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Artificial Intelligence Engineer

Course Description

This Artificial Intelligence Engineer Master’s Program, in collaboration with IBM, gives training on the skills required to become a successful Artificial Intelligence Engineer. Throughout this exclusive online course, you’ll master Deep Learning, Machine Learning, and the programming languages required to excel in this domain and kick-start your career in Artificial Intelligence.

About the Program

About the Artificial Intelligence Engineer Master’s program developed in collaboration with IBM

IBM is the second-largest Predictive Analytics and Machine Learning solutions provider globally (source: The Forrester Wave report, September 2018). A joint partnership with Simplilearn and IBM introduces students to integrated blended learning, making them experts in Artificial Intelligence and Data Science. The AI courses designed in collaboration with IBM will make students industry-ready for Artificial Intelligence and Data Science job roles. IBM is a leading cognitive solutions and cloud platform company, headquartered in Armonk, New York, offering a plethora of technology and consulting services. Each year, IBM invests $6 billion in research and development and has achieved five Nobel prizes, nine US National Medals of Technology, five US National Medals of Science, six Turing Awards, and 10 Inductions in US Inventors Hall of Fame.

What can I expect from this Simplilearn program developed in collaboration with IBM?

  • Upon completion of this AI Engineer Master’s Program, you will receive the certificates from IBM and Simplilearn in the Artificial Intelligence courses on the learning path*. These certificates will testify to your skills as an expert in Artificial Intelligence. You will also receive the following:
  • USD 1200 worth of IBM cloud credits that you can leverage for hands-on exposure
  • Access to IBM cloud platforms featuring IBM Watson and other software for 24/7 practice
  • Industry-recognized Master’s Certificate from Simplilearn

What are the learning objectives of this Artificial Intelligence program?

This co-developed Simplilearn and IBM Artificial Intelligence Engineer Master’s Program is a blend of Artificial Intelligence, Data Science, Machine Learning, and Deep Learning, enabling the real-world implementation of advanced tools and models. The program is designed to give you in-depth knowledge of Artificial Intelligence concepts including the essentials of statistics required for Data Science, Python programming, and Machine Learning. Through these AI courses, you will learn how to use Python libraries like NumPy, SciPy, Scikit, and essential Machine Learning techniques, such as supervised and unsupervised learning, advanced concepts covering artificial neural networks and layers of data abstraction, and TensorFlow.

Artificial intelligence and Machine Learning will impact all segments of daily life by 2025, with applications in a wide range of industries such as healthcare, transportation, insurance, transport and logistics, and customer service. A role in this domain places you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond.

Why become an AI Engineer?

The current and future demand is staggering. The New York Times reports candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for AI Engineers with the required skills

What skills will be covered in this Artificial Intelligence program?

By the end of this AI training, you will be able to accomplish the following:

  • Understand the meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence
  • Design and build your own intelligent agents, applying them to create practical Artificial Intelligence projects, including games, machine learning models, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, and agent decision-making functions
  • Master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and functions
  • Learn how to write your own Python scripts and perform basic hands-on data analysis using Jupyter notebook
  • Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing
  • Perform high-level mathematical and technical computing using the NumPy and SciPy packages and data analysis with the Pandas package
  • Master the concepts of supervised and unsupervised learning models, including linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline, recommendation engine, and time series modeling
  • Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
  • Master advanced topics in Artificial Intelligence, such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces

What projects are included in this Artificial Intelligence program?

This Artificial Intelligence Engineer Master’s program co-developed with IBM includes over 15 real-life, branded projects in different domains. These projects are designed to help you master the key concepts of Artificial Intelligence like supervised and unsupervised learning, reinforcement learning, support vector machines, Deep Learning, TensorFlow, neural networks, convolutional neural networks, and recurrent neural networks.

This AI Engineer Master’s Program includes a capstone project allowing you to revisit the concepts learned throughout the courses. You will go through dedicated mentored classes in order to create a high-quality industry project, solving a real-world problem. The capstone project will cover key aspects from exploratory data analysis to model creation and fitting. To complete this capstone project, you will use cutting edge Artificial Intelligence-based supervised and unsupervised algorithms like Regression, Multinomial Naïve Bayes, SVM, Tree-based algorithms, and NLP in the domain of your choice. After successful submission of the project, not only will you be awarded a capstone certificate but you will have a project that can be showcased to potential employers as a testament to your learning.

Project 1: Fare Prediction for Uber | Domain: Delivery (Commerce)

Uber, one of the largest US-based taxi providers, 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: Products rating prediction for Amazon | Domain: E-commerce

Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.

Project 4: Demand Forecasting for Walmart | Domain: Sales

Predict accurate sales for 45 stores of Walmart, one of the leading US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales.

Project 5: Improving customer experience for Comcast | Domain: Telecom

Comcast, one of the leading US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.

Project 6: Attrition Analysis for IBM | Domain: Workforce Analytics

IBM, one of the leading US-based IT companies, would like to identify the factors that influence the attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.

Project 7: NYC 311 Service Request Analysis | Domain: Telecommunication

Perform a service request data analysis of New York City 311 calls. You will focus on data wrangling techniques to understand patterns in the data and visualize the major complaint types.

Project 8: MovieLens Dataset Analysis | Domain: Engineering

The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. The researchers of this group are involved in several research projects in the fields of information filtering, collaborative filtering, and recommender systems. Here, we ask you to perform an analysis using the Exploratory Data Analysis technique for user datasets.

Project 9: Stock Market Data Analysis | Domain: Stock Market

As a part of this project, you will import data using Yahoo data reader from the following companies: Yahoo, Apple, Amazon, Microsoft, and Google. You will perform fundamental analytics, including plotting, closing price, plotting stock trade by volume, performing daily return analysis, and using pair plot to show the correlation between all of the stocks.

