Machine Learning Course Description

  • Why learn Machine learning?

    • Machine learning is taking over the world, and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning
    • The machine learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period

  • What are the objectives of our Machine Learning Certification Training course?

    This Machine Learning online course will provide you with insights into the vital roles played by machine learning engineers and data scientists. Upon completion of this course, you will be able to uncover the hidden value in data using Python programming for futuristic inference. You will work with real-time data across multiple domains including e-commerce, automotive, social media and more. You will learn how to develop machine learning algorithms using concepts of regression, classification, time series modelling and much more.

  • What skills will you learn with our Machine Learning Certification Course?

    • Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modelling
    • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
    • Acquire thorough knowledge of the statistical and heuristic aspects of machine learning
    • Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
    • Validate machine learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques
    • Comprehend theoretical concepts and how they relate to the practical aspects of machine learning

  • Who should take this Machine Learning Training Course?

    There is an increasing demand for skilled machine learning engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course for the following professionals in particular:

    • Developers aspiring to be data scientists or machine learning engineers
    • Analytics managers who are leading a team of analysts 
    • Business analysts who want to understand data science techniques
    • Information architects who want to gain expertise in machine learning algorithms 
    • Analytics professionals who want to work in machine learning or artificial intelligence
    • Graduates looking to build a career in data science and machine learning
    • Experienced professionals who would like to harness machine learning in their fields to get more insights

  • What projects are included in this Machine Learning Online Training Course?

    Simplilearn’s Machine Learning course is a hands-on, code-driven training that will help you apply your machine learning knowledge. You will work on 4 projects that encompass 25+ ancillary exercises and 17 machine learning algorithms. 

    Project 1: Fare Prediction for Uber

    Domain: Delivery (Commerce)
    Uber, one of the largest US-based taxi cab provider, wants to improve the accuracy of fare predicted for any of the trips. Help Uber by building and choosing the right model.

    Project 2: Test bench time reduction for Mercedes-Benz

    Domain: Automobile

    Mercedes-Benz, a global Germany based automobile manufacturer, wants to reduce the time it spends on the test bench for any car. Faster testing will reduce the time to hit the market. Build and optimize the algorithm by performing dimensionality reduction and various techniques including xgboost to achieve the said objective. 

    Project 3: Income qualification prediction for Inter-American Development bank

    Many social programs have a hard time making sure the right people are given enough aid. It’s tricky when a program focuses on the poorest segment of the population. This segment of the population can’t provide the necessary income and expense records to prove that they qualify. Predicting the right set of people to be included for the aid remains a big challenge for  Inter-American Development Bank. Help the bank by building and improving the accuracy of the model using random forest classifier.

    Project 4: Access privileges prediction for Amazon.com employees

    There is a considerable amount of data regarding employees’ roles within an organization and the resources to which they have access. Given the data related to current employees and their provisioned access, models can be built that automatically determine access privileges as employees enter and leave roles within a company. These auto-access models seek to minimize the human involvement required to grant or revoke employee access. Help Amazon.com to build such a model and suggest the one with maximum accuracy.


     

  • What are the prerequisites for this Machine Learning course?

    Participants in this Machine Learning online course should have:

    • Familiarity with the fundamentals of Python programming 
    • Fair understanding of the basics of statistics and mathematics

    And, here are some of the fundamental courses that participants need to know before they get into Machine Learning online course:

    • Python for Data Science
    • Math Refresher
    • Statistics Essential for Data Science

Machine Learning Course Preview

    • Lesson 01 Course Introduction

      06:41
      • Course Introduction
        05:31
      • Accessing Practice Lab
        01:10
    • Lesson 02 Introduction to AI and Machine Learning

      19:36
      • 2.1 Learning Objectives
        00:43
      • 2.2 Emergence of Artificial Intelligence
        01:56
      • 2.3 Artificial Intelligence in Practice
        01:48
      • 2.4 Sci-Fi Movies with the Concept of AI
        00:22
      • 2.5 Recommender Systems
        00:45
      • 2.6 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
        02:47
      • 2.7 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
        01:23
      • 2.8 Definition and Features of Machine Learning
        01:30
      • 2.9 Machine Learning Approaches
        01:48
      • 2.10 Machine Learning Techniques
        02:21
      • 2.11 Applications of Machine Learning: Part A
        01:34
      • 2.12 Applications of Machine Learning: Part B
        02:11
      • 2.13 Key Takeaways
        00:28
      • Knowledge Check
    • Lesson 03 Data Preprocessing

