75% increase in AI and Machine Learning jobs in the short span of four years bears testimony to the urgent requirement for employees possessing Machine Learning skills. The industry beckons candidates with problem-solving skills, promising them career growth and lucrative pay. You, too, can pursue a career in ML by gaining Machine Learning knowledge and hands-on experience.
All you need is to choose the right course to scale your journey from novice to expert. Dive in to learn the tips and best machine learning courses to kickstart today!
Key Concepts and Terminologies in Machine Learning
Supervised Learning: It uses labeled data to train algorithms for data classification and outcome prediction.
Unsupervised Learning: It predicts the patterns and relationships in the data.
Reinforcement Learning: It uses trial and error methods, grouping, clustering, and dimensionality reduction in a dynamic environment.
Self-supervised Learning: It is capable of self-training to learn one part of input from another part of the input.
Classification: It is part of supervised Machine Learning that builds the model to separate data into distinct or discrete classes and predicts the correct label of given input data.
Regression: It is also a part of the supervised ML technique that predicts continuous values.
Clustering: An unsupervised Machine Learning method, it groups the unlabelled dataset based on the presence and absence of similar patterns.
Association: It checks the interdependency of the data and finds an association among the variables of the dataset.
Decision Trees: It is a tree-like structure suited for modeling and outcome prediction according to input data.
Support Vector Machines: It is used for classification and regression problems and aims to segregate n-dimensional space into classes for easy categorization.
Neural Networks: It uses interconnected nodes or neurons for data processing. It is efficient due to learning from mistakes and continuous improvements.
Bayesian: It is used to determine the probability of occurrence of an event if one event has already occurred. It is also effective in relating the condition and marginal probability.
Prerequisites for Learning Machine Learning
Prior information or possession of certain skills contributes to easing the journey of Machine Learning. Here is a list of prerequisites which are not necessary but can be beneficial if incorporated:
Statistics
It is associated with data collection, analysis, presentation, sorting, and interpretation. Playing a crucial role in Machine Learning, two types of statistical knowledge are required. Descriptive statistics describes or summarizes a specific dataset, while inferential statistics summarizes a sample rather than a data set.
Probability
It is used for the prediction of the occurrence of an event and decision-making. The important concepts required here are independence, notation, different rules of probability, continuous random variables, and probability distribution.
Programming languages
Used to execute Machine Learning algorithms, the knowledge or familiarity eases the path to learning ML. Experience with at least one programming language is desirable in jobs as well. Hence, make sure to put effort into learning it. Choose any among Python, R, Julia, C, etc.
Linear Algebra
The concepts of linear algebra help in understanding the algorithmic functionality and participate in decision-making. The important topics to be familiar with here are linear transforms, algorithms in code, tensors, and matrix multiplication.
Calculus
It is important for the optimization of algorithms, regularization, and model training. It also improves the efficiency of models. Concepts of significance in calculus are partial derivatives, chain rule for training neural networks, basic knowledge of integration and differentiation, and gradient or slope.
How to Select the Right ML Course?
Select the best Machine Learning courses after reviewing the following points:
Identify your Goals
Research different options available in machine learning and choose the field that interests you most. You will need it to determine which courses align with the demand of the industry or specialization of your interest.
Assess Yourself
Evaluate yourself in possession of prerequisites of the field of your interest. The goal is to choose the course that fills your knowledge gap.
Review the Course and Course Provider
Make sure you choose from a reliable course provider whose certifications are recognized globally. It should also provide education from experts. The course must provide a flexible schedule with clarity on the benefits and curriculum to be covered.
Projects
Always choose the course that provides hands-on experience through significant projects. It should also cover the latest tools and technologies.
6 Best Machine Learning Courses
Here we provide the list of best Machine Learning courses:
1. Post Graduate Program in AI and Machine Learning
Simplilearn, Purdue University, and IBM offer a program where various hands-on projects and elaborate curricula via online boot camps. The duration of the program is 11 months. The program advisor and program trainers are experienced personnel with more than 20 years of experience.
Features:
- Certification of completion from Simplilearn and Purdue University
- Live online master calls and interaction sessions on important topics
- Exposure to prominent tools like OpenAI, ChatGPT, Dall-E, Midjourney
- Access to Purdue’s alumni association membership on program completion
- The core curriculum covered on live courses by industry experts
- 3 capstones and 25+ hands-on projects from various industry domains
- Simpliearn’s Job Assist increased job chances
- Exclusive hackathons and AMA sessions by IBM
Skills Covered:
- Generative, conversational, and Explainable AI
- Deep Learning
- Computer Vision
- Prompt Engineering
- ChatGPT
- Supervised, Unsupervised, and Reinforcement learning
- Speech recognition
- Machine Learning Algorithms
- Natural Language Processing
- Ensemble methods
- Model evaluation and validation
- Large Language Models
2. Professional Certificate Program in Generative AI and Machine Learning
Provided by the iHUB DivyaSampark, the technology innovation hub at IIT Roorkee, the program is the source of the certificate of the prestigious institute of India. It is at the top among the best machine learning courses. Here, you can also earn certifications for IBM courses and attend their masterclasses.