Who should take this Artificial Intelligence program?

With the demand for AI in a broad range of industries, Simplilearn’s AI course is well suited for a variety of roles and disciplines, including:

  • Developers aspiring to be an Artificial Intelligence Engineer or Machine Learning Engineer
  • Analytics Managers who are leading a team of analysts
  • Information Architects who want to gain expertise in Artificial Intelligence algorithms
  • Analytics professionals who want to work in machine learning or artificial intelligence
  • Graduates looking to build a career in Artificial Intelligence and machine learning
  • Experienced professionals who would like to harness Artificial Intelligence in their fields to get more insight

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Course Syllabus

Course 1

Introduction to Artificial Intelligence
This Introduction to Artificial Intelligence (AI) is designed to help learners decode the mystery of artificial intelligence and its business applications. This AI for beginners course provides an overview of AI concepts and workflows, machine learning, deep learning, and performance metrics.

Introduction to Artificial Intelligence

Lesson 00 – Course Introduction03:09
Lesson 01 – Decoding Artificial Intelligence21:09
01 Decoding Artificial Intelligence00:19
02 Meaning, Scope, and Stages Of Artificial Intelligence00:46
03 Three Stages of Artificial Intelligence02:19
04 Applications of Artificial Intelligence01:06
05 Image Recognition01:26
06 Applications of Artificial Intelligence – Examples01:06
07 Effects of Artificial Intelligence on Society03:54
08 Supervises Learning for Telemedicine03:10
09 Solves Complex Social Problems03:17
10 Benefits Multiple Industries02:39
11 Key Takeaways01:07
Knowledge Check
Lesson 02 – Fundamentals of Machine Learning and Deep Learning31:00
01 Fundamentals Of Machine Learning and Deep Learning00:50
02 Meaning of Machine Learning02:22
03 Relationship between Machine Learning and Statistical Analysis01:31
04 Process of Machine Learning01:32
05 Types of Machine Learning01:34
06 Meaning of Unsupervised Learning01:24
07 Meaning of Semi-supervised Learning01:59
08 Algorithms of Machine Learning01:00
09 Regression03:12
10 Naive Bayes01:09
11 Naive Bayes Classification02:45
12 Machine Learning Algorithms02:22
13 Deep Learning02:45
14 Artificial Neural Network Definition01:46
15 Definition of Perceptron01:18
16 Online and Batch Learning02:17
17 Key Takeaways01:14
Knowledge Check
Lesson 03 – Machine Learning Workflow14:52
01 Learning Objective00:28
02 Machine Learning Workflow01:06
03 Get more data00:41
04 Ask a Sharp Question02:07
05 Add Data to the Table03:20
06 Check for Quality01:15
07 Transform Features02:41
08 Answer the Questions01:49
09 Use the Answer00:31
11 Key takeaways00:54
Knowledge Check
Lesson 04 – Performance Metrics19:39
01 Performance Metrics00:31
02 Need For Performance Metrics01:10
03 Key Methods Of Performance Metrics00:53
04 Confusion Matrix Example01:15
05 Terms Of Confusion Matrix01:53
06 Minimize False Cases01:37
07 Minimize False Positive Example01:07
08 Accuracy01:42
09 Precision01:13
10 Recall Or Sensitivity02:32
11 Specificity01:36
12 F1 Score03:01
13 Key takeaways01:09
Knowledge Check

Course 2 Online Classroom Flexi Pass

Data Science with Python
The Data Science with Python course provides a complete overview of Data Analytics tools and techniques using Python. Learning Python is a crucial skill for many Data Science roles. Acquiring knowledge in Python will be the key to unlock your career as a Data Scientist.