      36:19
      • 3.1 Learning Objectives
        00:38
      • 3.2 Data Exploration Loading Files: Part A
        02:52
      • 3.2 Data Exploration Loading Files: Part B
        01:56
      • 3.3 Demo: Importing and Storing Data
        01:27
      • Practice: Automobile Data Exploration - A
      • 3.4 Data Exploration Techniques: Part A
        02:56
      • 3.5 Data Exploration Techniques: Part B
        02:47
      • 3.6 Seaborn
        02:18
      • 3.7 Demo: Correlation Analysis
        02:38
      • Practice: Automobile Data Exploration - B
      • 3.8 Data Wrangling
        01:27
      • 3.9 Missing Values in a Dataset
        01:55
      • 3.10 Outlier Values in a Dataset
        01:49
      • 3.11 Demo: Outlier and Missing Value Treatment
        04:18
      • Practice: Data Exploration - C
      • 3.12 Data Manipulation
        00:47
      • 3.13 Functionalities of Data Object in Python: Part A
        01:49
      • 3.14 Functionalities of Data Object in Python: Part B
        01:33
      • 3.15 Different Types of Joins
        01:32
      • 3.16 Typecasting
        01:23
      • 3.17 Demo: Labor Hours Comparison
        01:54
      • Practice: Data Manipulation
      • 3.18 Key Takeaways
        00:20
      • Knowledge Check
      • Storing Test Results
    • Lesson 04 Supervised Learning

      1:21:04
      • 4.1 Learning Objectives
        00:31
      • 4.2 Supervised Learning
        02:17
      • 4.3 Supervised Learning- Real-Life Scenario
        00:53
      • 4.4 Understanding the Algorithm
        00:52
      • 4.5 Supervised Learning Flow
        01:50
      • 4.6 Types of Supervised Learning: Part A
        01:54
      • 4.7 Types of Supervised Learning: Part B
        02:03
      • 4.8 Types of Classification Algorithms
        01:01
      • 4.9 Types of Regression Algorithms: Part A
        03:20
      • 4.10 Regression Use Case
        00:34
      • 4.11 Accuracy Metrics
        01:23
      • 4.12 Cost Function
        01:48
      • 4.13 Evaluating Coefficients
        00:53
      • 4.14 Demo: Linear Regression
        13:47
      • Practice: Boston Homes - A
      • 4.15 Challenges in Prediction
        01:45
      • 4.16 Types of Regression Algorithms: Part B
        02:40
      • 4.17 Demo: Bigmart
        21:55
      • Practice: Boston Homes - B
      • 4.18 Logistic Regression: Part A
        01:58
      • 4.19 Logistic Regression: Part B
        01:38
      • 4.20 Sigmoid Probability
        02:05
      • 4.21 Accuracy Matrix
        01:36
      • 4.22 Demo: Survival of Titanic Passengers
        14:07
      • Practice: Iris Species
      • 4.23 Key Takeaways
        00:14
      • Knowledge Check
      • Health Insurance Cost
    • Lesson 05 Feature Engineering

      27:52
      • 5.1 Learning Objectives
        00:27
      • 5.2 Feature Selection
        01:28
      • 5.3 Regression
        00:53
      • 5.4 Factor Analysis
        01:57
      • 5.5 Factor Analysis Process
        01:05
      • 5.6 Principal Component Analysis (PCA)
        02:31
      • 5.7 First Principal Component
        02:43
      • 5.8 Eigenvalues and PCA
        02:32
      • 5.9 Demo: Feature Reduction
        05:47
      • Practice: PCA Transformation
      • 5.10 Linear Discriminant Analysis
        02:27
      • 5.11 Maximum Separable Line
        00:44
      • 5.12 Find Maximum Separable Line
        03:12
      • 5.13 Demo: Labeled Feature Reduction
        01:53
      • Practice: LDA Transformation
      • 5.14 Key Takeaways
        00:13
      • Knowledge Check
      • Simplifying Cancer Treatment
    • Lesson 06 Supervised Learning Classification

