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Machine Learning Course Overview

This Machine Learning course offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling. Learn how to use Python in this Machine Learning certification training to draw predictions from data.

The Executive Programme in Machine Learning, provides a cutting-edge curriculum designed with an emphasis on the tools and techniques used for handling, managing, analysing and interpreting data and applying them on real-time business use cases. This intensive six-month programme is delivered via engaging live online sessions and a three-day on-campus immersion at the campus. 

 

Machine Learning Key Features

100% Money Back Guarantee
No questions asked refund*
At Fiesttech, we value the trust of our patrons immensely. But, if you feel that this Machine Learning does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 58 hours of Applied Learning
  • Gain expertise with 25+ hands-on exercises
  • 4 real-life industry projects with integrated labs
  • Dedicated mentoring sessions from industry experts
  • Data-driven, real-time, day-to-day organizational decisions
  • Skills to implement machine learning algorithms

 

Skills Covered

  • Supervised and unsupervised learning
  • Time series modeling
  • Linear and logistic regression
  • Kernel SVM
  • KMeans clustering
  • Naive Bayes
  • Decision tree
  • Random forest classifiers
  • Boosting and Bagging techniques
  • Deep Learning fundamentals

 

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Benefits

Your career in machine learning, and the chances are you will be able to use linear algebra, calculus, probability, and statistics in your daily work. Since machine learning focuses on computers, data and optimizing their performance, computer programming is an essential skill to master machine learning. The Machine Learning market is expected to reach USD $32.64 Billion by 2024, at a growth rate of 44.5%, indicating the increased adoption of Machine Learning among companies. By 2024, the demand for Machine Learning engineers is expected to grow by 11-percent. So this qualifies to be the best skill to learn in 2022 and take the career growth you deserve.

 

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REACH OUT TO US FOR MORE INFORMATION


+91 844 844 0724

info@fiesttech.com
GO AT YOUR OWN PACE

Training Options

Explore all of our training options and pick your suitable ones to enroll and start learning with us! We ensure that you will never regret it!

SELF-PACED LEARNING
299
  • Lifetime access self-paced eLearning content 
  • 4 hands-on AI projects to perfect the skills learned
  • Simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • Three 90-minute workshops from industry experts
  • Job placement assistance from partner companies
  • Receipt of all programme brochures and newsletters
  • 24x7 learner assistance and support
INSTRUCTOR-LED TRAINING
449
  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Daily Live Session with our Certified ML Instructor
  • Dedicated Doubts Session
  • One to One Mentoring for all Students
  • Interview preparation and Assignments
  • Live Projects detailed for real-world demo

Classes starting from:-

  22ndJan 2024:Weekday Class

  27th Jan 2024:Weekend Class

CORPORATE TRAINING
Customized to your learning needs
  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise-grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • Customized sessions for specific business needs
  • Project Specific business use cases
  • In campus training, on-premises training
  • 24x7 learner assistance and support
APPLY

Who Can Apply

  • Engineering Students and Recently Graduate Candidates
  • Software Developers
  • Business Analysts
  • Automation Engineers
  • Solution Architects
  • Quality Analysts
  • Project Managers

 

Machine Learning Course Curriculum

Eligibility

The Machine Learning certification course is well-suited for participants at the intermediate level including, Analytics Managers, Business Analysts, Information Architects, Developers looking to become Machine Learning Engineers or Data Scientists, and graduates seeking a career in Data Science and Machine Learning.

Pre-requisites

This Machine Learning course requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial. Before getting into the Machine Learning certification training, you should understand these fundamental courses, including Python for Data Science, Math Refresher, and Statistics Essential for Data Science.

