About the course
Machine Learning Engineer Masters Program covers a broad array of topics which includes: Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models, Reinforcement Learning and many more.
The program provides access to 200+ hours of interactive online learning, 12 industry-based use cases, skills specific assessments and other resources. There are no prerequisites for enrollment to the Masters Program. It is designed and developed to accommodate diverse professional backgrounds. Our Masters Program recommends the ideal path for becoming a Machine Learning Engineer, however, it is learner’s preference to complete the courses in any order they intend to.
Index
Python Programming
Machine Learning with Python
Graphical Models
Reinforcement Learning
NLP with Python
Deep Learning with TensorFlow 2.0 Certification Training
Python Spark using PySpark
Python Programming
About the Course
Cloudruha’s Python Programming Certification Course will help you master important Python programming concepts such as Data Operations, File Operations, Object-Oriented concepts, and various Python libraries such as Pandas, Numpy, Matplotlib and many more. You will learn Data Visualization and techniques to deal with different types of data – ordinal, categorical, encoding. This course makes you industry-ready by working on real-life case-studies and equipping you with relevant concepts.
Curriculum
Module 1: Introduction to Python
Learning Objective: In this module, you will get to know about the basic concepts of Python.
Topics:
Need for Programming
Advantages of Programming
Overview of Python
Organizations using Python
Python Applications in Various Domains
Python Installation
Variables
Operands and Expressions
Conditional Statements
Loops
Command Line Arguments
Module 2: Sequences and File Operations
Learning Objective: Perform operations on Files and learn different types of sequence structures, their usage, and execute sequence Operation.
Topics:
Method of Accepting User Input and eval Function
Python - Files Input/Output Functions
Lists and Related Operations
Tuples and Related Operations
Strings and Related Operations
Sets and Related Operations
Dictionaries and Related Operations
Module 3 : Deep Dive – Functions and OOPs
Learning Objective: Learn about different types of Functions and various Object-Oriented concepts such as Abstraction, Inheritance, Polymorphism, Overloading, Constructor, and so on.
Topics:
User-Defined Functions
Concept of Return Statement
Concept of __name__=” __main__”
Function Parameters
Different Types of Arguments
Global Variables
Global Keyword
Variable Scope and Returning Values
Lambda Functions
Various Built-In Functions
Introduction to Object-Oriented Concepts
Built-In Class Attributes
Public, Protected and Private Attributes, and Methods
Class Variable and Instance Variable
Constructor and Destructor
Decorator in Python
Core Object-Oriented Principles
Inheritance and Its Types
Method Resolution Order
Overloading
Overriding
Getter and Setter Methods
Inheritance-In-Class Case Study
Module 4 : Working with Modules and Handling Exceptions
Learning Objective: Learn how to create generic python scripts, address errors/exceptions in code, and extract/filter content using regex.
Topics:
Standard Libraries
Packages and Import Statements
Reload Function
Important Modules in Python
Sys Module
Os Module
Math Module
Date-Time Module
Random Module
JSON Module
Regular Expression
Exception Handling
Module 5 : Introduction to NumPy
Learning Objective: Get familiar with the basics of Data Analysis using two essential libraries: NumPy and Pandas. You will also understand the concept of file handling using the NumPy library.
Topics:
Basics of Data Analysis
NumPy - Arrays
Operations on Arrays
Indexing Slicing and Iterating
NumPy Array Attributes
Matrix Product
NumPy Functions
Functions
Array Manipulation
File Handling Using NumPy
Module 6: Data Manipulation using pandas
Learning Objective: Gain in-depth knowledge about analyzing datasets and data manipulation using Pandas.
Topics:
Introduction to pandas
Data structures in pandas
Series
Data Frames
Importing and Exporting Files in Python
Basic Functionalities of a Data Object
Merging of Data Objects
Concatenation of Data Objects
Types of Joins on Data Objects
Data Cleaning using pandas
Exploring Datasets
Module 7: Data Visualization using Matplotlib
Learning Objective: Learn Data Visualization using Matplotlib.
Topics:
Why Data Visualization?
Matplotlib Library
Line Plots
Multiline Plots
Bar Plot
Histogram
Pie Chart
Scatter Plot
Boxplot
Saving Charts
Customizing Visualizations
Saving Plots
Grids
Subplots
Module 8: GUI Programming
Learning Objective: In this module, you will learn GUI programming using ipywidgets package.