      55:43
      • 6.1 Learning Objectives
        00:34
      • 6.2 Overview of Classification
        02:05
      • Classification: A Supervised Learning Algorithm
        00:52
      • 6.4 Use Cases of Classification
        02:37
      • 6.5 Classification Algorithms
        00:16
      • 6.6 Decision Tree Classifier
        02:17
      • 6.7 Decision Tree Examples
        01:45
      • 6.8 Decision Tree Formation
        00:47
      • 6.9 Choosing the Classifier
        02:55
      • 6.10 Overfitting of Decision Trees
        01:00
      • 6.11 Random Forest Classifier- Bagging and Bootstrapping
        02:22
      • 6.12 Decision Tree and Random Forest Classifier
        01:06
      • Performance Measures: Confusion Matrix
        02:21
      • Performance Measures: Cost Matrix
        02:06
      • 6.15 Demo: Horse Survival
        08:30
      • Practice: Loan Risk Analysis
      • 6.16 Naive Bayes Classifier
        01:28
      • 6.17 Steps to Calculate Posterior Probability: Part A
        01:44
      • 6.18 Steps to Calculate Posterior Probability: Part B
        02:21
      • 6.19 Support Vector Machines : Linear Separability
        01:05
      • 6.20 Support Vector Machines : Classification Margin
        02:05
      • 6.21 Linear SVM : Mathematical Representation
        02:04
      • 6.22 Non-linear SVMs
        01:06
      • 6.23 The Kernel Trick
        01:19
      • 6.24 Demo: Voice Classification
        10:42
      • Practice: College Classification
      • 6.25 Key Takeaways
        00:16
      • Knowledge Check
      • Classify Kinematic Data
    • Lesson 07 Unsupervised Learning

      28:26
      • 7.1 Learning Objectives
        00:29
      • 7.2 Overview
        01:48
      • 7.3 Example and Applications of Unsupervised Learning
        02:17
      • 7.4 Clustering
        01:49
      • 7.5 Hierarchical Clustering
        02:28
      • 7.6 Hierarchical Clustering Example
        02:01
      • 7.7 Demo: Clustering Animals
        05:39
      • Practice: Customer Segmentation
      • 7.8 K-means Clustering
        01:46
      • 7.9 Optimal Number of Clusters
        01:24
      • 7.10 Demo: Cluster Based Incentivization
        08:32
      • Practice: Image Segmentation
      • 7.11 Key Takeaways
        00:13
      • Knowledge Check
      • Clustering Image Data
    • Lesson 08 Time Series Modeling

      37:44
      • 8.1 Learning Objectives
        00:24
      • 8.2 Overview of Time Series Modeling
        02:16
      • 8.3 Time Series Pattern Types: Part A
        02:16
      • 8.4 Time Series Pattern Types: Part B
        01:19
      • 8.5 White Noise
        01:07
      • 8.6 Stationarity
        02:13
      • 8.7 Removal of Non-Stationarity
        02:13
      • 8.8 Demo: Air Passengers - A
        14:33
      • Practice: Beer Production - A
      • 8.9 Time Series Models: Part A
        02:14
      • 8.10 Time Series Models: Part B
        01:28
      • 8.11 Time Series Models: Part C
        01:51
      • 8.12 Steps in Time Series Forecasting
        00:37
      • 8.13 Demo: Air Passengers - B
        05:01
      • Practice: Beer Production - B
      • 8.14 Key Takeaways
        00:12
      • Knowledge Check
      • IMF Commodity Price Forecast
    • Lesson 09 Ensemble Learning

      35:41
      • 9.01 Ensemble Learning
        00:24
      • 9.2 Overview
        02:41
      • 9.3 Ensemble Learning Methods: Part A
        02:28
      • 9.4 Ensemble Learning Methods: Part B
        02:37
      • 9.5 Working of AdaBoost
        01:43
      • 9.6 AdaBoost Algorithm and Flowchart
        02:28
      • 9.7 Gradient Boosting
        02:36
      • 9.8 XGBoost
        02:23
      • 9.9 XGBoost Parameters: Part A
        03:15
      • 9.10 XGBoost Parameters: Part B
        02:30
      • 9.11 Demo: Pima Indians Diabetes
        04:14
      • Practice: Linearly Separable Species
      • 9.12 Model Selection
        02:08
      • 9.13 Common Splitting Strategies
        01:45
      • 9.14 Demo: Cross Validation
        04:18
      • Practice: Model Selection
      • 9.15 Key Takeaways
        00:11
      • Knowledge Check
      • Tuning Classifier Model with XGBoost
    • Lesson 10 Recommender Systems

      25:45
      • 10.1 Learning Objectives
        00:28
      • 10.2 Introduction
        02:17
      • 10.3 Purposes of Recommender Systems
        00:45
      • 10.4 Paradigms of Recommender Systems
        02:45
      • 10.5 Collaborative Filtering: Part A
        02:14
      • 10.6 Collaborative Filtering: Part B
        01:58
      • 10.7 Association Rule Mining
        01:47
      • Association Rule Mining: Market Basket Analysis
        01:43
      • 10.9 Association Rule Generation: Apriori Algorithm
        00:53
      • 10.10 Apriori Algorithm Example: Part A
        02:11
      • 10.11 Apriori Algorithm Example: Part B
        01:18
      • 10.12 Apriori Algorithm: Rule Selection
        02:52
      • 10.13 Demo: User-Movie Recommendation Model
        04:19
      • Practice: Movie-Movie recommendation
      • 10.14 Key Takeaways
        00:15
      • Knowledge Check
      • Book Rental Recommendation
    • Lesson 11 Text Mining