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Course Content

Live Course

Self Paced

  • 1.01 - Course Introduction
    05:44
  • 1.02 - Accessing Practice Lab
    01:34
  • 2.01 - 2.1 Learning Objectives
    01:12
  • 2.02 - 2.2 Emergence of Artificial Intelligence
    03:23
  • 2.03 - 2.3 Artificial Intelligence in Practice
    02:11
  • 2.04 - 2.4 Sci-Fi Movies with the Concept of AI
    03:32
  • 2.05 - 2.5 Recommender Systems
    01:34
  • 2.06 - 2.6 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
    02:32
  • 2.07 - 2.7 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
    01:12
  • 2.08 - 2.9 Machine Learning Approaches
    00:54
  • 2.09 - 2.10 Machine Learning Techniques
    03:32
  • 2.10 - 2.11 Applications of Machine Learning: Part A
    01:21
  • 2.11 - 2.12 Applications of Machine Learning: Part B
    00:54
  • 2.12 - 2.13 Key Takeaways
    00:25
  • 2.13 - Knowledge Check
    01:12
  • 2.14 - 2.8 Definition and Features of Machine Learning
    02:45
  • 3.01 - 3.1 Learning Objectives
    00:43
  • 3.02 - 3.2 Data Exploration Loading Files: Part A
    02:32
  • 3.03 - 3.2 Data Exploration Loading Files: Part B
    01:21
  • 3.04 - Practice: Automobile Data Exploration - A
    09:34
  • 3.05 - 3.3 Demo: Importing and Storing Data
    04:32
  • 3.06 - 3.4 Data Exploration Techniques: Part A
    02:32
  • 3.07 - 3.5 Data Exploration Techniques: Part B
    01:12
  • 3.08 - 3.6 Seaborn
    03:32
  • 3.09 - 3.7 Demo: Correlation Analysis
    03:32
  • 3.10 - Practice: Automobile Data Exploration - B
    02:32
  • 3.11 - 3.8 Data Wrangling
    01:12
  • 3.12 - 3.9 Missing Values in a Dataset
    02:11
  • 3.13 - 3.10 Outlier Values in a Dataset
    00:54
  • 3.14 - 3.11 Demo: Outlier and Missing Value Treatment
    05:56
  • 3.15 - Practice: Data Exploration - C
    12:00
  • 3.16 - 3.12 Data Manipulation
    03:32
  • 3.17 - 3.13 Functionalities of Data Object in Python: Part A
    02:21
  • 3.18 - 3.14 Functionalities of Data Object in Python: Part B
    02:32
  • 3.19 - 3.15 Different Types of Joins
    01:12
  • 3.20 - 3.16 Typecasting
    01:32
  • 3.21 - 3.17 Demo: Labor Hours Comparison
    06:07
  • 3.22 - Practice: Data Manipulation
    08:08
  • 3.23 - 3.18 Key Takeaways
    00:43
  • 3.24 - Knowledge Check
    00:25
  • 3.25 - Storing
    05:44
  • 3.26 - Storing Test Results
    02:32
  • 4.01 - 4.1 Learning Objectives
    00:34
  • 4.02 - 4.2 Supervised Learning
    02:32
  • 4.03 - 4.3 Supervised Learning- Real-Life Scenario
    03:32
  • 4.04 - 4.4 Understanding the Algorithm
    03:32
  • 4.05 - 4.5 Supervised Learning Flow
    01:12
  • 4.06 - 4.6 Types of Supervised Learning: Part A
    02:32
  • 4.07 - 4.7 Types of Supervised Learning: Part B
    03:32
  • 4.08 - 4.8 Types of Classification Algorithms
    03:31
  • 4.09 - 4.9 Types of Regression Algorithms: Part A
    01:21
  • 4.10 - 4.10 Regression Use Case
    04:34
  • 4.11 - 4.11 Accuracy Metrics