Data Science with Python
Lesson 00 – Course Overview04:34
0.001 Course Overview04:34
Lesson 01 – Data Science Overview20:27
1.001 Introduction to Data Science08:42
1.002 Different Sectors Using Data Science05:59
1.003 Purpose and Components of Python05:02
1.4 Quiz
1.005 Key Takeaways00:44
Lesson 02 – Data Analytics Overview18:20
2.001 Data Analytics Process07:21
2.2 Knowledge Check
2.3 Exploratory Data Analysis(EDA)
2.4 EDA-Quantitative Technique
2.005 EDA – Graphical Technique00:57
2.006 Data Analytics Conclusion or Predictions04:30
2.007 Data Analytics Communication02:06
2.8 Data Types for Plotting
2.009 Data Types and Plotting02:29
2.11 Quiz
2.012 Key Takeaways00:57
2.10 Knowledge Check
Lesson 03 – Statistical Analysis and Business Applications23:53
3.001 Introduction to Statistics01:31
3.2 Statistical and Non-statistical Analysis
3.003 Major Categories of Statistics01:34
3.4 Statistical Analysis Considerations
3.005 Population and Sample02:15
3.6 Statistical Analysis Process
3.007 Data Distribution01:48
3.8 Dispersion
3.9 Knowledge Check
3.010 Histogram03:59
3.11 Knowledge Check
3.012 Testing08:18
3.13 Knowledge Check
3.014 Correlation and Inferential Statistics02:57
3.15 Quiz
3.016 Key Takeaways01:31
Lesson 04 – Python Environment Setup and Essentials23:58
4.001 Anaconda02:54
4.2 Installation of Anaconda Python Distribution (contd.)
4.003 Data Types with Python13:28
4.004 Basic Operators and Functions06:26
4.5 Quiz
4.006 Key Takeaways01:10
Lesson 05 – Mathematical Computing with Python (NumPy)30:31
5.001 Introduction to Numpy05:30
5.2 Activity-Sequence it Right
5.003 Demo 01-Creating and Printing an ndarray04:50
5.4 Knowledge Check
5.5 Class and Attributes of ndarray
5.006 Basic Operations07:04
5.7 Activity-Slice It
5.8 Copy and Views
5.009 Mathematical Functions of Numpy05:01
5.010 Analyse GDP of Countries
5.011 Assignment 01 Demo03:55
5.012 Analyse London Olympics Dataset
5.013 Assignment 02 Demo03:16
5.14 Quiz
5.015 Key Takeaways00:55
Lesson 06 – Scientific computing with Python (Scipy)23:32
6.001 Introduction to SciPy06:57
6.002 SciPy Sub Package – Integration and Optimization05:51
6.3 Knowledge Check
6.4 SciPy sub package
6.005 Demo – Calculate Eigenvalues and Eigenvector01:36
6.6 Knowledge Check
6.007 SciPy Sub Package – Statistics, Weave and IO05:46
6.008 Solving Linear Algebra problem using SciPy
6.009 Assignment 01 Demo01:20
6.010 Perform CDF and PDF using Scipy
6.011 Assignment 02 Demo00:52
6.12 Quiz
6.013 Key Takeaways01:10
Lesson 07 – Data Manipulation with Pandas47:34
7.001 Introduction to Pandas12:29
7.2 Knowledge Check
7.003 Understanding DataFrame05:31
7.004 View and Select Data Demo05:34
7.005 Missing Values03:16
7.006 Data Operations09:56
7.7 Knowledge Check
7.008 File Read and Write Support00:31
7.9 Knowledge Check-Sequence it Right
7.010 Pandas Sql Operation02:00
7.011 Analyse the Federal Aviation Authority Dataset using Pandas
7.012 Assignment 01 Demo04:09
7.013 Analyse NewYork city fire department Dataset
7.014 Assignment 02 Demo02:34
7.15 Quiz
7.016 Key Takeaways01:34
Lesson 08 – Machine Learning with Scikit–Learn01:02:10
8.001 Machine Learning Approach03:57
8.002 Steps One and Two01:00
8.3 Steps Three and Four
8.004 How it Works01:24
8.005 Steps Five and Six01:54
8.006 Supervised Learning Model Considerations00:30
8.008 ScikitLearn02:10
8.010 Supervised Learning Models – Linear Regression11:19
8.011 Supervised Learning Models – Logistic Regression08:43
8.012 Unsupervised Learning Models10:40
8.013 Pipeline02:37
8.014 Model Persistence and Evaluation05:45
8.15 Knowledge Check
8.016 Analysing Ad Budgets for different media channels
8.017 Assignment One05:45
8.018 Building a model to predict Diabetes
8.019 Assignment Two05:14
Knowledge Check
8.021 Key Takeaways01:12
Lesson 09 – Natural Language Processing with Scikit Learn49:03
9.001 NLP Overview10:42
9.2 NLP Applications
9.3 Knowledge Check
9.004 NLP Libraries-Scikit12:29
9.5 Extraction Considerations
9.006 Scikit Learn-Model Training and Grid Search10:17
9.007 Analysing Spam Collection Data
9.008 Demo Assignment 0106:32
9.009 Sentiment Analysis using NLP
9.010 Demo Assignment 0208:00
9.11 Quiz
9.012 Key Takeaway01:03
Lesson 10 – Data Visualization in Python using matplotlib32:43
10.001 Introduction to Data Visualization08:01
10.2 Knowledge Check
10.3 Line Properties
10.004 (x,y) Plot and Subplots10:01
10.5 Knowledge Check
10.006 Types of Plots09:32
10.007 Draw a pair plot using seaborn library
10.008 Assignment 01 Demo02:23
10.009 Analysing Cause of Death
10.010 Assignment 02 Demo01:47
10.11 Quiz
10.012 Key Takeaways00:59
Lesson 11 – Web Scraping with BeautifulSoup52:26
11.001 Web Scraping and Parsing12:50
11.2 Knowledge Check
11.003 Understanding and Searching the Tree12:56
11.4 Navigating options
11.005 Demo3 Navigating a Tree04:22
11.6 Knowledge Check
11.007 Modifying the Tree05:37
11.008 Parsing and Printing the Document09:05
11.009 Web Scraping of Simplilearn Website
11.010 Assignment 01 Demo01:55
11.011 Web Scraping of Simplilearn Website Resource page
11.012 Assignment 02 demo04:57
11.13 Quiz
11.014 Key takeaways00:44
Lesson 12 – Python integration with Hadoop MapReduce and Spark40:39
12.001 Why Big Data Solutions are Provided for Python04:55
12.2 Hadoop Core Components
12.003 Python Integration with HDFS using Hadoop Streaming07:20
12.004 Demo 01 – Using Hadoop Streaming for Calculating Word Count08:52
12.5 Knowledge Check
12.006 Python Integration with Spark using PySpark07:43
12.007 Demo 02 – Using PySpark to Determine Word Count04:12
12.8 Knowledge Check
12.009 Determine the word count
12.010 Assignment 01 Demo02:47
12.011 Display all the airports based in New York using PySpark
12.012 Assignment 02 Demo03:30
12.13 Quiz
12.014 Key takeaways01:20
Practice Projects
IBM HR Analytics Employee Attrition Modeling.

Course 3 Online Classroom Flexi Pass

Machine Learning
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.