      43:58
      • 11.1 Learning Objectives
        00:22
      • 11.2 Overview of Text Mining
        02:11
      • 11.3 Significance of Text Mining
        01:26
      • 11.4 Applications of Text Mining
        02:23
      • 11.5 Natural Language ToolKit Library
        02:35
      • 11.6 Text Extraction and Preprocessing: Tokenization
        00:33
      • 11.7 Text Extraction and Preprocessing: N-grams
        00:55
      • 11.8 Text Extraction and Preprocessing: Stop Word Removal
        01:24
      • 11.9 Text Extraction and Preprocessing: Stemming
        00:44
      • 11.10 Text Extraction and Preprocessing: Lemmatization
        00:35
      • 11.11 Text Extraction and Preprocessing: POS Tagging
        01:17
      • 11.12 Text Extraction and Preprocessing: Named Entity Recognition
        00:54
      • 11.13 NLP Process Workflow
        00:53
      • 11.14 Demo: Processing Brown Corpus
        10:05
      • Practice: Wiki Corpus
      • 11.15 Structuring Sentences: Syntax
        01:54
      • 11.16 Rendering Syntax Trees
        00:55
      • 11.17 Structuring Sentences: Chunking and Chunk Parsing
        01:38
      • 11.18 NP and VP Chunk and Parser
        01:39
      • 11.19 Structuring Sentences: Chinking
        01:44
      • 11.20 Context-Free Grammar (CFG)
        01:56
      • 11.21 Demo: Structuring Sentences
        07:46
      • Practice: Airline Sentiment
      • 11.22 Key Takeaways
        00:09
      • Knowledge Check
      • FIFA World Cup
    • Lesson 12 Project Highlights

      02:40
      • Project Highlights
        02:40
      • Uber Fare Prediction
      • Amazon - Employee Access
    • Practice Projects

      • California Housing Price Prediction
      • Phishing Detector with LR
    • Lesson 00 - Course Overview

      04:34
      • 0.001 Course Overview
        04:34
    • Lesson 01 - Data Science Overview

      20:27
      • 1.001 Introduction to Data Science
        08:42
      • 1.002 Different Sectors Using Data Science
        05:59
      • 1.003 Purpose and Components of Python
        05:02
      • 1.4 Quiz
      • 1.005 Key Takeaways
        00:44
    • Lesson 02 - Data Analytics Overview

      18:20
      • 2.001 Data Analytics Process
        07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.005 EDA - Graphical Technique
        00:57
      • 2.006 Data Analytics Conclusion or Predictions
        04:30
      • 2.007 Data Analytics Communication
        02:06
      • 2.8 Data Types for Plotting
      • 2.009 Data Types and Plotting
        02:29
      • 2.11 Quiz
      • 2.012 Key Takeaways
        00:57
      • 2.10 Knowledge Check
    • Lesson 03 - Statistical Analysis and Business Applications

      23:53
      • 3.001 Introduction to Statistics
        01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.003 Major Categories of Statistics
        01:34
      • 3.4 Statistical Analysis Considerations
      • 3.005 Population and Sample
        02:15
      • 3.6 Statistical Analysis Process
      • 3.007 Data Distribution
        01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.010 Histogram
        03:59
      • 3.11 Knowledge Check
      • 3.012 Testing
        08:18
      • 3.13 Knowledge Check
      • 3.014 Correlation and Inferential Statistics
        02:57
      • 3.15 Quiz
      • 3.016 Key Takeaways
        01:31
    • Lesson 04 - Python Environment Setup and Essentials

      23:58
      • 4.001 Anaconda
        02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.003 Data Types with Python
        13:28
      • 4.004 Basic Operators and Functions
        06:26
      • 4.5 Quiz
      • 4.006 Key Takeaways
        01:10
    • Lesson 05 - Mathematical Computing with Python (NumPy)

      30:31
      • 5.001 Introduction to Numpy
        05:30
      • 5.2 Activity-Sequence it Right
      • 5.003 Demo 01-Creating and Printing an ndarray
        04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.006 Basic Operations
        07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.009 Mathematical Functions of Numpy
        05:01
      • 5.10 Assignment 01
      • 5.011 Assignment 01 Demo
        03:55
      • 5.12 Assignment 02
      • 5.013 Assignment 02 Demo
        03:16
      • 5.14 Quiz
      • 5.015 Key Takeaways
        00:55
    • Lesson 06 - Scientific computing with Python (Scipy)