    01:48
  • 4.12 - 4.12 Cost Function
    01:48
  • 4.13 - 4.13 Evaluating Coefficients
    03:23
  • 4.14 - 4.14 Demo: Linear Regression
    08:43
  • 4.15 - Practice: Boston Homes - A
    09:34
  • 4.16 - 4.15 Challenges in Prediction
    01:34
  • 4.17 - 4.16 Types of Regression Algorithms: Part B
    08:43
  • 4.18 - 4.17 Demo: Bigmart
    23:51
  • 4.19 - Practice: Boston Homes - B
    06:07
  • 4.20 - 4.18 Logistic Regression: Part A
    04:21
  • 4.21 - 4.19 Logistic Regression: Part B
    03:23
  • 4.22 - 4.20 Sigmoid Probability
    02:43
  • 4.23 - 4.21 Accuracy Matrix
    02:35
  • 4.24 - 4.22 Demo: Survival of Titanic Passengers
    05:25
  • 4.25 - Practice: Iris Species
    09:09
  • 4.26 - 4.23 Key Takeaways
    00:46
  • 4.27 - Knowledge Check
    00:46
  • 4.28 - Health Insurance Cost
    09:21
  • 5.01 - 5.1 Learning Objectives
    01:12
  • 5.02 - 5.2 Feature Selection
    01:21
  • 5.03 - 5.3 Regression
    01:34
  • 5.04 - 5.4 Factor Analysis
    03:23
  • 5.05 - 5.5 Factor Analysis Process
    02:11
  • 5.06 - 5.6 Principal Component Analysis (PCA)
    02:45
  • 5.07 - 5.7 First Principal Component
    02:11
  • 5.08 - 5.8 Eigenvalues and PCA
    02:35
  • 5.09 - 5.9 Demo: Feature Reduction
    07:28
  • 5.10 - Practice: PCA Transformation
    09:00
  • 5.11 - 5.10 Linear Discriminant Analysis
    03:43
  • 5.12 - 5.11 Maximum Separable Line
    03:23
  • 5.13 - 5.12 Find Maximum Separable Line
    05:56
  • 5.14 - 5.13 Demo: Labeled Feature Reduction
    04:22
  • 5.15 - Practice
    09:21
  • 5.16 - Practice: LDA Transformation
    05:44
  • 5.17 - 5.14 Key Takeaways
    00:34
  • 5.18 - Knowledge Check
    00:25
  • 5.19 - Simplifying Cancer Treatment
    08:43
  • 6.01 - 6.1 Learning Objectives
    00:43
  • 6.02 - 6.2 Overview of Classification
    01:34
  • 6.03 - 6.3 Classification: A Supervised Learning Algorithm
    02:43
  • 6.04 - 6.4 Use Cases of Classification
    02:11
  • 6.05 - 6.5 Classification Algorithms
    03:23
  • 6.06 - 6.6 Decision Tree Classifier
    02.11
  • 6.07 - 6.7 Decision Tree Examples
    01:32
  • 6.08 - 6.8 Decision Tree Formation
    01:35
  • 6.09 - 6.9 Choosing the Classifier
    01:32
  • 6.10 - 6.10 Overfitting of Decision Trees
    01:34
  • 6.11 - 6.11 Random Forest Classifier- Bagging and Bootstrapping
    01:12
  • 6.12 - 6.12 Decision Tree and Random Forest Classifier
    02:45
  • 6.13 - 6.13 Performance Measures: Confusion Matrix
    04:34
  • 6.14 - Performance Measures: Cost Matrix
    05:56
  • 6.15 - 6.15 Demo: Horse Survival
    08:43
  • 6.16 - Practice: Loan Risk Analysis
    23:51
  • 6.17 - 6.16 Naive Bayes Classifier
    04:21
  • 6.18 - 6.17 Steps to Calculate Posterior Probability: Part A
    03:11
  • 6.19 - 6.18 Steps to Calculate Posterior Probability: Part B
    02:11
  • 6.20 - 6.19 Support Vector Machines : Linear Separability
    02:43
  • 6.21 - 6.20 Support Vector Machines : Classification Margin