Machine Learning

Lesson 01 Course Introduction06:41
Course Introduction05:31
Accessing Practice Lab01:10
Lesson 02 Introduction to AI and Machine Learning19:36
2.1 Learning Objectives00:43
2.2 Emergence of Artificial Intelligence01:56
2.3 Artificial Intelligence in Practice01:48
2.4 Sci-Fi Movies with the Concept of AI00:22
2.5 Recommender Systems00:45
2.6 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A02:47
2.7 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B01:23
2.8 Definition and Features of Machine Learning01:30
2.9 Machine Learning Approaches01:48
2.10 Machine Learning Techniques02:21
2.11 Applications of Machine Learning: Part A01:34
2.12 Applications of Machine Learning: Part B02:11
2.13 Key Takeaways00:28
Knowledge Check
Lesson 03 Data Preprocessing35:57
3.1 Learning Objectives00:38
3.2 Data Exploration Loading Files: Part A02:52
3.2 Data Exploration Loading Files: Part B01:34
3.3 Demo: Importing and Storing Data01:27
Practice: Automobile Data Exploration – A
3.4 Data Exploration Techniques: Part A02:56
3.5 Data Exploration Techniques: Part B02:47
3.6 Seaborn02:18
3.7 Demo: Correlation Analysis02:38
Practice: Automobile Data Exploration – B
3.8 Data Wrangling01:27
3.9 Missing Values in a Dataset01:55
3.10 Outlier Values in a Dataset01:49
3.11 Demo: Outlier and Missing Value Treatment04:18
Practice: Data Exploration – C
3.12 Data Manipulation00:47
3.13 Functionalities of Data Object in Python: Part A01:49
3.14 Functionalities of Data Object in Python: Part B01:33
3.15 Different Types of Joins01:32
3.16 Typecasting01:23
3.17 Demo: Labor Hours Comparison01:54
Practice: Data Manipulation
3.18 Key Takeaways00:20
Knowledge Check
Storing Test Results
Lesson 04 Supervised Learning01:21:04
4.1 Learning Objectives00:31
4.2 Supervised Learning02:17
4.3 Supervised Learning- Real-Life Scenario00:53
4.4 Understanding the Algorithm00:52
4.5 Supervised Learning Flow01:50
4.6 Types of Supervised Learning: Part A01:54
4.7 Types of Supervised Learning: Part B02:03
4.8 Types of Classification Algorithms01:01
4.9 Types of Regression Algorithms: Part A03:20
4.10 Regression Use Case00:34
4.11 Accuracy Metrics01:23
4.12 Cost Function01:48
4.13 Evaluating Coefficients00:53
4.14 Demo: Linear Regression13:47
Practice: Boston Homes – A
4.15 Challenges in Prediction01:45
4.16 Types of Regression Algorithms: Part B02:40
4.17 Demo: Bigmart21:55
Practice: Boston Homes – B
4.18 Logistic Regression: Part A01:58
4.19 Logistic Regression: Part B01:38
4.20 Sigmoid Probability02:05
4.21 Accuracy Matrix01:36
4.22 Demo: Survival of Titanic Passengers14:07
Practice: Iris Species
4.23 Key Takeaways00:14
Knowledge Check
Health Insurance Cost
Lesson 05 Feature Engineering27:52
5.1 Learning Objectives00:27
5.2 Feature Selection01:28
5.3 Regression00:53
5.4 Factor Analysis01:57
5.5 Factor Analysis Process01:05
5.6 Principal Component Analysis (PCA)02:31
5.7 First Principal Component02:43
5.8 Eigenvalues and PCA02:32
5.9 Demo: Feature Reduction05:47
Practice: PCA Transformation
5.10 Linear Discriminant Analysis02:27
5.11 Maximum Separable Line00:44
5.12 Find Maximum Separable Line03:12
5.13 Demo: Labeled Feature Reduction01:53
Practice: LDA Transformation
5.14 Key Takeaways00:13
Knowledge Check
Simplifying Cancer Treatment
Lesson 06 Supervised Learning Classification55:43
6.1 Learning Objectives00:34
6.2 Overview of Classification02:05
Classification: A Supervised Learning Algorithm00:52
6.4 Use Cases of Classification02:37
6.5 Classification Algorithms00:16
6.6 Decision Tree Classifier02:17
6.7 Decision Tree Examples01:45
6.8 Decision Tree Formation00:47
6.9 Choosing the Classifier02:55
6.10 Overfitting of Decision Trees01:00
6.11 Random Forest Classifier- Bagging and Bootstrapping02:22
6.12 Decision Tree and Random Forest Classifier01:06
Performance Measures: Confusion Matrix02:21
Performance Measures: Cost Matrix02:06
6.15 Demo: Horse Survival08:30
Practice: Loan Risk Analysis
6.16 Naive Bayes Classifier01:28
6.17 Steps to Calculate Posterior Probability: Part A01:44
6.18 Steps to Calculate Posterior Probability: Part B02:21
6.19 Support Vector Machines : Linear Separability01:05
6.20 Support Vector Machines : Classification Margin02:05
6.21 Linear SVM : Mathematical Representation02:04
6.22 Non-linear SVMs01:06
6.23 The Kernel Trick01:19
6.24 Demo: Voice Classification10:42
Practice: College Classification
6.25 Key Takeaways00:16
Knowledge Check
Classify Kinematic Data
Lesson 07 Unsupervised Learning28:26
7.1 Learning Objectives00:29
7.2 Overview01:48
7.3 Example and Applications of Unsupervised Learning02:17
7.4 Clustering01:49
7.5 Hierarchical Clustering02:28
7.6 Hierarchical Clustering Example02:01
7.7 Demo: Clustering Animals05:39
Practice: Customer Segmentation
7.8 K-means Clustering01:46
7.9 Optimal Number of Clusters01:24
7.10 Demo: Cluster Based Incentivization08:32
Practice: Image Segmentation
7.11 Key Takeaways00:13
Knowledge Check
Clustering Image Data
Lesson 08 Time Series Modeling37:44
8.1 Learning Objectives00:24
8.2 Overview of Time Series Modeling02:16
8.3 Time Series Pattern Types: Part A02:16
8.4 Time Series Pattern Types: Part B01:19
8.5 White Noise01:07
8.6 Stationarity02:13
8.7 Removal of Non-Stationarity02:13
8.8 Demo: Air Passengers – A14:33
Practice: Beer Production – A
8.9 Time Series Models: Part A02:14
8.10 Time Series Models: Part B01:28
8.11 Time Series Models: Part C01:51
8.12 Steps in Time Series Forecasting00:37
8.13 Demo: Air Passengers – B05:01
Practice: Beer Production – B
8.14 Key Takeaways00:12
Knowledge Check
IMF Commodity Price Forecast
Lesson 09 Ensemble Learning35:41
9.01 Ensemble Learning00:24
9.2 Overview02:41
9.3 Ensemble Learning Methods: Part A02:28
9.4 Ensemble Learning Methods: Part B02:37
9.5 Working of AdaBoost01:43
9.6 AdaBoost Algorithm and Flowchart02:28
9.7 Gradient Boosting02:36
9.8 XGBoost02:23
9.9 XGBoost Parameters: Part A03:15
9.10 XGBoost Parameters: Part B02:30
9.11 Demo: Pima Indians Diabetes04:14
Practice: Linearly Separable Species
9.12 Model Selection02:08
9.13 Common Splitting Strategies01:45
9.14 Demo: Cross Validation04:18
Practice: Model Selection
9.15 Key Takeaways00:11
Knowledge Check
Tuning Classifier Model with XGBoost
Lesson 10 Recommender Systems25:45
10.1 Learning Objectives00:28
10.2 Introduction02:17
10.3 Purposes of Recommender Systems00:45
10.4 Paradigms of Recommender Systems02:45
10.5 Collaborative Filtering: Part A02:14
10.6 Collaborative Filtering: Part B01:58
10.7 Association Rule Mining01:47
Association Rule Mining: Market Basket Analysis01:43
10.9 Association Rule Generation: Apriori Algorithm00:53
10.10 Apriori Algorithm Example: Part A02:11
10.11 Apriori Algorithm Example: Part B01:18
10.12 Apriori Algorithm: Rule Selection02:52
10.13 Demo: User-Movie Recommendation Model04:19
Practice: Movie-Movie recommendation
10.14 Key Takeaways00:15
Knowledge Check
Book Rental Recommendation
Lesson 11 Text Mining43:58
11.1 Learning Objectives00:22
11.2 Overview of Text Mining02:11
11.3 Significance of Text Mining01:26
11.4 Applications of Text Mining02:23
11.5 Natural Language ToolKit Library02:35
11.6 Text Extraction and Preprocessing: Tokenization00:33
11.7 Text Extraction and Preprocessing: N-grams00:55
11.8 Text Extraction and Preprocessing: Stop Word Removal01:24
11.9 Text Extraction and Preprocessing: Stemming00:44
11.10 Text Extraction and Preprocessing: Lemmatization00:35
11.11 Text Extraction and Preprocessing: POS Tagging01:17
11.12 Text Extraction and Preprocessing: Named Entity Recognition00:54
11.13 NLP Process Workflow00:53
11.14 Demo: Processing Brown Corpus10:05
Wiki Corpus
11.15 Structuring Sentences: Syntax01:54
11.16 Rendering Syntax Trees00:55
11.17 Structuring Sentences: Chunking and Chunk Parsing01:38
11.18 NP and VP Chunk and Parser01:39
11.19 Structuring Sentences: Chinking01:44
11.20 Context-Free Grammar (CFG)01:56
11.21 Demo: Structuring Sentences07:46
Practice: Airline Sentiment
11.22 Key Takeaways00:09
Knowledge Check
FIFA World Cup
Lesson 12 Project Highlights02:40
Project Highlights02:40
Uber Fare Prediction
Amazon – Employee Access
Practice Projects
California Housing Price Prediction
Phishing Detector with LR