Topics:
Ipywidgets Package
Numeric Widgets
Boolean Widgets
Selection Widgets
String Widgets
Date Picker
Color Picker
Container Widgets
Creating a GUI Application
Module 9: Developing Web Maps and Representing Information using Plots (Self-paced)
Learning Objective: Learn to design Python Applications.
Topics:
Use of Folium Library
Use of Pandas Library
Flow Chart of Web Map Application
Developing Web Map Using Folium and Pandas
Reading Information from Titanic Dataset and Represent It Using Plots
Module 10: Computer Vision using OpenCV and Visualization using Bokeh (Self-paced)
Learning Objective: Learn to design Python Applications.
Topics:
• Beautiful Soup Library
• Requests Library
• Scrap All Hyperlinks from a Webpage Using Beautiful Soup and Requests
• Plotting Charts Using Bokeh
• Plotting Scatterplots Using Bokeh
• Image Editing Using OpenCV
• Face Detection Using OpenCV
• Motion Detection and Capturing Video
Machine Learning with Python
About the Course
The Course ‘Machine Learning with Python’ is designed to gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR.
Curriculum
Module 1: Introduction to Data Science
Learning Objective: At the end of this Module, you should be able to define Data Science, discuss the era of Data Science, describe the Role of a Data Scientist, illustrate the Life cycle of Data Science, list the Tools used in Data Science, and states what role Big Data and Hadoop, Python, R and Machine Learning play in Data Science.
Topics:
• What is Data Science?
• What does Data Science involve?
• Era of Data Science
• Business Intelligence vs Data Science
• Life cycle of Data Science
• Tools of Data Science
• Introduction to Python
Module 2: Data Extraction, Wrangling, Visualization
Learning Objective: At the end of this Module, you should be able to discuss Data Acquisition technique, list the different types of Data, evaluate Input Data, explain the Data Wrangling techniques, and discuss Data Exploration.
Topics:
• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis
• Visualization of Data
Module 3: Introduction to Machine Learning with Python
Learning Objective: At the end of this module, you should be able to essential Python Revision, necessary Machine Learning Python libraries, define Machine Learning, discuss Machine Learning Use cases, list the categories of Machine Learning, illustrate Supervised Learning Algorithms, identify and recognize machine learning algorithms around us and understand the various elements of machine learning algorithm like parameters, hyper parameters, loss function and optimization.
Topics:
• Python Revision (numpy, Pandas, scikit learn, matplotlib)
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Linear regression
• Gradient descent
Skills:
• Machine Learning concepts
• Machine Learning types
• Linear Regression Implementation
Module 4: Supervised Learning – I
Learning Objective: At the end of this module, you should be able to understand What is Supervised Learning, illustrate Logistic Regression, define Classification, and explain different Types of Classifiers such as - Decision Tree and Random Forest.
Topics:
• What is Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?
Skills:
• Supervised Learning concepts
• Implementing different types of Supervised Learning algorithms
• Evaluating model output
Module 5: Dimensionality Reduction
Learning Objective: At the end of this module, you should be able to define the importance of Dimensions, explore PCA and its implementation, and discuss LDA and its implementation
Topics:
• Introduction to Dimensionality
• Why Dimensionality Reduction
• PCA
• Factor Analysis
• Scaling dimensional model
• LDA
Skills:
• Implementing Dimensionality Reduction Technique
Module 6: Supervised Learning – II
Learning Objective: At the end of this module, you should be able to understand What is Naïve Bayes Classifier, how Naïve Bayes Classifier works, understand Support Vector Machine, illustrate How Support Vector Machine works, and understand Hyperparameter Optimization
Topics:
• What is Naïve Bayes?
• How Naïve Bayes works?
• Implementing Naïve Bayes Classifier
• What is Support Vector Machine?
• Illustrate how Support Vector Machine works?
• Hyperparameter Optimization
• Grid Search vs Random Search
• Implementation of Support Vector Machine for Classification
Skills:
• Supervised Learning concepts
• Implementing different types of Supervised Learning algorithms
• Evaluating model output
Module 7: Unsupervised Learning
Learning Objective: At the end of this module, you should be able to define Unsupervised Learning, discuss the following Cluster Analysis: K - means Clustering, C - means Clustering, and Hierarchical Clustering
Topics:
• What is Clustering & its Use Cases?
