Machine Learning
Benefits of learning Machine Learning
Our Machine Learning certification course offers comprehensive training in machine learning, emphasizing its real-world applications. Our seasoned instructors will lead you through the core concepts of machine learning, covering data preprocessing, model selection, and evaluation.
Throughout the course, you’ll gain practical experience with leading machine learning algorithms and tools, such as TensorFlow, Keras, and sci-kit-learn. Additionally, you’ll learn to develop and deploy machine learning models in cloud environments like Azure.
Designed to equip you with the necessary skills for success across various industries, our training program prepares you for roles in healthcare, finance, and retail. With a solid grounding in machine learning, you’ll be well-equipped to seize the numerous job opportunities in this rapidly expanding field.
Related Job Roles
- Data Engineer
- Data Scientist
- Data Analyst
- Applied Machine Learning Engineer
- Data Science Lead Manager
- Natural Language Processing Scientist
Machine Learning
How to become a machine learning expert?
By applying for this course – it is as simple as that! With this course, you will be able to have a detailed insight into the various ML methodologies and intricacies.
Is it a good decision to pursue a career in machine learning?
Every jaw-dropping technology around you starting from your google assistant to your YouTube video recommendations runs on systems running machine learning frameworks and this is only the beginning. Hence, if you take this course now, chances are really good that you will be at the front and center when ML becomes the norm.
What is the definition of machine learning?
When a system uses Artificial Intelligence to learn from past mistakes and take productive decisions in the present and future without taking any help from programmer-written source code then this is known as Machine Learning.
Do I need to know how to code if I want to learn machine learning?
Yes, you would need to be proficient in programming languages like Python if you want to ace this course.
Training Key Features
Access to state-of-the-art labs
Guaranteed 3 Interview Arrangements
Assured Job Placement
Delivered by Professional Trainers
Skills Covered In Machine Learning
Course Plan
• The emergence of Artificial Intelligence
• Artificial Intelligence in Practice
• Sci-Fi Movies with the concept of AI
• Recommender Systems
• Relationship Between Artificial Intelligence, Machine Learning, and Data Science - Part A
• Relationship Between Artificial Intelligence, Machine Learning, and Data Science - Part B
• Definition and Features of Machine Learning
• Machine Learning Approaches
• Machine Learning Techniques
• Applications of Machine Learning - Part A
• Applications of Machine Learning - Part B
• Key Takeaways
• Data Exploration: Loading Files
• Demo: Importing and Storing Data
• Practice: Automobile Data Exploration I
• Data Exploration Techniques: Part 1
• Data Exploration Techniques: Pa
rt 2
• Seaborn • Demo: Correlation Analysis
• Practice: Automobile Data Exploration II
• Data Wrangling
• Missing Values in a Dataset
• Outlier Values in a Dataset
• Demo: Outlier and Missing Value Treatment
• Practice: Data Exploration III
• Data Manipulation
• Functionalities of Data Object in Python: Part A
• Functionalities of Data Object in Python: Part B
• Different Types of Joins
• Typecasting
• Demo: Labor Hours Comparison
• Practice: Data Manipulation
• Key Takeaways
• Lesson-end project: Storing Test Results
• Supervised Learning
• Supervised Learning- Real-Life Scenario
• Understanding the Algorithm
• Supervised Learning Flow
• Types of Supervised Learning – Part A
• Types of Supervised Learning – Part B
• Types of Classification Algorithms
• Types of Regression Algorithms - Part A
• Regression Use Case
• Accuracy Metrics
• Cost Function
• Evaluating Coefficients
• Demo: Linear Regression
• Practice: Boston Homes I
• Challenges in Prediction
• Types of Regression Algorithms - Part B
• Demo: Bigmart
• Practice: Boston Homes II
• Logistic Regression - Part A
• Logistic Regression - Part B
• Sigmoid Probability
• Accuracy Matrix • Demo: Survival of Titanic Passengers
• Practice: Iris Species
• Key Takeaways
• Lesson-end Project: Health Insurance Cost
• Feature Selection
• Regression
• Factor Analysis
• Factor Analysis Process
• Principal Component Analysis (PCA)
• First Principal Component
• Eigenvalues and PCA
• Demo: Feature Reduction
• Practice: PCA Transformation
• Linear Discriminant Analysis
• Maximum Separable Line
• Find Maximum Separable Line
• Demo: Labeled Feature Reduction
• Practice: LDA Transformation
• Key Takeaways
• Lesson-end Project: Simplifying Cancer Treatment
• Classification: A Supervised Learning Algorithm
• Use Cases
• Classification Algorithms
• Decision Tree Classifier
• Decision Tree: Examples
• Decision Tree Formation
• Learning Objectives
• Choosing the Classifier