      23:35
      • 6.001 Introduction to SciPy
        06:57
      • 6.002 SciPy Sub Package - Integration and Optimization
        05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.005 Demo - Calculate Eigenvalues and Eigenvector
        01:36
      • 6.6 Knowledge Check
      • 6.007 SciPy Sub Package - Statistics, Weave and IO
        05:46
      • 6.8 Assignment 01
      • 6.009 Assignment 01 Demo
        01:20
      • 6.10 Assignment 02
      • 6.011 Assignment 02 Demo
        00:55
      • 6.12 Quiz
      • 6.013 Key Takeaways
        01:10
    • Lesson 07 - Data Manipulation with Pandas

      47:34
      • 7.001 Introduction to Pandas
        12:29
      • 7.2 Knowledge Check
      • 7.003 Understanding DataFrame
        05:31
      • 7.004 View and Select Data Demo
        05:34
      • 7.005 Missing Values
        03:16
      • 7.006 Data Operations
        09:56
      • 7.7 Knowledge Check
      • 7.008 File Read and Write Support
        00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.010 Pandas Sql Operation
        02:00
      • 7.11 Assignment 01
      • 7.012 Assignment 01 Demo
        04:09
      • 7.13 Assignment 02
      • 7.014 Assignment 02 Demo
        02:34
      • 7.15 Quiz
      • 7.016 Key Takeaways
        01:34
    • Lesson 08 - Machine Learning with Scikit–Learn

      1:02:10
      • 8.001 Machine Learning Approach
        03:57
      • 8.002 Steps 1 and 2
        01:00
      • 8.3 Steps 3 and 4
      • 8.004 How it Works
        01:24
      • 8.005 Steps 5 and 6
        01:54
      • 8.006 Supervised Learning Model Considerations
        00:30
      • 8.7 Knowledge Check
      • 8.008 Scikit-Learn
        02:10
      • 8.9 Knowledge Check
      • 8.010 Supervised Learning Models - Linear Regression
        11:19
      • 8.011 Supervised Learning Models - Logistic Regression
        08:43
      • 8.012 Unsupervised Learning Models
        10:40
      • 8.013 Pipeline
        02:37
      • 8.014 Model Persistence and Evaluation
        05:45
      • 8.16 Assignment 01
      • 8.15 Knowledge Check
      • 8.017 Assignment 01
        05:45
      • 8.18 Assignment 02
      • 8.019 Assignment 02
        05:14
      • 8.20 Quiz
      • 8.021 Key Takeaways
        01:12
    • Lesson 09 - Natural Language Processing with Scikit Learn

      49:03
      • 9.001 NLP Overview
        10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge Check
      • 9.004 NLP Libraries-Scikit
        12:29
      • 9.5 Extraction Considerations
      • 9.006 Scikit Learn-Model Training and Grid Search
        10:17
      • 9.7 Assignment 01
      • 9.008 Demo Assignment 01
        06:32
      • 9.9 Assignment 02
      • 9.010 Demo Assignment 02
        08:00
      • 9.11 Quiz
      • 9.012 Key Takeaway
        01:03
    • Lesson 10 - Data Visualization in Python using matplotlib

      32:43
      • 10.001 Introduction to Data Visualization
        08:01
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.004 (x,y) Plot and Subplots
        10:01
      • 10.5 Knowledge Check
      • 10.006 Types of Plots
        09:32
      • 10.7 Assignment 01
      • 10.008 Assignment 01 Demo
        02:23
      • 10.9 Assignment 02
      • 10.010 Assignment 02 Demo
        01:47
      • 10.11 Quiz
      • 10.012 Key Takeaways
        00:59
    • Lesson 11 - Web Scraping with BeautifulSoup

      52:26
      • 11.001 Web Scraping and Parsing
        12:50
      • 11.2 Knowledge Check
      • 11.003 Understanding and Searching the Tree
        12:56
      • 11.4 Navigating options
      • 11.005 Demo3 Navigating a Tree
        04:22
      • 11.6 Knowledge Check
      • 11.007 Modifying the Tree
        05:37
      • 11.008 Parsing and Printing the Document
        09:05
      • 11.9 Assignment 01
      • 11.010 Assignment 01 Demo
        01:55
      • 11.11 Assignment 02
      • 11.012 Assignment 02 demo
        04:57
      • 11.13 Quiz
      • 11.014 Key takeaways
        00:44
    • Lesson 12 - Python integration with Hadoop MapReduce and Spark