• What is K-means Clustering?
• How K-means algorithm works?
• How to do optimal clustering
• What is C-means Clustering?
• What is Hierarchical Clustering?
• How Hierarchical Clustering works?
Skills:
• Unsupervised Learning
• Implementation of Clustering – various type
Module 8: Association Rules Mining and Recommendation Systems
Learning Objective: At the end of this module, you should be able to define Association Rules and learn the backend of recommendation engines and develop your own using python
Topics:
• What are Association Rules?
• Association Rule Parameters
• Calculating Association Rule Parameters
• Recommendation Engines
• How Recommendation Engines work?
• Collaborative Filtering
• Content Based Filtering
Skills:
• Data Mining using python
• Recommender Systems using python
Module 9: Reinforcement Learning
Learning Objective: At the end of this module, you should be able to explain the concept of Reinforcement Learning, generalize a problem using Reinforcement Learning, explain Markov’s Decision Process, and demonstrate Q Learning
Topics:
• What is Reinforcement Learning
• Why Reinforcement Learning
• Elements of Reinforcement Learning
• Exploration vs Exploitation dilemma
• Epsilon Greedy Algorithm
• Markov Decision Process (MDP)
• Q values and V values
• Q – Learning
• α values
Skills:
• Implement Reinforcement Learning using python
• Developing Q Learning model in python
Module 10: Time Series Analysis
Learning Objective: At the end of this module, you should be able to explain Time Series Analysis (TSA), discuss the need of TSA, describe ARIMA modelling, and Forecast the time series model.
Topics:
• What is Time Series Analysis?
• Importance of TSA
• Components of TSA
• White Noise
• AR model
• MA model
• ARMA model
• ARIMA model
• Stationarity
• ACF & PACF
Skills:
• TSA in Python
Module 11: Model Selection and Boosting
Learning Objective: At the end of this module, you should be able to discuss Model Selection, define Boosting, express the need of Boosting, and explain the working of Boosting algorithm
Topics:
• What is Model Selection?
• Need of Model Selection
• Cross – Validation
• What is Boosting?
• How Boosting Algorithms work?
• Types of Boosting Algorithms
• Adaptive Boosting
Skills:
• Model Selection
• Boosting algorithm using python
Module 12: Hands-On Project
Learning Objective: At the end of this module, you should be able to know how to approach a project, hands-On project implementation, what Industry expects, Industry insights for the Machine Learning domain, and QA & doubt clearing session.
Graphical Models
About the Course
Graphical Modelling Certification Training’ is designed to teach Graphical Models, fundamentals of graphical modelling, Probabilistic theories, types of graphical models –Bayesian (Directed) and Markov’s (Undirected) networks, representation of Bayesian and Markov’s Networks, concepts related to Bayesian and Markov’s networks, and decision making– theories and Inference.
Curriculum
Module 1: Introduction to Graphical Model
Learning Objective: To give a brief idea about Graphical models, graph theory, probability theory, components of graphical models, types of graphical models, representation of graphical models, Introduction to inference, learning and decision making in Graphical Models.
Topics:
• Add examples where Graphical Models are used (Netflix or Amazon or Facebook)
• Why do we need Graphical Models?
• Introduction to Graphical Model
• Probability theory
• Graph theory
• How does Graphical Model help you deal with uncertainty and complexity?
• Types of Graphical Models
• Factor graph
• Undirected graph
• Directed graph
• Graphical Modes
• Bayesian Networks
• Markov Networks
• Components of Graphical Model
• Qualitative specification
• Quantitative specification
• Representation of Graphical Models
• Inference in Graphical Models
• Learning Graphical Models
• Decision theory
• Applications
Module 2: Bayesian Network
Learning Objective: To give a brief idea of Bayesian networks, independencies in Bayesian Networks and building a Bayesian network.
Topics:
• What is Bayesian Network?
• Advantages of Bayesian Network for data analysis
• Bayesian Network in Python Examples
• Independencies in Bayesian Networks
• Criteria for Model Selection
• Relative Posterior Probability
• Local Criteria
• Building a Bayesian Network
Module 3: Markov’s Networks
Learning Objective: To give a brief understanding of Markov’s networks, independencies in Markov’s networks, Factor graph and Markov’s decision process.