• Overfitting of Decision Trees
• Random Forest Classifier- Bagging and Bootstrapping
• Decision Tree and Random Forest Classifier
• Performance Measures: Confusion Matrix
• Performance Measures: Cost Matrix
• Demo: Horse Survival
• Practice: Loan Risk Analysis
• Naive Bayes Classifier
• Steps to Calculate Posterior Probability: Part A
• Steps to Calculate Posterior Probability: Part B
• Support Vector Machines: Linear Separability
• Support Vector Machines: Classification Margin
• Linear SVM: Mathematical Representation
• Non-linear SVMs
• The Kernel Trick
• Demo: Voice Classification
• Practice: College Classification
• Key Takeaways
• Lesson-end Project: Classify Kinematic Data
• Overview
• Example and Applications of Unsupervised Learning
• Clustering
• Hierarchical Clustering
• Hierarchical Clustering: Example
• Demo: Clustering Animals
• Practice: Customer Segmentation
• K-means Clustering
• Optimal Number of Clusters
• Demo: Cluster-Based Incentivization
• Practice: Image Segmentation
• Key Takeaways
• Lesson-end Project: Clustering Image Data
• Overview of Time Series Modeling
• Time Series Pattern Types Part A
• Time Series Pattern Types Part B
• White Noise
• Stationarity
• Removal of Non-Stationarity
• Demo: Air Passengers I
• Practice: Beer Production I
• Time Series Models Part A
• Time Series Models Part B
• Time Series Models Part C
• Steps in Time Series Forecasting
• Demo: Air Passengers II
• Practice: Beer Production II
• Key Takeaways
• Lesson-end Project: IMF Commodity Price Forecast
• Overview
• Ensemble Learning Methods Part A
• Ensemble Learning Methods Part B
• Working of AdaBoost
• AdaBoost Algorithm and Flowchart
• Gradient Boosting
• XGBoost
• XGBoost Parameters Part A
• XGBoost Parameters Part B
• Demo: Pima Indians Diabetes
• Practice: Linearly Separable Species
• Model Selection
• Common Splitting Strategies
• Demo: Cross-Validation
• Practice: Model Selection
• Key Takeaways
• Lesson-end Project: Tuning Classifier Model with XGBoost
• Introduction
• Purposes of Recommender Systems
• Paradigms of Recommender Systems
• Collaborative Filtering Part A
• Collaborative Filtering Part B
• Association Rule Mining
• Association Rule Mining: Market Basket Analysis
• Association Rule Generation: Apriori Algorithm
• Apriori Algorithm Example: Part A
• Apriori Algorithm Example: Part B
• Apriori Algorithm: Rule Selection
• Demo: User-Movie Recommendation Model
• Practice: Movie-Movie recommendation
• Key Takeaways
• Lesson-end Project: Book Rental Recommendation
• Overview of Text Mining
• Significance of Text Mining
• Applications of Text Mining
• Natural Language Toolkit Library
• Text Extraction and Preprocessing: Tokenization
• Text Extraction and Preprocessing: N-grams
• Text Extraction and Preprocessing: Stop Word Removal
• Text Extraction and Preprocessing: Stemming
• Text Extraction and Preprocessing: Lemmatization
• Text Extraction and Preprocessing: POS Tagging
• Text Extraction and Preprocessing: Named Entity Recognition
• NLP Process Workflow
• Demo: Processing Brown Corpus
• Practice: Wiki Corpus
• Structuring Sentences: Syntax
• Rendering Syntax Trees
• Structuring Sentences: Chunking and Chunk Parsing
• NP and VP Chunk and Parser
• Structuring Sentences: Chinking
• Context-Free Grammar (CFG)
• Demo: Twitter Sentiments
• Practice: Airline Sentiment
• Key Takeaways
• Lesson-end Project: FIFA World Cup
Our Hiring Partners
Machine Learning Course Reviews
“Kodeverse Academy’s Machine Learning course is excellent! The curriculum is comprehensive, and the hands-on learning experience is truly valuable.”
“Kodeverse Academy’s Machine Learning course is top-notch! It’s packed with practical knowledge and taught by experienced instructors.”
“Kodeverse Academy’s Machine Learning course is fantastic! It’s helping me grasp complex concepts easily.”
About Machine Learning Program
When you complete the Machine Learning course, you will be awarded with an industry-recognised Machine learning course completion certificate. The document will be valid for the rest of your work life.
Yes, this course comes with a practice test that will help you groom yourself for the real ML certification exam.
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Our Top Instructors
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Career After Machine Learning
If you’re passionate about machine learning and aiming for a career in the field, Azure Machine Learning presents an excellent starting point. As a cloud-based platform, Azure Machine Learning equips you with the necessary tools to develop, train, and deploy machine learning models effortlessly.
As a machine learning practitioner utilizing Azure, you’ll engage in creating intelligent applications capable of data analysis and prediction. Armed with a profound knowledge of machine learning algorithms and tools, you’ll construct models applicable across diverse sectors, spanning healthcare, finance, and e-commerce.