      40:39
      • 12.001 Why Big Data Solutions are Provided for Python
        04:55
      • 12.2 Hadoop Core Components
      • 12.003 Python Integration with HDFS using Hadoop Streaming
        07:20
      • 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count
        08:52
      • 12.5 Knowledge Check
      • 12.006 Python Integration with Spark using PySpark
        07:43
      • 12.007 Demo 02 - Using PySpark to Determine Word Count
        04:12
      • 12.8 Knowledge Check
      • 12.9 Assignment 01
      • 12.010 Assignment 01 Demo
        02:47
      • 12.11 Assignment 02
      • 12.012 Assignment 02 Demo
        03:30
      • 12.13 Quiz
      • 12.014 Key takeaways
        01:20
    • Practice Projects

      • IBM HR Analytics Employee Attrition Modeling.
    • Math Refresher

      30:36
      • Math Refresher
        30:36
    • Lesson 1 Introduction

      02:55
      • 1.1 Introduction
        02:55
    • Lesson 2 Sample or population data

      03:56
      • 2.1 Sample or population data
        03:56
    • Lesson 3 The fundamentals of descriptive statistics

      21:18
      • 3.1 The fundamentals of descriptive statistics
        03:18
      • 3.2 Levels of measurement
        02:57
      • 3.3 Categorical variables. Visualization techniques for categorical variables
        04:06
      • 3.4 Numerical variables. Using a frequency distribution table
        03:24
      • 3.5 Histogram charts
        02:27
      • 3.6 Cross tables and scatter plots
        05:06
    • Lesson 4 Measures of central tendency, asymmetry, and variability

      25:17
      • 4.1 Measures of central tendency, asymmetry, and variability
        04:24
      • 4.2 Measuring skewness
        02:43
      • 4.3 Measuring how data is spread out calculating variance
        05:58
      • 4.4 Standard deviation and coefficient of variation
        04:54
      • 4.5 Calculating and understanding covariance
        03:31
      • 4.6 The correlation coefficient
        03:47
    • Lesson 5 Practical example descriptive statistics

      14:30
      • 5.1 Practical example descriptive statistics
        14:30
    • Lesson 6 Distributions

      16:17
      • 6.1 Distributions
        01:02
      • 6.2 What is a distribution
        03:40
      • 6.3 The Normal distribution
        03:45
      • 6.4 The standard normal distribution
        02:51
      • 6.5 Understanding the central limit theorem
        03:40
      • 6.6 Standard error
        01:19
    • Lesson 7 Estimators and Estimates

      23:36
      • 7.1 Estimators and Estimates
        02:36
      • 7.2 Confidence intervals - an invaluable tool for decision making
        06:31
      • 7.3 Calculating confidence intervals within a population with a known variance
        02:30
      • 7.4 Student’s T distribution
        03:14
      • 7.5 Calculating confidence intervals within a population with an unknown variance
        04:07
      • 7.6 What is a margin of error and why is it important in Statistics
        04:38
    • Lesson 8 Confidence intervals advanced topics

      14:27
      • 8.1 Confidence intervals advanced topics
        04:47
      • 8.2 Calculating confidence intervals for two means with independent samples (part One)
        04:36
      • 8.3 Calculating confidence intervals for two means with independent samples (part two)
        03:40
      • 8.4 Calculating confidence intervals for two means with independent samples (part three)
        01:24
    • Lesson 9 Practical example inferential statistics

      09:37
      • 9.1 Practical example inferential statistics
        09:37
    • Lesson 10 Hypothesis testing Introduction

      12:36
      • 10.1 Hypothesis testing Introduction
        04:56
      • 10.2 Establishing a rejection region and a significance level
        04:20
      • 10.3 Type I error vs Type II error
        03:20
    • Lesson 11 Hypothesis testing Let's start testing!

      26:39
      • 11.1 Hypothesis testing Let's start testing!
        06:07
      • 11.2 What is the p-value and why is it one of the most useful tool for statisticians
        03:55
      • 11.3 Test for the mean. Population variance unknown
        04:26
      • 11.4 Test for the mean. Dependent samples
        04:45
      • 11.5 Test for the mean. Independent samples (Part One)
        03:38
      • 11.6 Test for the mean. Independent samples (Part Two)
        03:48
    • Lesson 12 Practical example hypothesis testing

      06:31
      • 12.1 Practical example hypothesis testing
        06:31
    • Lesson 13 The fundamentals of regression analysis

      18:32
      • 13.1 The fundamentals of regression analysis
        01:02
      • 13.2 Correlation and causation
        04:06
      • 13.3 The linear regression model made easy
        05:02
      • 13.4 What is the difference between correlation and regression
        01:28
      • 13.5 A geometrical representation of the linear regression model
        01:18
      • 13.6 A practical example - Reinforced learning
        05:36
    • Lesson 14 Subtleties of regression analysis

