Machine learning is a sub-field of artificial intelligence and computer science that focuses on using data and algorithms to enable Artificial intelligence to imitate the way humans interact. Over the years, machine learning has garnered massive traction from the worldwide business community. It has been evident that ML has brought astounding automation to business processes and offers the highest convenience like never before. Seasoned AI professionals are made to master these nuances to enter the AI business world that works vastly with data and automation technology. Built In expects an average experienced ML engineer with a few years of experience can take home a salary of USD 179,121 annually. Whether you or your mates wish to plan a career in machine learning; you are at the right spot. This could be just the beginning to design a wholesome career path for yourself.
| Machine Learning | Artificial Intelligence | Deep Learning |
| A subset of AI, ML involves algorithms that enable computers to learn from and make data-based decisions | A broader concept of a machine capable of performing tasks in a way that would act just like humans and act smart | A further subset of Machine Learning that uses neural networks with numerous layers to analyze data patterns, mimicking human action |
3 Pillars of Machine Learning
| Parameters | Supervised ML | Unsupervised ML | Reinforcement ML |
| Meaning | Machine learns by using labeled data | The machine is trained using unlabeled data without guidance | Involves agent interaction; learns by errors and rewards |
| Data Type | Labeled | Unlabeled | No predefined data |
| Problem types | Regression and classification | Association and clustering | Reward and error-based |
| Supervision | External | No | No |
| Algorithms | Linear and logistic regression, naïve bayes, decision trees | K-means clustering, KNN, Principle component analysis, neural networks | Monte Carlo, Q-learning, SARSA |
| Goal | Calculate outcomes | Discover underlying patterns | Learn a series of action |
| Application | Risk evaluation, Forecast sales | Recommendation system, Anomaly detection | Self-driving cars, gaming, healthcare |
5 Compelling Reasons to Master Machine Learning in 2024:
- ML brings better career opportunities as it is a growing field of work
- ML offers a staggering opportunity to earn a big salary
- ML is experiencing massive demand for ML professionals
- ML levels up your expertise in the industry, being an intellectually challenging
- ML is pivotal in better understanding of consumer experience
Machine Learning- Recent Statistics
World Economic Forum predicts a massive growth rate to be experienced in the field of machine learning through 2027
Source: ZipRecruiter
ZipRecruiter’s latest statistics revealed in March 2024 highlights a handsome salary for qualified ML engineers and a yearly remuneration pattern like never before.
- DemandSage.com states:
- There will be a 22% surge in employment of machine learning engineers every year from 2023 to 2030
- 57% of companies use machine learning to enhance customer experience
- 80% of businesses reported that investing in machine learning increased their revenue manifold
- The market size of ML is forecasted to reach USD 204.30 billion by 2024 end
How to get started in Machine Learning?
- Believe in yourself
Evaluate your present skills and your future career goal. A closer understanding of your liking and choices shall lead you to your dream career sooner. Get started with the basic STEM education to get into the ML field.
- Pick a Specialization
Use a systematic procedure to work through your problems and build a specialist domain over time with the best ML skills in place.
- Pick a Tool
Select a tool for your level and map it to your process. Delve into a broader realm for beginners/ intermediate/ or advanced level tools to master.
- Get Certified
Get your hands on the best machine learning certifications to guard you against any competitor and flaunt your hard-earned AI ML skills to your future recruiter.
- Practice the process
Build inquisitive ML models, select datasets to work on, and practice the procedure.
- Build a Portfolio
Gather results and demonstrate your core AI ML skills to your preferred industry recruiter to land your dream job in no time.
Requisite areas of expertise:
| Foundation | Beginner | Intermediate | Advanced |
| Probability Statistical methods Linear algebra Optimization Calculus | Python ML algorithms Weka (no code) Python (Scikit-learn) R (caret) Time series forecasting Data Preparation | Code ML algorithms XGBoost Algorithm Imbalanced classification Deep learning (Keras) Deep learning (PyTorch) OpenCV Enhanced Deep learning Ensemble learning | Long-term, short-term memory Natural language (text) Computer vision CNN/LSTM Time series GANs Attention and transformers |
Final word:
Announcing Machine learning to be the top-rated career field among the hottest opportunities today shall not be an exaggeration. You have to believe us when we say that machine-learning engineers are ranked among the highest-paid technical professionals of all time today. Gain machine learning mastery from the leading and the most trusted names to build a lasting career trajectory.