    01:34
  • 6.22 - 6.21 Linear SVM : Mathematical Representation
    04:22
  • 6.23 - 6.22 Non-linear SVMs
    03:31
  • 6.24 - 6.23 The Kernel Trick
    03:21
  • 6.25 - 6.24 Demo: Voice Classification
    09:34
  • 6.26 - Practice: College Classification
    07:28
  • 6.27 - 6.25 Key Takeaways
    00:25
  • 6.28 - Knowledge Check
    01:12
  • 6.29 - Classify Kinematic Data
    06:07
  • 7.01 - 7.1 Learning Objectives
    00:43
  • 7.02 - 7.2 Overview
    01:35
  • 7.03 - 7.3 Example and Applications of Unsupervised Learning
    04:21
  • 7.04 - 7.4 Clustering
    02:11
  • 7.05 - 7.5 Hierarchical Clustering
    02:43
  • 7.06 - 7.6 Hierarchical Clustering Example
    02:32
  • 7.07 - 7.7 Demo: Clustering Animals
    04:22
  • 7.08 - Practice: Customer Segmentation
    02:35
  • 7.09 - 7.8 K-means Clustering
    03:23
  • 7.10 - 7.9 Optimal Number of Clusters
    01:26
  • 7.11 - 7.10 Demo: Cluster Based Incentivization
    07:28
  • 7.12 - Practice: Image Segmentation
    03:31
  • 7.13 - 7.11 Key Takeaways
    00:32
  • 7.14 - Knowledge Check
    00:25
  • 7.15 - Clustering Image Data
    05:44
  • 8.01 - 8.1 Learning Objectives
    00:25
  • 8.02 - 8.2 Overview of Time Series Modeling
    02.11
  • 8.03 - 8.3 Time Series Pattern Types: Part A
    02:35
  • 8.04 - 8.4 Time Series Pattern Types: Part B
    01:35
  • 8.05 - 8.5 White Noise
    01:28
  • 8.06 - 8.6 Stationarity
    02:06
  • 8.07 - 8.7 Removal of Non-Stationarity
    01:35
  • 8.08 - 8.8 Demo: Air Passengers - A
    08:08
  • 8.09 - Practice: Beer Production - A
    24:48
  • 8.10 - 8.9 Time Series Models: Part A
    03:23
  • 8.11 - 8.10 Time Series Models: Part B
    02:11
  • 8.12 - 8.11 Time Series Models: Part C
    01:34
  • 8.13 - 8.12 Steps in Time Series Forecasting
    02:45
  • 8.14 - 8.13 Demo: Air Passengers - B
    04:32
  • 8.15 - Practice: Beer Production - B
    19:46
  • 8.16 - 8.14 Key Takeaways
    00:34
  • 8.17 - Knowledge Check
    01:12
  • 8.18 - IMF Commodity Price Forecast
    03:31
  • 9.01 - 9.01 Ensemble Learning
    00:43
  • 9.02 - 9.2 Overview
    01:35
  • 9.03 - 9.3 Ensemble Learning Methods: Part A
    02.11
  • 9.04 - 9.4 Ensemble Learning Methods: Part B
    02:32
  • 9.05 - 9.5 Working of AdaBoost
    03:11
  • 9.06 - 9.6 AdaBoost Algorithm and Flowchart
    02:11
  • 9.07 - 9.7 Gradient Boosting
    02:43
  • 9.08 - 9.8 XGBoost
    03:23
  • 9.09 - 9.9 XGBoost Parameters: Part A
    01:32
  • 9.10 - 9.10 XGBoost Parameters: Part B
    02:11
  • 9.11 - 9.11 Demo: Pima Indians Diabetes
    04:21
  • 9.12 - Practice: Linearly Separable Species
    09:34
  • 9.13 - 9.12 Model Selection
    03:11
  • 9.14 - 9.13 Common Splitting Strategies
    03:23
  • 9.15 - 9.14 Demo: Cross Validation
    04:21
  • 9.16 - Practice: Model Selection
    08:08
  • 9.17 - 9.15 Key Takeaways
    00:25
  • 9.18 - Knowledge Check
    00:43
  • 9.19 - Tuning Classifier Model with XGBoost