Topics:
• Example of a Markov Network or Undirected Graphical Model
• Markov Model
• Markov Chain
• Continuous-time Markov Chain
• Reversible Markov Chain
• Markov Property
• Markov and Hidden Markov Models
• The Factor Graph
• Independencies in Markov Networks
• Markov Decision Process
• Decision Making under Uncertainty
• Decision Making Scenarios
Module 4: Inference
Learning Objective: To understand the need for inference and interpret inference in Bayesian and Markov’s Networks.
Topics:
• Inference
• Marginal Inference
• Posterior Inference
• MAP Inference
• Complexity in Inference
• Exact Inference
• Approximate Inference
• Monte Carlo Algorithm
• Gibb’s Sampling
• Inference in Bayesian Networks
• Inference in Bayesian Networks
Module 5: Model learning
Learning Objective: To understand the Structures and Parametrization in graphical Models
Topics:
• General Ideas in Learning
• Goals of Learning
• Density Estimation
• Predicting the Specific Probability Values
• Knowledge Discovery
• Parameter Learning
• Maximum Likelihood Estimation
• Maximum Likelihood Principle
• The Maximum Likelihood Estimate for Bayesian Networks
• Learning with Approximate Inference
• Structure learning
• Constraint-based Structure Learning
• Score-based Structure Learning
• The likelihood Score
• Bayesian Score
• Model Learning: Parameter Estimation in Bayesian Networks
• Model Learning: Parameter Estimation in Markov Networks
Reinforcement Learning
About the Course
Reinforcement Learning is designed as an area of Machine Learning. You will learn the Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) methods. You will be introduced to Value function, Bellman equation, and Value iteration. You will also learn Policy Gradient methods and learn to make decisions in uncertain environment.
Curriculum
Module 1: Introduction to Reinforcement Learning
Learning Objective: The aim of this module is to introduce you to the fundamentals of Reinforcement Learning and its elements. To learn Decision Making, Monte Carlo Approach and Temporal Difference Learning.
Topics:
• Branches of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• What is Reinforcement Learning?
• Reinforcement Learning - How does it differ from other machine learning paradigms
• Comparing RL with other ML techniques
• Elements of Reinforcement Learning
• The Reinforcement Learning Process
• Rewards
• The central idea of the Reward Hypothesis
• Reward Examples
• Agent and Environment
• Fully Observable Environments
• Partially Observable Environments
• RL Agent Components (Value-based, Policy-based, Model-based)
• RL Agent Taxonomy
• Types of Tasks (Episodic and Continuous Tasks)
• Ways of Learning (Monte Carlo Approach and Temporal Difference Learning)
• Exploration and Exploitation Trade off
• Approaches to Decision Making in RL
• Most used Reinforcement Learning algorithm (Q-learning)
• Practical applications of Reinforcement Learning
• Challenges with implementing RL
Module 2: Markov Decision Processes and Bandit Algorithms
Learning Objective: The aim of this module is to Markov Decision Processes and Bandit Algorithms.
Topics:
• Reinforcement Learning Problems
o Formulating a basic Reinforcement Learning Problem
o Framework for solving RL problem
• Markov Processes
• Markov Reward Processes
• Markov Decision Processes
• Bellman Equation
• Bandit Algorithms (UCB, PAC, Median Elimination, Policy Gradient)
• Contextual Bandits
Module 3: Dynamic Programming & Temporal Difference Methods
Learning Objective: The aim is to get an overview of the tools and techniques of Dynamic Programming and reset the state of the system to a particular state using temporal difference methods.
Topics:
• Introduction to Dynamic Programming
• Policy valuation (Prediction)
• Policy Improvement
• Policy Iteration
• Value Iteration
• Generalized Policy Iteration
• Asynchronous Dynamic Programming
• Efficiency of Dynamic Programming
• Temporal Difference Prediction
• Why TD Prediction Methods
• On-Policy and Off-Policy Learning
• Q-learning
• Reinforcement Learning in Continuous Spaces
• SARSA
Module 4: Value Function, Bellman Equation, Value Iteration, and Policy Gradient Methods
Learning Objective: The aim of this module is to use function approximation methods to represent value functions. Learn Bellman Equation, Value Iteration, and Policy Gradient methods.