Features:
- Curriculum completed in an online live classroom
- Masterclass from IIT and NIT experts
- Exposure to the latest AI advancements like prompt engineering, generative AI, and ChatGPT
- Opportunity to attend a two-day campus immersion program by iHUB divyaSampark at IIT Roorkee
- Access to Simplilearn’s JobAssist
- Access to hackathons and industry masterclasses by IBM experts
- Live project-led training for crucial topics in generative AI
- Opportunity to build expertise in 20+ tools and techniques along with access to integrated labs
Skills Covered:
- Generative AI, its models, architectures, and Explainable AI
- ChatGPT
- Prompt engineering
- Machine learning algorithms
- Model training and optimization
- Model evaluation and validation
- Supervise, unsupervised and reinforcement learning
- Speech recognition
- Computer vision
- Deep learning
- Natural Language Processing
- Ensemble methods
- Large language models
3. AI & Machine Learning Bootcamp
Are you looking for a degree from Caltech University? The dreams are easy to fulfill now with self-paced learning opportunities with a certificate from Simplilearn and Caltech. Get to learn from 25+ hands-on projects and more than 20 tools. The candidates who took this course have been placed at Google, Amazon, IBM, Apple, Adobe, and other companies.
Features:
- Chance for exposure to the latest AI trends
- Get CTME Circle Membership and participate in Caltech’s campus immersion initiative
- Earn 22 CEU credits from Caltech CTME
- Bootcamp completion certification from Caltech CTME
Skills Covered:
- Generative, conversational, and explainable AI
- Prompt engineering
- ChatGPT
- Model training, optimization, evaluation, and validation
- Ensemble methods
- Deep learning
- Natural Language Processing
- Speech recognition
- Supervised, unsupervised, and reinforcement learning
- Statistics
- Machine learning algorithms
- Large language models
4. Professional Certificate Course in AI and Machine Learning
This Machine Learning course is taught online by the faculty of IIT Kanpur. Covering all the important concepts, the course takers are currently placed in multinational companies like Deloitte, LinkedIn, Microsoft, Amazon, Netflix, and others. The course certificate is provided within 90 days of completion.
Features:
- Certificate of E&ICT academy, IIT Kanpur
- Masterclasses from IIT Kanpur faculty
- 25+ hands-on projects, wide arrays of AI tools and technologies, and Capstone project in 3 domains
- Live interaction sessions and applications of the latest AI trends
- Access to Simplilearn’s JobAssist for placement
Skills Covered:
- Statistics
- Generative and Explainable AI
- Machine Learning algorithms
- Prompt engineering
- ChatGPT
- Model training, evaluation, validation, and optimization
- Deep learning
- Natural Language Processing
- Ensemble methods
- Computer vision
- Speech recognition
5. AI and Machine Learning Bootcamp - UT Dallas
This program is most suitable for students looking for quality education from the faculty of UT Dallas Erik Jonsson School of Engineering and Computer Science. It offers a self-paced learning program while covering tools like Matplotlib, TensorFlow, Django, DALLE.2, and much more. The placements from the program have been in Microsoft, American Express, Accenture, Mastercard, Netflix, and so on.
Features:
- Industry-relevant experience with 25+ hands-on projects
- Seamless access to integrated labs
- Hands-on training on 20+ tools of current relevance
- Career assistance services and the opportunity to build a professional profile
- Exposure to the latest AI trends
Skills Covered:
- Generative, conversational, and explainable AI
- Computer vision
- Statistics
- Prompt engineering
- Model training, optimization, evaluation, and validation
- Ensemble methods
- Deep learning
- Large language models
- Supervised, unsupervised, and reinforcement learning
- Natural Language Processing
6. Caltech Post Graduate in AI and Machine Learning
Are you interested in another top US institute? Simplilearn provides a Machine Learning course from Caltech. Additionally, the program also offers industrial knowledge and experience from IBM experts. You can join the online boot camp for 11 months to learn recent tools and techniques. The placements from this program have landed the candidates jobs at HSBC, Airbus, JPMorgan Chase, Adobe, and others.
Features:
- Exposure to the latest AI trends
- Hands-on experience through 25+ hands-on projects
- Access to more than 20 tools and integrated lab
- Opportunity to earn 22 CEU credits
- IBM certificates for IBM classes
- Live interactive class on the latest topics by CTME instructors
- Program completion certificate from Caltech CTME
- online convocation with Caltech CTME Executive Director
- Opportunity to benefit from Simplilearn’s Job Assist program
Skills Covered:
- Generative, conversational, and explainable AI
- Supervised, unsupervised, and reinforcement learning
- Computer vision
- Large language models
- Statistics
- Natural Language Processing
- Model training, optimization, evaluation, and validation
- Prompt engineering
- Ensemble methods
- Deep learning
Looking forward to a successful career in AI and Machine learning. Enrol in our Professional Certificate Program in AI and ML in collaboration with Purdue University now.
Conclusion
Machine learning is a booming industry seeking candidates with fresh and innovative minds. Multiple top-notch companies hire candidates with clear concepts and hands-on experience. Gaining Machine Learning certificates from top universities has the potential to accelerate your career. Explore the details of the best machine learning courses shared above and pick the most suitable for your interests, passions, and needs! Are you thinking about leveling up your career? Start with our Post Graduate Program In AI And Machine Learning course.
FAQs
1. Can I learn machine learning in one month?
It takes at least 6 months or more to learn Machine Learning basic concepts. However, the time mainly depends on a person’s prior knowledge and experiences.
2. What is the best course to start machine learning?
The choice of the best Machine Learning courses depends on multiple factors such as goals, previous knowledge, skills, and concepts included in the course, time flexibility, and fees. Check all the criteria and decide accordingly.