    01:34
  • 10.01 - 10.1 Learning Objectives
    00:25
  • 10.02 - 10.2 Introduction
    01:32
  • 10.03 - 10.3 Purposes of Recommender Systems
    01:12
  • 10.04 - 10.4 Paradigms of Recommender Systems
    02:45
  • 10.05 - 10.5 Collaborative Filtering: Part A
    01:34
  • 10.06 - 10.6 Collaborative Filtering: Part B
    01:26
  • 10.07 - 10.7 Association Rule Mining
    01:32
  • 10.08 - Association Rule Mining: Market Basket Analysis
    04:32
  • 10.09 - 10.9 Association Rule Generation: Apriori Algorithm
    02.11
  • 10.10 - 10.10 Apriori Algorithm Example: Part A
    02:45
  • 10.11 - 10.11 Apriori Algorithm Example: Part B
    02:11
  • 10.12 - 10.12 Apriori Algorithm: Rule Selection
    03:23
  • 10.13 - 10.13 Demo: User-Movie Recommendation Model
    04:22
  • 10.14 - Practice: Movie-Movie recommendation
    06:07
  • 10.15 - 10.14 Key Takeaways
    00:34
  • 10.16 - Knowledge Check
    00:46
  • 10.17 - Book Rental Recommendation
    01:12
  • 11.01 - 11.1 Learning Objectives
    00:25
  • 11.02 - 11.2 Overview of Text Mining
    01:34
  • 11.03 - 11.3 Significance of Text Mining
    01:34
  • 11.04 - 11.4 Applications of Text Mining
    02:11
  • 11.05 - 11.5 Natural Language ToolKit Library
    01:12
  • 11.06 - 11.6 Text Extraction and Preprocessing: Tokenization
    02.11
  • 11.07 - 11.7 Text Extraction and Preprocessing: N-grams
    01:34
  • 11.08 - 11.8 Text Extraction and Preprocessing: Stop Word Removal
    02:35
  • 11.09 - 11.9 Text Extraction and Preprocessing: Stemming
    01:35
  • 11.10 - 11.10 Text Extraction and Preprocessing: Lemmatization
    02:43
  • 11.11 - 11.11 Text Extraction and Preprocessing: POS Tagging
    01:35
  • 11.12 - 11.12 Text Extraction and Preprocessing: Named Entity Recognition
    01:26
  • 11.13 - 11.13 NLP Process Workflow
    01:12
  • 11.14 - 11.14 Demo: Processing Brown Corpus
    05:44
  • 11.15 - Wiki Corpus
    00:34
  • 11.16 - 11.15 Structuring Sentences: Syntax
    02:11
  • 11.17 - 11.16 Rendering Syntax Trees
    02:32
  • 11.18 - 11.17 Structuring Sentences: Chunking and Chunk Parsing
    04:21
  • 11.19 - 11.18 NP and VP Chunk and Parser
    02:32
  • 11.20 - 11.19 Structuring Sentences: Chinking
    02.11
  • 11.21 - 11.20 Context-Free Grammar (CFG)
    01:34
  • 11.22 - 11.21 Demo: Structuring Sentences
    07:28
  • 11.23 - Practice: Airline Sentiment
    28:51
  • 11.24 - 11.22 Key Takeaways
    00:25
  • 11.25 - Knowledge Check
    00:32
  • 11.26 - FIFA World Cup
    23:51
  • 12.01 - Project Highlights
    02:45
  • 12.02 - Andrew McAfee | Building Mind-Machine Combinations: Welcome Technology as Your Colleague
    05:56
  • 12.03 - Uber Fare Prediction
    09:09
  • 12.04 - Amazon - Employee Access
    01:34:43
  • 13.01 - Phishing Detector with LR
    01:23:43
  • 13.02 - California Housing Price Prediction
    43:32
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Machine Learning Exam & Certification