Course 4 Online Classroom Flexi Pass

Deep Learning with Keras and TensorFlow
This Deep Learning course with Tensorflow certification training is developed by industry leaders and aligned with the latest best practices. You’ll master deep learning concepts and models using Keras and TensorFlow frameworks and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer.

Section 1 – Deep Learning with Tensor Flow (Self Learning)

Lesson 1 – Welcome!02:06
1.1 Welcome!02:06
1.2 Learning Objectives
Lesson 2 – Introduction to Tensorflow31:55
2.1 Learning Objectives07:00
2.2 Introduction to TensorFlow07:00
2.3 TensorFlow’s Hello World03:28
2.4 Tensorflow Hello World
2.5 Linear Regression With Tensorflow
2.6 Logistic Regression With Tensorflow
2.7 Activation Functions
2.8 Intro to Deep Learning02:39
2.9 Deep Neural Networks11:48
Lesson 3 – Convolutional Networks21:51
3.1 Learning Objectives
3.2 Intro to Convolutional Networks04:37
3.3 CNN for Classifications04:09
3.4 CNN Architecture13:05
3.5 Understanding Convolutions
3.6 CNN with MNIST Dataset
Lesson 4 – Recurrent Neural Network24:43
4.1 Learning Objectives03:06
4.2 The Sequential Problem03:06
4.3 The RNN Model05:28
4.4 The LSTM Model05:25
4.5 Applying RNNs to Language Modeling07:38
4.6 LTSM Basics
4.7 MNIST Data Classification With RNN/LSTM
4.8 Applying RNN/LSTM to Language Modelling
4.9 Applying RNN/LSTM to Character Modelling
Lesson 5 – Restricted Boltzmann Machines (RBM)14:14
5.1 Learning Objectives04:29
5.2 Intro to RBMs04:29
5.3 Training RBMs05:16
5.5 Collaborative Filtering With RBM
Lesson 6 – Autoencoders17:20
6.1 Learning Objectives04:51
6.2 Intro to Autoencoders04:51
6.3 Applying RNNs to Language Modelling07:38
6.4 Autoencoders
Lesson 7 – Course Summary02:17
7.1 Course Summary02:17
Unlocking IBM Certificate