Topics:
• Value Function
• Bellman Equations
• Optimal Value Functions
• Bellman Optimality Equation
• Optimality and Approximation
• Value Iteration
• Introduction to Policy-based Reinforcement Learning: Policy Gradient
• Monte Carlo Policy Gradients
• Generalized Advantage Estimation (GAE)
• Monte Carlo Prediction
• Monte Carlo Estimation of Action Values
• Monte Carlo Control
• Monte Carlo Control without Exploring Starts
• Incremental Implementation
• Policy optimization methods (Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO))
Module 5: In-class Project
Learning Objective: The aim of this module is to provide you hands-on experience in Reinforcement Learning.
NLP with Python
About the Course
Cloudruha’s ‘Natural Language Processing with Python’ course will take you through the essentials of text processing all the way up to classifying texts using Machine Learning algorithms. You will learn various concepts such as Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing and so on using Python’s most famous NLTK package. Once you delve into NLP, you will learn to build your own text classifier using the Naïve Bayes algorithm.
Curriculum
Module 1: Introduction to Text Mining and NLP
Learning Objective: At the end of this Module, you should be able to gain an understanding of Text Mining & NLP, manipulate various file types, and use the NLTK library.
Topics:
• Overview of Text Mining
• Need of Text Mining
• Natural Language Processing (NLP) in Text Mining
• Applications of Text Mining
• OS Module
• Reading, Writing to text and word files
• Setting the NLTK Environment
• Accessing the NLTK Corpora
Module 2: Extracting, Cleaning and Pre-processing Text
Learning Objective: At the end of this Module, you should be able to clean and preprocess text data, using sentence tokenization, clean and preprocess text data, using word tokenization, demonstrate the use of Bigrams, Trigrams and Ngrams, work on text data with Stemming, Lemmatization and Stop-Word removal, and describe your text data with POS tags and Named Entities
Topics:
• Tokenization
• Frequency Distribution
• Different Types of Tokenizers
• Bigrams, Trigrams & Ngrams
• Stemming
• Lemmatization
• Stopwords
• POS Tagging
• Named Entity Recognition
Module 3: Analyzing Sentence Structure
Learning Objective: At the end of this Module, you should be able to analyze Syntax Trees, analyze sentence structures using Context Free Grammars (CFG’s), and demonstrate sentence structures using Chunking and Chinking techniques.
Topics:
• Syntax Trees
• Chunking
• Chinking
• Context Free Grammars (CFG)
• Automating Text Paraphrasing
Module 4: Text Classification-I
Learning Objective: At the end of this Module, you should be able to recall the basic mechanism of Machine Learning, demonstrate the concept of Bag of Words, implement Count Vectorization technique, and use the concept of TF-IDF over the csr matrix.
Topics:
• Machine Learning: Brush Up
• Bag of Words
• Count Vectorizer
• Term Frequency (TF)
• Inverse Document Frequency (IDF)
Module 5: Text Classification-II
Learning Objectives: At the end of this Module, you should be able to implement Vectorization technique over text data, derive Bag of Words Model, and use Naiive Bayes classifier to classify labelled text data
Topics:
• Converting text to features and labels
• Multinomial Naiive Bayes Classifier
• Leveraging Confusion Matrix
Module 6: In-Class Project
Learning Objectives: At the end of this module, you should be able to implement all the text processing techniques starting with tokenization, express your end-to-end work on Text Mining, and implement Machine Learning along with Text Processing.
Deep Learning with TensorFlow 2.0 Certification Training
Module 1: Introduction to Deep Learning
Learning Objective: At the end of this module, you will be able to understand the concepts of Deep Learning and learn how it differs from machine learning. This module will also brief you out on implementing the concept of single-layer perceptron.
Topics:
• What is Deep Learning?
• Curse of Dimensionality
• Machine Learning vs. Deep Learning
• Use cases of Deep Learning
• Human Brain vs. Neural Network
• What is Perceptron?
• Learning Rate
• Epoch
• Batch Size
• Activation Function
• Single Layer Perceptron
Module 2: Getting Started with TensorFlow 2.0
Learning Objective: At the end of this module, you should be able to get yourself introduced with TensorFlow 2.x. You will install and validate TensorFlow 2.x by building a Simple Neural Network to predict handwritten digits and using Multi-Layer Perceptron to improvise the accuracy of the model.
