Online Machine Learning Courses: Certification, Subjects, Scope, Salary

Do you want to upskill your skills in machine learning? If yes, there are a number of courses available for you. You can join the Machine Learning courses in campus or online learning mode. But with the increasing population and demand of online training in ML, Many institutes offers you the most demanded machine learning courses, and make your bright career in ML and AI fields.

If you are excited to dive into the details of Machine Learning and equip skills in it, then you should join the online machine learning courses. You will become experience in this domain and make your bright career in Machine Learning field

Online Machine Learning Certificate Courses

how to start machine learning certifications? Machine learning is the famous AI specialization and its demand is increasing quickly. There will be the future world when everything is enabled by AI. So, Machine learning is the best course to make your future in it. You can join the Online Machine Learning Certificate Courses, develop your learning skills through bounds and leaps, and get expert knowledge.

With the best machine learning courses for your career growth, you will get knowledge of important ML concepts, such as supervised learning, ML algorithms, vector machines, and unsupervised learning. You will get placement support after complete your course. You will get opportunity to work on real time projects and assignments after enroll in online ML course. It shapes your career and helps you to become ML engineer and job opportunities in different fields. This course is best fits for students who have dive in Machine learning and related domains. If you want to get systematic formal education and career support, then this is the best course for you. Here are the list of online Machine Learning courses available

Course 

Duration 

Machine Learning in Python Course

12 Hours (Self-Paced) 

Machine Learning Algorithms

2 Hours (Self-Paced)

TensorFlow Python

2 Hours (Self-Paced)

Machine Learning Crash Course

with TensorFlow APIs

15 Hours 

Machine Learning on Google Cloud Specialization

2 Months 

Key Concepts of Machine Learning

There are important machine Learning algorithms used in this field and built your successful career. To get the detailed idea about Machine learning, you should know about its types and algorithms.

Types of Machine Learning

Here are the different types of Machine learning

  • Supervised learning

Supervised learning involves training models on labelled data. It shows input data come from output labels. Examples are regression tasks and classification.

  • Unsupervised Learning

This Machine learning type deals with unlabeled data. It helps you to find intrinsic structures and patterns in input data. Examples are association and clustering.

  • Reinforcement learning

This type of machine learning involves the model to make decisions. You can make sequence of decisions by penalize or rewarding it on basis of actions. It is commonly used in game robotics and playing. You will join the course and get certification at a reasonable Machine Learning course fee with a Certificate.

Key algorithms of Machine learning

Here are the algorithms of Machine learning

  • Linear regression: It is used to predict continuous value
  • Logistic regression: It is used to solve binary classification problems
  • Decision trees: Used for regression and classification levels
  • Support Vector Machines ( SVM): It is used for classification tasks
  • Neural networks: It is used for complex pattern recognition tasks

What Topics Are Covered in Machine Learning Courses?

What topics are typically covered in machine learning courses? Here are the important topics covered in online machine learning courses. Below, we tell you core subject topics with their details

Category

Topics

Details

Introduction to Machine Learning

  • Definition and Scope
  • History and Evolution
  • Types of Machine Learning
  • Understanding ML, its applications, and evolution
  • Supervised, Unsupervised, and Reinforcement Learning

Mathematics and Statistics for Machine Learning

  • Linear Algebra
  • Calculus
  • Probability and Statistics
  • Optimization Techniques
  • eigenvectors, Differentiation, integration, Vectors, matrices, eigenvalues,
  • partial derivatives
  • Probability distributions, hypothesis testing
  • Gradient descent, convex optimization

Data Preprocessing and Exploration

  • Data Cleaning
  • Data Transformation
  • Exploratory Data Analysis (EDA)
  • Handle missing values, outlier detection
  • Normalization, standardization, encoding categorical variables
  • Visualization techniques, feature engineering

Supervised Learning Algorithms

  • Regression Algorithms
  • Classification Algorithms
  • Ensemble Methods
  • Linear regression, logistic regression
  • Decision trees, SVM, KNN, naive Bayes
  • Random forests, GBM, XGBoost