Section 2 – Deep Learning with Keras and Tensor Flow (Live Classes)

Lesson 1 – Course introduction
Lesson 2 – AI and Deep learning introduction
What is AI and Deep learning
Brief History of AI
Recap: SL, UL and RL
Deep learning : successes last decade
Demo & discussion: Self driving car object detection
Applications of Deep learning
Challenges of Deep learning
Demo & discussion: Sentiment analysis using LSTM
Fullcycle of a deep learning project
Key Takeaways
Knowledge Check
Lesson 3 – Artificial Neural Network
Biological Neuron Vs Perceptron
Shallow neural network
Training a Perceptron
Demo code: Perceptron ( linear classification) (Assisted)
Role of Activation functions & backpropagation
Demo code: Backpropagation (Assisted)
Demo code: Activation Function (Unassisted)
Dropout layer
Key Takeaways
Knowledge Check
Lesson-end Project (MNIST Image Classification)
Lesson 4 – Deep Neural Network & Tools
Deep Neural Network : why and applications
Designing a Deep neural network
How to choose your loss function?
Tools for Deep learning models
Keras and its Elements
Demo Code: Build a deep learning model using Keras (Assisted)
Tensorflow and Its ecosystem
Demo Code: Build a deep learning model using Tensorflow (Assisted)
Pytorch and its elements
Key Takeaways
Knowledge Check
Lesson-end Project: Build a deep learning model using Pytorch with Cifar10 dataset
Lesson 5 – Deep Neural Net optimization, tuning, interpretability
Optimization algorithms
SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
Batch normalization
Demo Code: Batch Normalization (Assisted)
Exploding and vanishing gradients
Hyperparameter tuning
Key Takeaways
Knowledge Check
Lesson-end Project: Hyperparameter Tunning With Keras Tuner
Lesson 6 – Convolutional Neural Network
Success and history
CNN Network design and architecture
Demo code: CNN Image Classification (Assisted)
Deep convolutional models
Key Takeaways
Knowledge Check
Lesson-end Project: Image Classification
Lesson 7 – Recurrent Neural Networks
Sequence data
Sense of time
RNN introduction
LSTM ( retail sales dataset kaggle)
Demo code: Stock Price Prediction with LSTM (Assisted)
Demo code: Multiclass Classification using LSTM (Unassisted)
Demo code: Sentiment Analysis using LSTM (Assisted)
Key Takeaways
Knowledge Check
Lesson-end Project: Stock Price Forecasting
Lesson 8 – Autoencoders
Introduction to Autoencoders
Applications of Autoencoders
Autoencoder for anomaly detection
Demo code: Autoencoder model for MNIST data (Assisted)
Key Takeaways
Knowledge Check
Lesson-end Project: Anomaly detection with Keras

Section 3 – Practice Projects

Practice Projects
PUBG Players Finishing Placement Prediction

Course 5 Online Classroom Flexi Pass

Advanced Deep Learning and Computer Vision
Advanced Deep Learning and Computer Vision

Section 01 – Self-paced Learning Curriculum

Lesson 01 – DL Overview and Denoising Images with Autoencoders42:02
1 The Course Overview03:49
2 A High-Level Overview of Deep Learning08:38
3 Installing Keras and TensorFlow06:20
4 Building a CNN Based Autoencoder to Denoise Images22:07
5 Summary01:08
Lesson 02 – 2 Image Classification with Keras19:41
1 An Introduction to ImageNet Dataset and VGG Model06:13
2 Using a Pre-Trained VGG Model11:32
3 Summary and What’s Next01:56
Lesson 03 – Construct a GAN with Keras24:54
1 Introduction to GANs03:45
2 Building GANs to Learn MNIST Dataset19:47
3 Summary and What’s Next01:22
Lesson 04 – Object Detection with YOLO25:19
1 An Introduction to Object Detection and YOLO06:01
2 Installing and Setting Up Keras Implementation of YOLO09:36
3 Using a Pre-Trained YOLO Model for Object Detection06:47
4 Summary and What’s Next02:55
Lesson 05 – Generating Images with Neural Style11:55
1 An Introduction to Neural Style Transfer05:51
2 Using Keras Implementation of Neural Style Transfer04:58
3 Summary01:06