Topics:
• Introduction to TensorFlow 2.x
• Installing TensorFlow 2.x
• Defining Sequence model layers
• Activation Function
• Layer Types
• Model Compilation
• Model Optimizer
• Model Loss Function
• Model Training
• Digit Classification using Simple Neural Network in TensorFlow 2.x
• Improving the model
• Adding Hidden Layer
• Adding Dropout
• Using Adam Optimizer
Module 3: Convolution Neural Network
Learning Objective: At the end of this module, you will be able to understand how and why CNN came into existence after MLP and learn about Convolutional Neural Network (CNN) by exploring the theory behind how CNN is used to predict ‘X’ or ‘O’. You will also use CNN VGG-16 using TensorFlow 2 and predict whether the given image is of a ‘cat’ or a ‘dog’ and save and load a model’s weight.
Topics:
• Image Classification Example
• What is Convolution
• Convolutional Layer Network
• Convolutional Layer
• Filtering
• ReLU Layer
• Pooling
• Data Flattening
• Fully Connected Layer
• Predicting a cat or a dog
• Saving and Loading a Model
• Face Detection using OpenCV
Module 4: Regional CNN
Learning Objective: At the end of this module, you will be able to understand the concept and working of RCNN and figure out the reason why it was developed in the first place. The module will cover various important topics like Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask RCNN.
Topics:
• Regional-CNN
• Selective Search Algorithm
• Bounding Box Regression
• SVM in RCNN
• Pre-trained Model
• Model Accuracy
• Model Inference Time
• Model Size Comparison
• Transfer Learning
• Object Detection – Evaluation
• mAP
• IoU
• RCNN – Speed Bottleneck
• Fast R-CNN
• RoI Pooling
• Fast R-CNN – Speed Bottleneck
• Faster R-CNN
• Feature Pyramid Network (FPN)
• Regional Proposal Network (RPN)
• Mask R-CNN
Module 5: Boltzmann Machine & Autoencoder
Learning Objective: At the end of this module, you should be able to understand what a Boltzmann Machine is and how it is implemented. You will also learn about what an Autoencoder is, what are its various types, and understand how it works.
Topics:
• What is Boltzmann Machine (BM)?
• Identify the issues with BM
• Why did RBM come into picture?
• Step by step implementation of RBM
• Distribution of Boltzmann Machine
• Understanding Autoencoders
• Architecture of Autoencoders
• Brief on types of Autoencoders
• Applications of Autoencoders
Module 6: Generative Adversarial Network (GAN)
Learning Objective: At the end of this module, you should be able to understand what generative adversarial model is and how it works by implementing step by step Generative Adversarial Network.
Topics:
• What is Boltzmann Machine (BM)?
• Identify the issues with BM
• Why did RBM come into picture?
• Step by step implementation of RBM
• Distribution of Boltzmann Machine
• Understanding Autoencoders
• Architecture of Autoencoders
• Brief on types of Autoencoders
• Applications of Autoencoders
Module 7: Emotion and Gender Detection
Learning Objective: At the end of this module, you will be able to classify each emotion shown in the facial expression into different categories by developing a CNN model for recognizing the facial expression of the images and predict the facial expression of the uploaded image. During the project implementation, you will also be using OpenCV and Haar Cascade File to check the emotion in real-time.
Topics:
• What is Boltzmann Machine (BM)?
• Identify the issues with BM
• Why did RBM come into picture?
• Step by step implementation of RBM
• Distribution of Boltzmann Machine
• Understanding Autoencoders
• Architecture of Autoencoders
• Brief on types of Autoencoders
• Applications of Autoencoders
Module 8: Introduction RNN and GRU
Learning Objectives: After completing this module, you should be able to distinguish between Feed Forward Network and Recurrent neural network (RNN) and understand how RNN works. You will also understand and learn about GRU and finally implement Sentiment Analysis using RNN and GRU.
Topics:
• What is Boltzmann Machine (BM)?
• Identify the issues with BM
• Why did RBM come into picture?
• Step by step implementation of RBM
• Distribution of Boltzmann Machine
• Understanding Autoencoders
• Architecture of Autoencoders
• Brief on types of Autoencoders
• Applications of Autoencoders
Module 9: LSTM
Learning Objective: After completing this module, you should be able to understand the architecture of LSTM and the importance of gates in LSTM. You will also be able to differentiate between the types of sequence-based models and finally increase the efficiency of the model using BPTT.
Topics:
• What is Boltzmann Machine (BM)?
• Identify the issues with BM
• Why did RBM come into picture?
