Apponix Technologies

Machine Learning Certification Course Delivered by Senior Data Scientists with 10 Industry Projects

Overview of Machine Learning Training Course

  • Our Machine Learning certification training program provides comprehensive training in the field of machine learning, with a focus on real-world applications. Our experienced trainers will guide you through the fundamentals of machine learning, including data preprocessing, model selection, and evaluation.
  • During the training, you'll gain hands-on experience with popular machine learning algorithms and tools, including TensorFlow, Keras, and sci-kit-learn. You'll also learn how to build and deploy machine learning models in cloud environments like Azure.
  • Our training program is designed to help you develop the skills you need to succeed in a variety of industries, from healthcare to finance to retail. With a strong foundation in machine learning, you'll be well-positioned to take advantage of the many job opportunities in this rapidly-growing field.

Benefits of learning Machine Learning

  • Our Machine Learning certification training program provides comprehensive training in the field of machine learning, with a focus on real-world applications. Our experienced trainers will guide you through the fundamentals of machine learning, including data preprocessing, model selection, and evaluation.
  • During the training, you'll gain hands-on experience with popular machine learning algorithms and tools, including TensorFlow, Keras, and sci-kit-learn. You'll also learn how to build and deploy machine learning models in cloud environments like Azure.
  • Our training program is designed to help you develop the skills you need to succeed in a variety of industries, from healthcare to finance to retail. With a strong foundation in machine learning, you'll be well-positioned to take advantage of the many job opportunities in this rapidly-growing field.


Related job roles

  • Data Engineer
  • Data Scientist
  • Data Analyst
  • Applied Machine Learning Engineer
  • Data Science Lead Manager
  • Natural Language Processing Scientist

2000+ Ratings

3000+ Learners

Skills Covered in Machine Learning Course

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Supervised machine learning
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Unsupervised machine learning
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An in-depth discussion on time series modeling
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Linear regressions
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Logistic regression
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An in-depth discussion on Kernel SVM
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An in-depth discussion on KMeans clustering
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Details about Naive Bayes
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Details about decision tree
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Details about random forest classifiers
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Bagging techniques
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Boosting techniques

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.

Career

Course Key Features

Access to state-of-the-art labs
Access to four 4 real-world industry projects
More than 25 hands-on projects are available
Applicants can get back 100% of their money
Mentoring sessions headed by industry experts are available
The course will span 58 hours in total and will mostly consist of applied learning

Machine Learning Videos

Machine Learning

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.

Fees & Training Options

Online Training

$

  • All benefits available in self-paced learning training method along with 90 days access to online classes with flexible timings.
  • All online classes will be live and they will be taken by veterans of Machine Learning who are actively associated with the sector.
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Machine Learning Training Syllabus

Prerequisites

This course is best suited for –

  • Fresh Graduates
  • Analytics Managers
  • Information Architects
  • Business Analysts
  • Software developers and
  • Web developers.

With this course, the aforementioned will be able to become Data Scientists and secure a High-paying job in the Machine Learning sector easily. In terms of prerequisites, applicants would need to have a strong grip on statistics and advanced mathematics.

They would also need to be familiar with programming languages like Python.

Machine Learning Course Syllabus

Machine Learning
1. Course Introduction
  • Course Introduction
2. Introduction to AI and Machine Learning
  • Learning Objectives
  • 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
3. Data Preprocessing
  • Learning Objectives
  • Data Exploration: Loading Files
  • Demo: Importing and Storing Data
  • Practice: Automobile Data Exploration I
  • Data Exploration Techniques: Part 1
  • Data Exploration Techniques: Part 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
4. Supervised Learning
  • Learning Objectives
  • 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
5. Feature Engineering
  • Learning Objectives
  • 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
6. Supervised Learning: Classification
  • Overview of Classification
  • 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
7. Unsupervised Learning
  • Learning Objectives
  • 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
8. Time Series Modeling
  • Learning Objectives
  • 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
9. Ensemble Learning
  • Learning Objectives
  • 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
10. Recommender Systems
  • Learning Objectives
  • 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
11. Text Mining
  • Learning Objectives
  • 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
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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.

To learn Machine Learning, applicants would need to be proficient in programming languages like Python, C++, R, Java, and JavaScript.

Certification

Our Top Instructors

Career after Machine Learning

  • If you're interested in machine learning and looking for a career in the field, Azure Machine Learning can be a great platform to start. As a cloud-based platform, Azure Machine Learning provides you with the tools to build, train, and deploy machine learning models with ease.
  • As a machine learning professional on Azure, you can work on developing intelligent applications that can analyze data and make predictions. With a strong understanding of machine learning algorithms and tools, you can build models that can be used in a variety of industries, including healthcare, finance, and e-commerce.
  • The demand for skilled machine learning professionals is growing rapidly, and Azure Machine Learning is a great place to start your career. As companies continue to invest in machine learning technology, there will be more and more job opportunities for those with the necessary skills and expertise.
  • Whether you're a recent graduate or an experienced professional, a career in Azure Machine Learning can be a fulfilling and lucrative choice. With the right training and experience, you can become an expert in one of the most exciting and rapidly growing fields in technology.
Career

Machine Learning Course Reviews

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, Pune, Chennai only.

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.

Yes, We provide course completion certificate on web design & development. apart from this there is 1 more certificate called as “ Apponix Certified Professional in Machine Learning”
If you score more than 80% in the exam you will be awarded as “Apponix 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.

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Machine Learning Certification Course career in 2023

  • Machine learning is a field of study that allows machines to learn and improve on their own without being explicitly programmed. It has become an essential component of many industries, from finance to healthcare, and is changing the way businesses operate. As the demand for machine learning professionals continues to rise, there has never been a better time to pursue a career in this exciting field.
  • Our Machine Learning Certification Training Course is designed to provide you with the skills and knowledge you need to succeed in this fast-paced industry. From data preparation to model selection and evaluation, our experienced trainers will guide you through the entire machine learning process.
  • Taking our Machine Learning Certification Training Course offers many benefits, including:
  • Increased Job Opportunities: The demand for machine learning professionals is expected to grow rapidly over the next decade, making it an ideal field for those looking for job security and high-paying roles.
  • Improved Earning Potential: Machine learning professionals are in high demand and command high salaries. By acquiring the skills and knowledge needed to succeed in this field, you can significantly improve your earning potential.
  • Enhanced Problem-Solving Skills: Machine learning requires critical thinking, problem-solving, and analytical skills. By taking our course, you'll learn how to apply these skills to real-world problems, enhancing your problem-solving abilities.
  • Increased Creativity: Machine learning is a constantly evolving field that requires creativity and innovation. Our course is designed to encourage creativity and help you develop innovative solutions to complex problems.
  • Career Advancement: By acquiring machine learning skills, you can position yourself for career advancement within your organization or pursue new opportunities in the industry.
  • Machine learning is becoming increasingly popular due to its ability to analyze vast amounts of data quickly and accurately, leading to better business decisions and improved outcomes. As companies across industries continue to adopt machine learning, the demand for skilled professionals will only continue to grow.
  • There are many job opportunities available for machine learning professionals in 2023, including data scientists, machine learning engineers, software developers, and more. As companies continue to embrace artificial intelligence and machine learning technologies, the demand for skilled professionals will only continue to increase. By taking our Machine Learning Certification Training Course, you'll be equipped with the skills and knowledge needed to succeed in this exciting field and advance your career.

What are the real-world applications of 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!
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