Section 02 – Live Class Curriculum

Lesson 01 – Course Introduction
Course Objective
Course Outline
Course Components
Course Prerequisites
Project Highlight
Course Outcome
Lesson 02 – Prerequisites for the Course
Basic of Generative Models
KL Divergence
Image processing with OpenCv
Lesson 03 – RBM and DBNs
Collaborative Filtering
Boltzmann Machines
RBM on MNIST handwritten dataset
Convolutional Boltzman Machines
Movie Recomendation system using RBM
Lesson 04 – Variational AutoEncoder
Stacked Autoencoder
Denoising Autoencoder
Sparse Autoencoder
Variational Autoencoder
Vector Quantized Variational AutoEncode
Temporal Difference VAE
Variational Autoencoder with Tensorflow
Variational Autoencoder with Keras
Build a variational autoencoder model to regenerate images of MNIST.
Lesson 05 – Working with Deep Generative Models
Introduction to Generative Adversarial Networks
Generative vs. Discriminative Algorithms
Architectural Overview
Basic building block – generator
Basic building block – discriminator
Types of GANs
Introduction to Deep Convolutional GANs (DCGAN)
Generating images with DCGANs
Augmenting datasets with conditional GANs
Introduction to Least Square GANs (LSGAN)
Introduction to Auxiliary Classifier GAN (ACGAN)
Introduction to infoGAN
Image Translation with GANs : pix2pix
Image Translation with GANs : CycleGANs
Cycle GAN
Use Keras or TensorFlow to build a deep generative model that will translate drawings of shoes to designs.
Lesson 06 – Applications: Neural Style transfer and Object Detection
An Introduction to neural style Transfer
Concept of Neural Style Transfer
Neural Style Transfer with Tensorflow
Create a Photo Editing Feature Using PyTorch
Success and History
Transfer Learning
Transfer learning method
Object Detection
Intersection Over Union
Object Identification Using YoloV3
Object Detection With Pretrained YoloV3
Lesson 07 – Distributed & Parallel Computing for Deep Learning Models
Introduction to CUDA architecture
Training tensorflow models on GPUs with Keras
Parallel Training
Distributed vs Parallel Computing
Distributed computing in Tensorflow
Introduction to tf.distribute
Distributed training across multiple CPUs
Distributed Training
Distributed training across multiple GPUs
Federated Learning
Parallel computing in Tensorflow
Introduction to tf federated
Train a CNN model on AWS SageMaker that classifies the fashion-mnist dataset using distributed training.
Lesson 08 – Reinforcement Learning
Introduction to Reinforcement learning
What is Reinforcement learning and its types
Reinforcement learning framework
Elements of Reinforcement Learning and Approaches
Mathematical formulation of Reinforcement Learning
Solution Methods: Dynamic Programming
Solution Methods: Algorithms
OpenAI gym
Lesson 09 – Deploying Deep Learning Models and Beyond
Understanding model Persistence in Keras
Saving and Serializing Models in Keras
Saving Models
Restoring and loading saved models
Loading Models
Introduction to Tensorflow Serving
Creating Custom REST APIs for your models with Flask/Django
Tensorflow Serving Rest
Deploying deep learning models with Docker
Tensorflow Serving Docker
Deploying deep learning models on Kubernetes
Tensorflow Deployment Flask
Deploying deep learning models in Serverless Environments
Deploying Model to Sage Maker
Introducing Tensorflow Lite
Introducing ONNx
Train and deploy a CNN model with TensorFlow on SageMaker to classify fashion articles.
Restricted Boltzmann Machines
Convolutional Boltzman Machines

Course 6 Online Classroom Flexi Pass

Natural Language Processing
The Natural Language Processing course gives you a detailed look at the science of applying machine learning algorithms to process large amounts of natural language data. NLP is driving the growth of the AI market, and this course helps you develop the skills required to become an NLP Engineer.

Section 01 – NLP Overview (Self Learning)

Lesson 1 Working with Text Corpus26:17
1.1 The Course Overview03:59
1.2 Access and Use the Built-in Corpora of NLTK06:20
1.3 Loading a Corpus04:08
1.4 An Example of Conditional Frequency Distribution05:11
1.5 An Example of Lexical Resouce06:39
Lesson 2 Processing Raw Text with NLTK23:12
2.1 Working with an NLP Pipeline06:14
2.2 Implementing Tokenization05:31
2.3 Regular Expressions05:30
2.4 Regular Expressions Used in Tokenization05:57
Lesson 3 A Practical Real-World Example of Text Classification19:38
3.1 Naive Bayes Text Classification07:06
3.2 Age Prediction Application06:37
3.3 Document Classifier Application05:55
Lesson 4 Finding Useful Information from Piles of Text13:24
4.1 Hierarchy of Ideas or Chunking02:33
4.2 Chunking in Python NLTK05:18
4.3 Chinking Non Chunk Patterns in NLTK05:33
Lesson 5 Developing a Speech to Text Application Using Python28:43
5.1 Python Speech Recognition Module06:11
5.2 Speech to Text with Recurrent Neural Networks09:36
5.3 Speech to Text with Convolutional Neural Networks Part One06:29
5.4 Speech to Text with Convolutional Neural Networks Part Two06:27

Section 02 -NLP (Live Classes)