• Step by step implementation of RBM
• Distribution of Boltzmann Machine
• Understanding Autoencoders
• Architecture of Autoencoders
• Brief on types of Autoencoders
• Applications of Autoencoders
Module 10: Auto Image Captioning Using CNN LSTM
Learning Objective: After completing this module, you should be able to implement Auto Image captioning using pre-trained model Inception V3 and LSTM for text processing.
Topics:
• Auto Image Captioning
• COCO dataset
• Pre-trained model
• Inception V3 model
• Architecture of Inception V3
• Modify last layer of pre-trained model
• Freeze model
• CNN for image processing
• LSTM or text processing
Python Spark using PySpark
About the Course
Cloudruha’s ‘PySpark Certification Training’ is designed to provide you the knowledge and skills that are required to become a successful Spark Developer using Python and prepare you for the Cloudera Hadoop and Spark Developer Certification Exam (CCA175). Throughout the PySpark Training, you will get an in-depth knowledge of Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, Spark MLlib and Spark Streaming. You will also get comprehensive knowledge of Python Programming language, HDFS, Sqoop, Flume, Spark GraphX and Messaging System such as Kafka.
Curriculum
Module 1: Introduction to Big Data Hadoop and Spark
Learning Objective: In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, Hadoop ecosystem components, Hadoop Architecture, HDFS, Rack Awareness and Replication. You will learn about the Hadoop Cluster Architecture, important configuration files in a Hadoop Cluster. You will also get an introduction to Spark, why it is used and understanding of the difference between batch processing and real time processing.
Topics:
• What is Big Data
• Big Data Customer Scenarios
• Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case
• How Hadoop Solves the Big Data Problem
• What is Hadoop
• Hadoop’s Key Characteristics
• Hadoop Ecosystem and HDF
• Hadoop Core Component
• Rack Awareness and Block Replication
• YARN and Its Advantage
• Hadoop Cluster and Its Architecture
• Hadoop: Different Cluster Modes
• Big Data Analytics with Batch & Real-Time Processing
• Why Spark is Needed
• What is Spar
• How Spark Differs from Its Competitors
• Spark’s Place in Hadoop Ecosystem
Module 2: Introduction to Python for Apache Spark
Learning Objective: At the end of this Module, you should be able to define Python, explain Numbers, explain Strings, Tuples, Lists, Dictionaries, and Sets, understand Operands and Expressions, write your First Python Program, understand Command Line Parameters and Flow Control, and take input from the user and perform operations on it.
Topics:
• Overview of Python
• Different Applications where Python is Used
• Values, Types, Variables
• Operands and Expressions
• Conditional Statements
• Loops
• Command Line Arguments
• Writing to the Screen
• Python files I/O Functions
• Numbers
• Strings and related operations
• Tuples and related operations
• Lists and related operations
• Dictionaries and related operations
• Sets and related operations
Module 3: Functions, OOPs, Modules in Python
Learning Objective: At the end of this Module, you should be able to create and Execute Python Functions, learn Object Oriented Concepts in Python, understand Python Standard Libraries, define Modules in Python, and handle Errors and Exceptions
Topics:
• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values
• Lambda Functions
• Object Oriented Concepts
• Standard Libraries
• Modules Used in Python
• The Import Statements
• Module Search Path
• Package Installation Ways
• Errors and Exception Handling
• Handling Multiple Exceptions
Module 4: Deep Dive into Apache Spark Framework
Learning Objective: In this module, you will understand Apache Spark in depth and you will be learning about various Spark components, you will be creating and running various spark applications. At the end you, will learn how to perform data ingestion using Sqoop.
Topics:
• Spark Components & It’s Architecture
• Spark Deployment Modes
• Introduction to PySpark Shell
• Submitting PySpark Job
• Spark Web UI
• Writing your first PySpark Job Using Jupyter Notebook
• Data Ingestion using Sqoop
Module 5: Playing with Spark RDDs
Learning Objective: In this module, you will learn about Spark - RDDs and other RDD related manipulations for implementing business logics (Transformations, Actions and Functions performed on RDD).
Topics:
• Challenges in Existing Computing Methods
• Probable Solution & How RDD Solves the Problem
• What is RDD, It’s Operations, Transformations & Actions?