Upon successful completion of the ML course, Fiest Tech will provide you with an industry-recognized course completion certificate which has lifelong validity.

This course will give you a complete overview of Machine Learning methodologies, enough to prepare you to excel in your next role as a Machine Learning Engineer. You will earn Fiest Tech Machine Learning certification that will attest to your new skills and on-the-job expertise. Get familiar with regression, classification, time series modelling, and clustering.

Online Classroom:

  • Attend one complete batch of Machine Learning training
  • Submit at least one completed project.

Online Self-Learning:

  • Complete 85% of the course
  • Submit at least one completed project.

Yes, we provide 1 practice test as part of our Machine Learning course to help you prepare for the actual certification exam. You can try this Course to understand the type of tests that are part of the course curriculum.

REVIEWS

Machine Learning Course Reviews

Bootcamp

Why Online Bootcamp

Develop skills for real career growth

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

Develop skills for real career growth

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

Develop skills for real career growth

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

Develop skills for real career growth

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

FAQS

Machine Learning Course FAQs

Machine learning is nothing but an implementation of Artificial Intelligence that allows systems to simultaneously learn and improve from past experiences without the need of being explicitly programmed. It is a process of observing data patterns, collecting relevant information, and making effective decisions for a better future of any organization. Machine learning facilitates the analysis of huge quantities of data, usually delivering faster and accurate results to extract profitable benefits and opportunities.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Machine learning is generally divided into three types - Supervised Learning, Unsupervised Learning, and Reinforcement Learning. This Machine Learning course gives you an in-depth understanding of all these three types of machine learning.

Yes, some coding knowledge is required to perform certain machine learning tasks like statistical analysis. Basic knowledge of either Python, R, or Java is recommended before taking this Machine Learning certification course.

Machine learning is one of the most in-demand career fields today. Present-day applications like driverless cars, facial recognition, voice assistants, and ecommerce recommendation engines are powered by machine learning. This field will be relevant going forward and professionals entering it can fetch lucrative salaries. As a first step, you can take our machine learning online course and learn everything from scratch.

Machine learning is in high demand. But before you jump into certification training, it’s essential for beginners to get familiar with the basics of machine learning first. Fiest Tech free resources articles, tutorials, and YouTube videos will help you get a handle on the concepts and techniques of machine learning. Start your learning with our free ML courses that serve as a foundation for this exciting and dynamic field: Statistics Essentials for Data Science, Math Refresher, and Data Science with Python.

 While taking this machine learning training, you can refer to the following books for a more comprehensive learning experience:

  •  Machine Learning Yearning by Andrew Ng
  • Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson
  • Machine Learning Design Patterns by Valliappa Lakshmanan, Sara Robinson, Michael Munn
  • Hands-on Machine Learning by Aurelien Geron
  • Pattern Recognition & Machine Learning by Christopher M. Bishop

 Industries that use machine learning extensively are transportation, healthcare, finance, agriculture, retail, and customer service. By pursuing the right Machine learning course, you can easily find jobs in these industries and have a highly fulfilling career ahead of you.

 Artificial Intelligence is a broad field that encompasses everything that involves giving machines human-like intelligence. Machine learning is an important subset of AI where machines are given a lot of input data and algorithms are applied to train it and give them the ability to ‘learn’ and perform the desired actions. Our ML course deals with this topic in detail.

The roles and responsibilities of Machine Learning Engineers include:  Designing and building machine learning systems and schemes Analyzing and processing data science prototypes Performing statistical analysis and modifying models using test results Training ML systems whenever required and enhancing prevailing Machine Learning frameworks and libraries Exploring new data to improve the machine’s performance

Having a Machine Learning certification will help you gain the necessary knowledge and training to shape your career in an AI-led future and deal with machine learning problems.

 Machine Learning Engineers take into account various factors to decide which language would best suit their project. Their top choices include Python, C++, R, Java, and JavaScript.

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