The demand for proficient machine learning practitioners is escalating, with Azure Machine Learning serving as an ideal entry point for budding careers. With ongoing corporate investments in machine learning technology, the landscape promises abundant job opportunities for individuals possessing requisite skills and expertise.
Whether you’re embarking on your career journey or seeking to further your expertise, Azure Machine Learning offers a rewarding and potentially lucrative path. Through appropriate training and hands-on experience, you can emerge as a proficient authority in one of technology’s most dynamic and rapidly evolving domains.
Frequently Asked Questions
By applying for this course – it is as simple as that! With this course, you will be able to have a detailed insight into the various ML methodologies and intricacies.
In case you took online classes for this course then you would need to attend a complete batch of the course and then submit at least one project.
In case you chose self-paced learning then you would need to complete more than 85% of the course curriculum and submit one complete project.
Yes, this course comes with a practice test that will help you groom yourself for the real ML certification exam.
When a system uses Artificial Intelligence to learn from past mistakes and take productive decisions in the present and future without taking any help from programmer-written source code then this is known as Machine Learning.
The examples of real-world applications of Machine Learning are as follows –
- Google’s Assistant in all Android smartphones
- Apple’s Siri
- The video recommendation trick of YouTube
- Driverless cars and many more!
Machine learning is categorised into the following types –
- Supervised Machine Learning
- Unsupervised Machine Learning and
- Reinforcement Machine Learning.
Yes, you would need to be proficient in programming languages like Python if you want to ace this course.
Every jaw-dropping technology around you starting from your Google assistant to your YouTube video recommendations run on systems running machine learning frameworks and this is only the beginning. Hence, if you take this course now, chances are really good that you will be at the front and centre when ML becomes the norm.
They can but it would be difficult. Applicants should have a clear understanding of the sector and should also be proficient in statistics, mathematics and Python programming language.
To learn Machine Learning, applicants would need to be proficient in programming languages like Python, C++, R, Java, and JavaScript.
The age of AI and machine learning is already here. On top of this, the demand for machine learning experts is increasing exponentially but the number of suitable candidates is still low. Hence, get ML certified and bag a high-paying job before the competition becomes cutthroat!
Over the past few years, machine learning has witnessed a whopping seventy-five per cent growth and experts predict that the job vacancies in the sector will be in the millions by the time year 2022 wraps up! Hence, the future of the machine learning job sector is bright, to state the least!
Machine learning experts are called upon when a company needs to build efficient machine learning systems. They are also experts in data processing and analysis. They are also called upon to train nascent ML systems so that the system can start working as it should.
All courses available online & Offline classes are available in Bangalore
It is mentioned under the training options. Online, Offline & self paced learning course fees differs.
Course duration is 2 months or 60 Hrs Usually daily 2 hrs.
Indeed, we furnish a course completion certificate for web design & development. Additionally, we offer another certification known as the ‘Kodeverse Academy Certified Professional in Machine Learning.’ Achieving a score exceeding 80% in the exam entitles you to be recognized as a ‘Kodeverse Academy Certified Professional.’
Yes, we provide you the assured placement. we have a dedicated team for placement assistance.
All our trainers are working professional having more than 6 years of relevant industry experience.
Machine Learning Certification Course Career In 2023
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“Machine learning, a discipline enabling machines to learn and evolve autonomously without explicit programming, has become integral to numerous sectors, spanning finance to healthcare, revolutionizing business operations. With the burgeoning demand for machine learning experts, there’s no better time to venture into this dynamic field.
Our Machine Learning Certification Training Course is meticulously crafted to equip you with the essential skills and knowledge vital for thriving in this dynamic industry. Led by seasoned trainers, the course covers everything from data preparation to model selection and evaluation, ensuring a comprehensive understanding of the machine learning process.
Enrolling in our Machine Learning Certification Training Course offers numerous advantages:
– Enhanced Career Prospects: Anticipated rapid growth in demand for machine learning professionals promises job security and lucrative opportunities.
– Increased Earning Potential: High demand for machine learning experts translates to competitive salaries, providing substantial earning potential.
– Strengthened Problem-Solving Abilities: The course fosters critical thinking, problem-solving, and analytical skills, empowering you to tackle real-world challenges adeptly.
– Fostering Creativity: Encouraging creativity and innovation, our course nurtures the development of innovative solutions to intricate problems.
– Career Progression: Acquiring machine learning proficiency positions you for advancement within your organization or opens doors to new industry opportunities.
As machine learning gains traction for its adeptness in swiftly and accurately analyzing vast data volumes, leading to informed business decisions and superior outcomes, its adoption by companies across diverse sectors is set to escalate. Consequently, the demand for adept professionals will soar.
In 2023, a plethora of job opportunities awaits machine learning professionals, including roles like data scientists, machine learning engineers, and software developers. With companies increasingly embracing artificial intelligence and machine learning technologies, the demand for skilled professionals will continue its upward trajectory. Enroll in our Machine Learning Certification Training Course today to acquire the expertise needed for a thriving career in this dynamic field.”