Lesson 1 – Introduction to NLP
Introduction to Natural Language Processing
Components of NLP
Applications of NLP
Challenges and scope
Data formats
Text Processing
Assisted Practice: Implement Text Processing Using Stemming and Regular Expression after Noise Removal and Convert It into List of Phrases
Tweets Cleanup and Analysis Using Regular Expressions
Lesson 2 – Feature Engineering on text data
Bag of Words
Document Term Matrix
Levenshtein Distance
Word Embedding(Word2Vec)
Word Analogies
Topic Modelling
Assisted Practice: Word2vec Model Creation
Assisted Practice: Word Analogies Demo
Assisted Practice: Identify Topics from News Items
Build Your Own News Search Engine
Lesson 3 – Natural Language Understanding techniques
Parts of Speech Tagging
Dependency Parsing
Constituency Parsing
Morphological Parsing
Named Entity Recognition
Coreference Resolution
Word Sense Disambiguation
Fuzzy Search
Document and Sentence Similarity
Document Indexing
Sentiment Analysis
Assisted Practice: Analyzing the Disease and Instrument Name with the Action Performed
Assisted Practice: Analyzing the Sentiments
Assisted Practice: Extract City and Person Name from Text
Identifying Top Product Feature from User Reviews
Lesson 4 – Natural Language Generation
Retrieval based model
Generative based model
Language Modelling
Sentence Correction
Assisted Practice: Create AIML Patterns for QnA on Mental Wellness
Assisted Practice: To Predict the Next Word in a Sentence
Create your Own Spell Checker
Lesson 5 – NLP Libraries
Assisted Practice: Simplilearn Review Analysis
Create your Own NLP Module
Lesson 6 – NLP with Machine Learning and Deep Learning
Neural Machine Translation
Text Classification
Text Summarization
Document Clustering
Attention Mechanism
Question Answering Engine
Assisted Practice: Target Spam Words and Patterns
Assisted Practice: Summarization of News
Document Clustering for BBC News
Lesson 7 – Speech recognition techniques
Basic concepts for voice/sound
Reading, loading and processing the voice data
Creating speech model
Saving model
Implementation/use cases
Speech libraries
Assisted Practice: Translation from Speech to Text
Speech to Text: Extract Keywords from Audio Reviews

Natural Language Processing

Section 03 – Practice Projects
Twitter Hate
Zomato Rating

Course 7

AI Capstone Project
Simplilearn’s Artificial Intelligence (AI) Capstone project will give you an opportunity to implement the skills you learned in the masters of AI. With dedicated mentoring sessions, you’ll know how to solve a real industry-aligned problem. The project is the final step in the learning path and will help you to showcase your expertise to employers.

AI Capstone Project

Exploratory Data Analysis
Exploratory Data Analysis
Model Building and fitting
Model Building and fitting
Unsupervised learning
Unsupervised learning
Representing results
Representing results

From: $14.99 / month

  • Vast selection of courses and labs Access
  • Unlimited access from all devices
  • Learn from industry expert instructors
  • Assessment quizzes and monitor progress
  • Vast selection of courses and labs Access
  • Blended Learning with Virtual Classes
  • Access to new courses every quarter
  • 100% satisfaction guarantee

You Will Get Certification After Completetion This Course.

Instructor Led Lectures
All IT Tutor Pro Formerly It Nuggets Courses replicate a live class experience with an instructor on screen delivering the course’s theories and concepts.These lectures are pre-recorded and available to the user 24/7. They can be repeated, rewound, fast forwarded.
Visual Demonstrations, Educational Games & Flashcards
IT Tutor Pro Formerly It Nuggets recognizes that all students do not learn alike and different delivery mediums are needed in order to achieve success for a large student base. With that in mind, we delivery our content in a variety of different ways to ensure that students stay engaged and productive throughout their courses.
Mobile Optimization & Progress Tracking
Our courses are optimized for all mobile devices allowing students to learn on the go whenever they have free time. Students can access their courses from anywhere and their progress is completely tracked and recorded.
Practice Quizzes And Exams
IT Tutor Pro Formerly It Nuggets Online’s custom practice exams prepare you for your exams differently and more effectively than the traditional exam preps on the market. Students will have practice quizzes after each module to ensure you are confident on the topic you are learning.
World Class Learning Management System
IT Tutor Pro Formerly It Nuggets provides the next generation learning management system (LMS). An experience that combines the feature set of traditional Learning Management Systems with advanced functionality designed to make learning management easy and online learning engaging from the user’s perspective.

Frequently Asked Questions

How does online education work on a day-to-day basis?
Instructional methods, course requirements, and learning technologies can vary significantly from one online program to the next, but the vast bulk of them use a learning management system (LMS) to deliver lectures and materials, monitor student progress, assess comprehension, and accept student work. LMS providers design these platforms to accommodate a multitude of instructor needs and preferences.
Is online education as effective as face-to-face instruction?
Online education may seem relatively new, but years of research suggests it can be just as effective as traditional coursework, and often more so. According to a U.S. Department of Education analysis of more than 1,000 learning studies, online students tend to outperform classroom-based students across most disciplines and demographics. Another major review published the same year found that online students had the advantage 70 percent of the time, a gap authors projected would only widen as programs and technologies evolve.
Do employers accept online degrees?
All new learning innovations are met with some degree of scrutiny, but skepticism subsides as methods become more mainstream. Such is the case for online learning. Studies indicate employers who are familiar with online degrees tend to view them more favorably, and more employers are acquainted with them than ever before. The majority of colleges now offer online degrees, including most public, not-for-profit, and Ivy League universities. Online learning is also increasingly prevalent in the workplace as more companies invest in web-based employee training and development programs.
Is online education more conducive to cheating?
The concern that online students cheat more than traditional students is perhaps misplaced. When researchers at Marshall University conducted a study to measure the prevalence of cheating in online and classroom-based courses, they concluded, “Somewhat surprisingly, the results showed higher rates of academic dishonesty in live courses.” The authors suggest the social familiarity of students in a classroom setting may lessen their sense of moral obligation.
How do I know if online education is right for me?
Choosing the right course takes time and careful research no matter how one intends to study. Learning styles, goals, and programs always vary, but students considering online courses must consider technical skills, ability to self-motivate, and other factors specific to the medium. Online course demos and trials can also be helpful.
What technical skills do online students need?
Our platform typically designed to be as user-friendly as possible: intuitive controls, clear instructions, and tutorials guide students through new tasks. However, students still need basic computer skills to access and navigate these programs. These skills include: using a keyboard and a mouse; running computer programs; using the Internet; sending and receiving email; using word processing programs; and using forums and other collaborative tools. Most online programs publish such requirements on their websites. If not, an admissions adviser can help.