      23:25
      • 14.1 Subtleties of regression analysis
        02:04
      • 14.2 What is Rsquared and how does it help us
        05:00
      • 14.3 The ordinary least squares setting and its practical applications
        02:08
      • 14.4 Studying regression tables
        04:34
      • 14.5 The multiple linear regression model
        02:42
      • 14.6 Adjusted R-squared
        04:57
      • 14.7 What does the F-statistic show us and why we need to understand it
        02:00
    • Lesson 15 Assumptions for linear regression analysis

      19:16
      • 15.1 Assumptions for linear regression analysis
        02:11
      • 15.2 Linearity
        01:40
      • 15.3 No endogeneity
        03:43
      • 15.4 Normality and homoscedasticity
        05:09
      • 15.5 No autocorrelation
        03:11
      • 15.6 No multicollinearity
        03:22
    • Lesson 16 Dealing with categorical data

      05:20
      • 16.1 Dealing with categorical data
        05:20
    • Lesson 17 Practical example regression analysis

      14:42
      • 17.1 Practical example regression analysis
        14:42
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Machine Learning Exam & Certification

  • Who provides the certificate and how long is it valid for?

    Upon successful completion of this course, Simplilearn will provide you with an industry-recognized course completion certificate which has a lifelong validity.

  • How do I become a Machine Learning expert?

    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 expert. You will earn Simplilearn’s  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.  

     


     

  • What do I need to do to unlock my Simplilearn certificate?

    Online Classroom:

    • Attend one complete batch
    • Submit at least one completed project.

    Online Self-Learning:

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

  • Do you provide any practice tests as part of this course?

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

Maching Learning Course Advisor

Mike Tamir
Mike Tamir No. 1 AI and Machine Learning Influencer, Head of Data Science - Uber ATG

​Named by Onalytica as the No.1 influencer in AI and Machine Learning space, Mike serves as Head of Data Science for Uber ATG self-driving engineering team and as UC Berkeley data science faculty.

Machine Learning Course Reviews

Gary Grewal
Gary Grewal Atlanta

It was a fantastic experience to go through Simplilearn for Machine Learning. This is a course that I would recommend to my friends and colleagues. The instructors, especially Mr. Bhupendra, are extremely knowledgable and have vast experience which helps to distill that and give us concrete steps. There are always a number of ways to solve a problem, but sometimes you need concrete steps where other times, you need to discover it for yourself - making the course very well balanced. The tools given in the course prepared me very well to apply this practice in my job. Thanks!

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Trupti Joshi
Trupti Joshi Pune

Nowadays, Machine Learning is a "BUZZ" word – very catchy and bit scary for people who don’t know much about it. The Simplilearn team makes sure people from any background can complete the course with a perfect understanding. They have designed this course in a very detailed manner so that students get comfortable with the subject quickly. My trainer, Mr. Bhupendra Sinha Sir, explained every topic rigorously. Recorded WebEx sessions were helpful if I missed class.

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Ashok Kumar Kothandapani
Ashok Kumar Kothandapani Chennai

Simplilearn’s trainers are patient, clearing any confusion and answering all questions without impacting the course timeline. Simplilearn is the most convenient platform for those who want to grow in the fields of Data Analytics and Data Science.

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Jaya Raghavendra
Jaya Raghavendra Mumbai

I am a B.Sc Computers graduate. I had always attended physical classroom sessions, but this is the first time I experienced online classes. Simplilearn allowed learning from different mentors. Big thanks to the support team.

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Asmita Wankhade
Asmita Wankhade Warangal

Course content is excellent. You can learn and understand, even if you are only a beginner. I am delighted to have joined and successfully finished the 'Certified Machine Learning’course. All thanks to Simplilearn.

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Sarita Visavalia
Sarita Visavalia Gyanam, Anand

I have enrolled in Data Science using Python and Machine Learning courses on Simplilearn. Course content is very well structured and relates to the current trends in technology and markets, as well. Each session is designed with real-time examples. Instructor-led training has given us excellent exposure. The trainer provides an understanding of all topics through practical exercises. The support team is very responsive. Thanks to Simplilearn team for the wonderful platform provided.

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Mahesh Gaonkar
Mahesh Gaonkar Software Engineer, Bangalore

Simplilearn is a great start for the beginner as well for the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas on the concepts and exercises. I could finish my Machine Learning advance course very easily with good project exercise.

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Somil Gadhwal
Somil Gadhwal Application Engineer, Hyderabad

Simplilearn's course content is designed in a way that every session is closely connected to the next. There is no need to mug up the lessons. The instructors put thought into training and motivating students. I am really happy I joined the course.

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Afrid Mondal
Afrid Mondal Nagpur

The training was fantastic. Thank you for providing a great platform to learn.