Unsupervised Learning Algorithms

  • Clustering
  • Dimensionality Reduction
  • K-means, hierarchical clustering, DBSCAN
  • PCA, t-SNE

Advanced Topics in Machine Learning

  • Neural Networks and Deep Learning
  • Natural Language Processing (NLP)
  • Reinforcement Learning
  • Neural networks basics, CNNs, RNNs, GANs
  • Text processing, sentiment analysis, topic modelling
  • Q-learning, DQNs, policy gradients

Model Evaluation and Validation

  • Evaluation Metrics
  • Validation Techniques
  • Hyperparameter Tuning
  • Accuracy, precision, recall, F1 score, ROC-AUC
  • Cross-validation, train-test split, overfitting, underfitting
  • Grid search, random search, Bayesian optimization

Practical Implementation

  • Programming Languages and Tools
  • Machine Learning Libraries
  • Deployment of Models
  • Python, R, Jupyter notebooks
  • Scikit-learn, TensorFlow, Keras, PyTorch
  • Model saving and loading, creating APIs, using cloud platforms

Special Topics and Case Studies

  • Ethics in AI
  • Case Studies
  • Capstone Projects
  • Bias in ML, transparency, fairness
  • Real-world applications in healthcare, e-commerce, finance
  • End-to-end projects cover all ML stages

 

Why Learn Machine Learning

Join the best machine learning courses for your career growth is a great decision. The field allows you to develop your data science skills and get career opportunities and programs across various industries. Here are the best reasons to study machine learning

  • High demand and growth

 The demand for AI and machine learning experts is expected to increase by 40% by 2027. As the data era and the need for data-driven decision-making grow throughout industries, the job market for machine learning experts is expanding rapidly.

  • Lucrative salaries

Machine learning jobs are the most-paying in the tech industry. Roles like Data Scientist, AI Engineer, and Machine Learning Engineer get salaries starting from INR 15-25 Lacs. The demand of the machine learning field makes it an attractive and rewarding career.

  • Dynamic world

 Machine learning offers a dynamic world of complex problems to resolve. The field demand for consistent learning and model to new technology and methodologies, making it an exciting pace for problem-solvers and those obsessed with drive the limits of what machines can do.

  • Broad applications

 Machine learning has broad applications in industry, from healthcare and education to finance, advertising and marketing. Learning ML skills can open up opportunities across numerous sectors and enable you to make a bright career.

  • Automation and efficiency

machine learning course for beginners equips them with knowledge and enables them to automate tasks, enhance operations, and gain valuable insights from data. Professionals with ML expertise are precious assets that assisting companies to leverage data to force innovation and growth.

Career and Salary After Machine Learning Engineer

There are the various job opportunities will available for you after completion of online free machine learning courses. You will develop your knowledge and skills and get access to a lot of career opportunities with great salary ranges.

Job Title

Average Annual Salary

Machine Learning Engineer

8-20 LPA

Data Scientist

6-18 LPA

AI/ML Research Scientist

10-25 LPA

Data Analyst

4-10 LPA

Business Intelligence (BI) Developer

5-12 LPA

Big Data Engineer

7-15 LPA

Natural Language Processing (NLP) Engineer

8-18 LPA

Computer Vision Engineer

9-20 LPA

Robotics Engineer

6-15 LPA

AI Product Manager

12-30 LPA

 

Why Should You Choose

Reputation of Excellence:

High academic standards and a dedication to the success of its students are hallmarks of Jain University. Their programs are reliable and highly regarded.

Updated Industry-Focused Curriculum:

The curriculum is created to ensure that students acquire applicable and useful skills in line with the standards and requirements of the modern industry.

Affordable Fee Structure:

A wider range of students can now afford to attend the university thanks to its competitive and reasonable tuition rates.

Recognized Degrees:

Degrees from Jain University are recognized and respected, enhancing graduates' career prospects and opportunities.

Flexible Learning Options:

Students can balance their education with their personal and professional obligations by learning at their own pace and on their own schedule with online programs.

Placement Assistance:

The university provides strong placement support, helping students secure employment and advance in their careers through dedicated placement services.

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