• Data Loading and Saving Through RDDs
• Key-Value Pair RDDs
• Other Pair RDDs, Two Pair RDDs
• RDD Lineage
• RDD Persistence
• WordCount Program Using RDD Concepts
• RDD Partitioning & How It Helps Achieve Parallelization
• Passing Functions to Spark
Module 6: DataFrames and Spark SQL
Learning Objective: In this module, you will learn about SparkSQL which is used to process structured data with SQL queries. You will learn about data-frames and datasets in Spark SQL along with different kind of SQL operations performed on the data-frames. You will also learn about the Spark and Hive integration.
Topics:
• Need for Spark SQL
• What is Spark SQL?
• Spark SQL Architecture
• SQL Context in Spark SQL
• Schema RDDs
• User Defined Functions
• Data Frames & Datasets
• Interoperating with RDDs
• JSON and Parquet File Formats
• Loading Data through Different Sources
• Spark – Hive Integration
Module 7: Machine Learning using Spark MLlib
Learning Objective: In this module you will learn about why machine learning is needed, different Machine Learning techniques/algorithms and their implementation using Spark MLlib.
Topics:
• Why Machine Learning?
• What is Machine Learning?
• Where Machine Learning is Used?
• Face Detection: USE CASE
• Different Types of Machine Learning Techniques
• Introduction to MLlib
• Features of MLlib and MLlib Tools
• Various ML algorithms supported by MLlib
Module 8: Deep Dive into Spark MLlib
Learning Objective: In this module you will be implementing various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and many more.
Topics:
• Supervised Learning
• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest
• Unsupervised Learning
• K-Means Clustering & How It Works with MLlib
• Analysis on US Election Data using MLlib (K-Means)
Module 9: Understanding Apache Kafka and Apache Flume
Learning Objective: In this module, you will understand Kafka and Kafka Architecture. Afterwards you will go throughthe details of Kafka Cluster and you will also learn how to configure different types of Kafka Cluster. At last, you will see how messages are produced and consumed using Kafka API’s in Java. You will also get an introduction to Apache Flume, its basic architecture and how it is integrated with Apache Kafka for event processing. You will learn how to ingest streaming data using flume.
Topics:
• Need for Kafka
• What is Kafka?
• Core Concepts of Kafka
• Kafka Architecture
• Where is Kafka Used?
• Understanding the Components of Kafka Cluster
• Configuring Kafka Cluster
• Kafka Producer and Consumer Java API
• Need of Apache Flume
• What is Apache Flume?
• Basic Flume Architecture
• Flume Sources
• Flume Sink Flume Channels
• Flume Configuration
• Integrating Apache Flume and Apache Kafka
Module 10: Apache Spark Streaming - Processing Multiple Batches
Learning Objective: In this module, you will work on Spark streaming which is used to build scalable fault-tolerant streaming applications. You will learn about DStreams and various Transformations performed on the streaming data. You will get to know about commonly used streaming operators such as Sliding Window Operators and Stateful Operators.
Topics:
• Drawbacks in Existing Computing Methods
• Why Streaming is Necessary?
• What is Spark Streaming?
• Spark Streaming Features
• Spark Streaming Workflow
• How Uber Uses Streaming DataStreaming Context & DStreams
• Transformations on DStreams
• Describe Windowed Operators and Why it is Useful
• Important Windowed Operators
• Slice, Window and ReduceByWindow Operators
• Stateful Operators
Module 11 - Apache Spark Streaming – Data Sources
Learning Objective: In this module, you will learn about the different streaming data sources such as Kafka and flume. At the end of the module, you will be able to create a spark streaming application.
Topics:
• Apache Spark Streaming: Data Sources
• Streaming Data Source Overview
• Apache Flume and Apache Kafka Data Sources
• Example: Using a Kafka Direct Data Source
• Perform Twitter Sentimental Analysis Using Spark Streaming
Module 12: Implementing an End-to-End Project
Learning Objective: In this module, you will be learning how to use different concepts of Spark to build a project.
Module 13: Spark GraphX (Self-Paced)
Learning Objective: In this module, you will be learning the key concepts of Spark GraphX programming concepts and operations along with different GraphX algorithms and their implementations.
Topics:
• Introduction to Spark GraphX
• Information about a Graph
• GraphX Basic APIs and Operations
• Spark GraphX Algorithm
• PageRank
• Personalized PageRank
• Triangle Count
• Shortest Paths
• Connected Components
• Strongly Connected Components
• Label Propagation
• Examples:
• The Traveling Salesman problem
• Minimum Spanning Tree