Roveena Sebastian
Roveena Sebastian R & D Manager, Bangalore

It was a great learning experience. The hands-on assignments and the various resources provided by Simplilearn is excellent. The live sessions were simply awesome. The trainer was so successful in keeping the remote class active, covered all topics in such a short span and majorly ensured that the entire class was moving forward together in spite of the known time constraints. My special thanks to the trainer for his interest and dedication. Thanks once again to Simplilearn.

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Deboleena Paul
Deboleena Paul Solution Architect, Meerut

I really liked the trainer. He is very patient, well organized, and interactive. I had an awesome learning experience with Simplilearn that was beyond my expectation for an online classroom.

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Happy Snehal
Happy Snehal Data Science Intern at ABB, Bangalore

My experience with Simplilearn has been amazing. This is the fifth course that I have done from here and all the courses provide the best quality knowledge. Machine Learning course content was wide and deep. It covered algorithms, Python programming, Mathematics, and Statistics. It also provided project support. My customer support experience has been fantastic as within seconds or minutes, I have been provided with solutions and all my issues have been resolved to my full satisfaction. The faculties are well educated, well experienced, humble, kind and eager to teach things.

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Aditi Dalal
Aditi Dalal Analyst (Data Analytics) at The Smart Cube, Noida

I have enrolled in Machine Learning from Simplilearn. The content of the course is elaborate and easy to understand. The faculty has clarity in his way of explaining, maintains a very good balance between theory and the practical process. It has been a great learning experience for me.

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Siddhant Vibhute
Siddhant Vibhute M.Tech Scholar at VJTI, Mumbai

Simplilearn provides a platform to explore the subject in depth. The way it connects every problem with the real world makes the subject even more interesting. The trainers and support staff act promptly to each query with every possible help. Machine Learning course is definitely one of my best experiences and is highly recommended for every data scientist aspirant.

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Ujjwal Seth
Ujjwal Seth Data Analyst at Hewlett Packard Enterprise, Chennai

I have completed Machine Learning certification recently from Simplilearn. It felt amused how I was able to this skill when I was finding it super-hard when learning through some of the other online platforms. No Doubt that I feel Simplilearn is the Best Online Platform for learning Computer Science Skills! The Online Lab access gives complete tech resources using which you can execute Computer code and don't need to install the software on your laptop. The whole system is both simplistic and 1uality wise absolutely to the point and makes the user experience simple and beautiful. The content of the course was interesting and it used a lot of real-life application which helped me to understand better. The customer support was very supportive and always ready to help us. In fact, they always assured that our problem will be solved and the response was quick. Hence, A curious mind should not miss a chance to enroll in his preferred course at Simplilearn.

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    Machine Learning Training FAQs

    • What is Machine Learning?

      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.
       

    • How will the labs be conducted?

      Simplilearn provides Integrated labs for all the hands-on execution of projects. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.
       
        

    • Is this live training, or will I watch pre-recorded videos?

      If you enroll for self-paced e-learning, you will have access to pre-recorded videos. If you enroll for the online classroom Flexi Pass, you will have access to live training conducted online as well as the pre-recorded videos.

    • What if I miss a class?

      Simplilearn provides recordings of each class so you can review them as needed before the next session. With Flexi-pass, Simplilearn gives you access to all classes for 90 days so that you have the flexibility to choose sessions as per your convenience.

    • Who are the instructors and how are they selected?

      All of our highly qualified trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

    • What is Global Teaching Assistance?

      Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

    • What is online classroom training?

      Online classroom training for Machine Learning Certification is conducted via online live streaming of each class. The classes are conducted by a Machine Learning certified trainer with more than 15 years of work and training experience.
       

    • What is covered under the 24/7 Support promise?

      We offer 24/7 support through email, chat, and telephone. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.
       

    • How do I enroll in this online training?

      You can enroll in this training on our website and make an online payment using any of the following options:

      • Visa Credit or Debit Card
      • MasterCard
      • American Express
      • Diner’s Club
      • PayPal

      Once payment is received you will automatically receive a payment receipt and access information via email.

       


       

    • If I need to cancel my enrollment, can I get a refund?

      Yes, you can cancel your enrolment if necessary. We will refund the course price after deducting an administrative fee. To learn more, please read our Refund Policy.

        

    • Do you provide a money back guarantee for the training programs?

      Yes. We do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support portal.
       

    • Who can I contact to learn more about this Machine Learning course?

      Please contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

    • * Disclaimer

      * The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

    Our London Correspondence / Mailing address

    Kemp House, 152 - 160 City Road, London EC1V 2NX, United Kingdom

    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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