasks involve millions of parameters being trained at once. Hence the processing time is more than that of machine learning tasks.
Problem-Solving Approach
While machine learning problems look into a specific task in parts, deep learning problems are handled by the systems entirely at once.
For example, for image recognition problems, the typical machine learning approach will detect the object in the image, followed by object recognition.
In contrast, a deep learning application will identify the image object and its area in the image in a single process due to the deep learning being trained with the dataset.
Applications
The machine learning problems are synonymous with email spam identification, prediction of price, and predictive-based healthcare problems.
Besides, serious learning problems are targeted at complex problems related to an autonomous vehicle, driving assistance and navigation, detection of tumor regions in healthcare images, facial recognition, and virtual assistants.
Conclusion
It is an exciting and profitable time for aspirants and professionals to dive into the field of artificial intelligence. A continually evolving field promises lucrative career opportunities and tremendous job satisfaction as the jobs are challenging and innovative. As per salary trends, machine learning engineers earn as high as $172,407 for the most experienced professionals.
The growth rate of engineers in the industry is phenomenal and ensures a much higher scope than any other field of work currently.
Hence, in their recent reports, LinkedIn has rightfully stated AI engineers as the top trending job in the market today. However, some key points to remember is the need for upgrading the skills as per industry trends.
The job market is competitive and unforgiving. Therefore anyone looking to switch careers has to be aware of the recent market trends in terms of skills and new technologies. It is possible to keep updating the skill repository without hampering the usual time schedules and at one’s own pace with the online courses.
All enthusiasts looking to gain entry into this field must look for appropriate courses that give the right balance of theoretical knowledge and practical experience.
In recent times, searching for the right course with the appropriate curriculum and an industry-recognized certificate is of utmost importance. However, it is not easy to find the right course online as several companies offer similar courses.
Still, all of them do not meet the industry expectations to provide the necessary exposure to the learners.
Thus, the article deep-dived into the findings of the best possible offerings in the market today and provided the list of courses that can ensure learners embark on an extraordinary career path as machine learning engineers.
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