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

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Course Currilcum

Section 01 Machine Learning

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

Section 02 Math Refresher

Math Refresher
30:36Preview
- Math Refresher
  30:36

Section 03 Statistics Essential for Data Science

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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SAP FICO 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.

What are the different types of Machine Learning?

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.

Why should you take this Selenium Certification training?Does Machine Learning require coding?

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.

Will this certification course help me to build a career in Machine Learning?

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.

What is the best language for Machine Learning?

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.

What are the job roles in Machine Learning?

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.