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Machine Learning Certification Training

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Explore the concepts of Machine Learning and understand how it’s transforming the digital world. An exciting branch of Artificial Intelligence, this Machine Learning certification online course will provide the skills you need to become a Machine Learning Engineer and unlock the power of this emerging field.
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Eligibility & Pre-requisites

  • Eligibility

    The Machine Learning certification online course is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become 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. You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science, before getting into the Machine Learning online course.

Machine Learning Course Overview

This Machine Learning online 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.

Benefits

The Machine Learning market is expected to reach USD $8.81 Billion by 2022, at a growth rate of 44.1-percent, indicating the increased adoption of Machine Learning among companies. By 2020, the demand for Machine Learning engineers is expected to grow by 60-percent.

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      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

      Training Options

      Batches

      (Online, In-Class)​

      One-on-One (Recommended)

      (Online, In-Class)​

      CORPORATE TRAINING

      (Online, Client sight)

      Customized to your team's needs

      Course Currilcum

      • Lesson 01 Course Introduction

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

        19:36Preview
        • 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

        35:57Preview
        • 3.1 Learning Objectives
          00:38
        • 3.2 Data Exploration Loading Files: Part A
          02:52
        • 3.2 Data Exploration Loading Files: Part B
          01:34
        • 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

        01:21:04Preview
        • 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

        27:52Preview
        • 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

        55:43Preview
        • 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

        28:26Preview
        • 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

        37:44Preview
        • 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

        35:41Preview
        • 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

        25:45Preview
        • 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

        43:58Preview
        • 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
        • 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

      • Math Refresher

        30:36Preview
        • Math Refresher
          30:36

      • Lesson 1 Introduction

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

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

        21:18Preview
        • 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

        25:17Preview
        • 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

        23:36Preview
        • 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!

        26:39Preview
        • 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

        18:32Preview
        • 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

        23:25Preview
        • 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

        19:16Preview
        • 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

        05:20Preview
        • 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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          VoiSAP’s certification training is recognized by more than 500  top MNCs, including CGI, Accenture, Walmart, Amazon, IMAX, Sony, RBC, HSBC, Standard Chartered Bank, IBM, Infosys, Lafarge, TCS, and many more.

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          SAP FICO Training 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 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.

          Simplilearn’s Machine Learning certification course is designed by subject matter experts who know what skills are most valued by employers. Topics like types of machine learning, time series modeling, regression, classification, clustering, and deep learning basics are thoroughly covered, and allow you to start a career in this field.

          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.

          Some of the top job roles in the field of machine learning are data scientists, machine learning engineers, NLP Scientists, computer vision engineers, and data architects. This Machine Learning course gives you all the necessary skills to become eligible for such roles.