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

About Machine Learning Training Course

Our ML course will put you on the short path towards success in case you are planning to become a Machine Learning expert before your rivals can!

Machine learning is the part and parcel of Artificial Intelligence and our course curriculum covers not only the basics but also the advanced levels of ML. The best part, this course will not span for several months! We have come up with an intelligently designed course curriculum that spans just 58 hours.

To maximise the learning experience, our course curriculum is backed with –
Hands-on projects for applied Learning
Interactive labs and
Mentoring from industry experts.

Our Machine Learning course curriculum covers the basics and advanced levels of-
Real-time data
Algorithm development
Supervised ML
Unsupervised ML
Regression
Classification and
Time series modelling

You will also be taught the intricacies of the popular programming language Python that will help you to chalk up predictions using raw data!

Other reasons to choose apponix’s ML course are as follows -
You will be developing skills that will help you achieve your career goals in no time. You will be taught using our cutting-edge ML course curriculum that is periodically refreshed in order to keep the same on par with the latest developments taking place in the ML scene.
All our trainers are ML veterans who are currently associated with the ML sector and are heading real-world projects.
You will have access to industry insights and real-world problems that will help you to gauge the progress you have made.
Our structured guidance for ML course participants ensures that you will have round-the-clock access to mentors from all over the world. This ensures that irrespective of your time zone if you have a doubt, your queries will not go unanswered!

Overview of the Course

Machine Learning Course Syllabus

  • 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
Download Full Machine Learning Course Syllabus Now

Course content

  • Introduction to machine learning
  • Introduction to Artificial Intelligence
  • Introduction and detailed discussion on data pre-processing
  • Introduction and detailed discussion on Supervised Machine Learning
  • Introduction and detailed discussion on Feature Engineering
  • Introduction and detailed discussion on Supervised Learning Classification
  • Introduction and detailed discussion on Unsupervised Learning
  • Introduction and detailed discussion on Time Series Modelling
  • Introduction and detailed discussion on Ensemble Learning
  • Introduction and detailed discussion on Recommender Systems
  • Introduction and detailed discussion on Text Mining
  • Hands-on practice of live industry-related projects

Industry projects

  • You will be asked to help Uber with its fare prediction as the company wants to improve the overall accuracy of its fare prediction model for the coming years. You will be helping the brand to choose the AI and machine learning solutions in order to let it enjoy the best outcomes.
  • You will be helping Mercedes Benz to shorten the time it spends on testing new cars so that the brand’s products reach to the hands of its customers quickly. You would need to design a machine learning algorithm for this project.
  • You will be helping out the Inter-American Development bank with their aid program targeted towards qualifying people for the same. You will be building and improving the overall accuracy of the data set used by the bank by making the best use of random forest classifiers!
  • You will be helping Amazon by accessing the data of its employees as well as their respective access privileges to come up with a solution that will offer and revoke access privileges to employees as they join or leave the company.

Benefits of Machine Learning Course

Machine learning is quickly becoming one of the most popular career paths in the world. Machine learning experts can easily land a high-paying job as the demand for such experts is increasing exponentially whereas the number of suitable candidates is yet to reach a competitive number!

Related job roles

  • Data Engineer
  • Applied Machine Learning Engineer
  • Data Scientist
  • Data Science Leader
  • Natural Language Processing Scientist

Skills covered

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

Course features

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

Training Options

Self-Paced Learning
  • Applicants will get lifetime access to quality-assured e-learning content that will complement their self-paced learning efforts.
  • Applicants will have access to 4 real-world projects that they can try their hands on in order to gauge their progress.
  • Applicants will have access to practice test papers.
  • Applicants will have access to test papers that they can solve to test their progress.
  • Applicants will have access to state-of-the-art labs.
Online Bootcamp
  • All benefits available in self-paced learning training method along with 90 days access to online classes with flexible timings.
  • All benefits available in self-paced learning training method along with 90 days access to online classes with flexible timings.
Corporate Training
  • This training method will make the use of both self-paced learning as well as trainer-based learning sessions.
  • The tariff is flexible.
  • Applicants will have access to Enterprise-grade Learning Management System, and enterprise dashboards that are ideal for individuals as well as participating teams.

Eligibility /prerequisites

This course is best suited for –

  • 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.

Exam & certification on Machine Learning

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.

Student Feedback for Machine Learning Training

Dhana Laxmi
Vinay H R

Excellent teaching. Trainer is very friendly and kind to us.. Best training for Machine Learning.

Machine Learning Training


rajesh
Vikas Kumar

This is the institute for learning the courses properly. You will get the full knowledge about the courses. The way of teaching is best.Thank you apponix

Machine Learning Training


student
Sudheer M

It was a good learning experience with apponix. Faculty is good. Staff are also good and friendly

Machine Learning Training

Apponix Ratings

1000+ Satisfied Learners

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Student Review

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Seema H

Had a wonderfull time learning at Apponix and it's such an interactive class where it's very easy to learn and much easy to implement it as it's a practical class. I would recommend any one interested in Machine Learning to join Apponix.

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Ajay K

It was a good experience with Machine Learning from Apponix technologies. All the concepts were cleared by the trainer and the recording sessions were also available to help in the future to practice it at home.

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Shubanka

It was very good to know about the overview of Machine Learning. The mentor taught the sessions very clearly so that I understood the topics which had been taught in the session.

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Savita

Sir is very helpful and takes every step to make the students understand topics.

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Sohail H

It is good to learn Google Cloud Platform here and the tranier is excellent.

Machine Learning Training Course FAQs

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.
  • 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!
  • What are the classifications of machine learning?

  • Machine learning is categorised into the following types –
    • Supervised Machine Learning
    • Unsupervised Machine Learning and
    • Reinforcement 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.
  • 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 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.
  • Can a novice learn machine learning?

  • 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.
  • Which computer language is used for machine learning?

  • To learn Machine Learning, applicants would need to be proficient in programming languages like Python, C++, R, Java, and JavaScript.
  • It is worth it to be machine learning certified?

  • 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!
  • What is the future of the machine learning job sector?

  • 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!
  • What is the job of a machine learning expert?

  • 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.
  • 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.
  • How to unlock apponix’s certificate for Machine Learning?

  • 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.
  • Does this course come with practice tests?

  • Yes, this course comes with a practice test that will help you groom yourself for the real ML certification exam.