Machine Learning Masters Program Training Course

• What can Python do?
• Why Python?
• Good to know
• Python Syntax compared to other programming languages
• Python Install

• The print statement
• Comments
• Python Data Structures & Data Types
• String Operations in Python
• Simple Input & Output
• Simple Output Formatting
• Operators in python

• Indentation
• The If statement and its’ related statement
• An example with if and it’s related statement
• The while loop
• The for loop
• The range statement
• Break &Continue
• Assert
• Examples for looping

• Create your own functions
• Functions Parameters
• Variable Arguments
• Scope of a Function
• Function Documentations
• Lambda Functions& map
• n Exercise with functions
• Create a Module
• Standard Modules

• Errors
• Exception handling with try
• handling Multiple Exceptions
• Writing your own Exception

• File handling Modes
• Reading Files
• Writing& Appending to Files
• Handling File Exceptions
• The with statement

• New Style Classes
• Creating Classes
• Instance Methods
• Inheritance
• Polymorphism
• Exception Classes & Custom Exceptions

• Iterators
• Generators
• The Functions any and all
• With Statement
• Data Compression

• List Comprehensions
• Nested List Comprehensions
• Dictionary Comprehensions
• Functions
• Default Parameters
• Variable Arguments
• Specialized Sorts

• namedtuple()
• deque
• ChainMap
• Counter
• OrderedDict
• defaultdict
• UserDict
• UserList
• UserString

• Introduction
• Components and Events
• An Example GUI
• The root Component
• Adding a Button
• Entry Widgets
• Text Widgets
• Check buttons

• Introduction
• Installation
• DB Connection
• Creating DB Table
• INSERT, READ, UPDATE, DELETE operations
• COMMIT & ROLLBACK operation
• handling Errors

• Introduction
• A Daytime Server
• Clients and Servers
• The Client Program
• The Server Program

• sleep
• Program execution time
• more methods on date/time

• Filter
• Map
• Reduce
• Decorators
• Frozen set
• Collections

• Split
• Working with special charLearnoVitars, date, emails
• Quantifiers
• Match and find all
• charLearnoVitar sequence and substitute
• Search method

• Class and threads
• Multi-threading
• Synchronization
• Treads Life cycle
• use cases

• Introduction
• Facebook Messenger
• Openweather

• 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
• State what role Big Data and Hadoop, Python, R and Machine Learning play in Data Science

• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis
• Visualization of Data

• 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

• 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?

• Introduction to Dimensionality
• Why Dimensionality Reduction
• PCA
• Factor Analysis
• Scaling dimensional model
• LDA

• 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

• 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?

• What are Association Rules?
• Association Rule Parameters
• Calculating Association Rule Parameters
• Recommendation Engines
• How Recommendation Engines work?
• Collaborative Filtering
• Content Based Filtering

• 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
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• What is Time Series Analysis?
• Importance of TSA
• Components of TSA
• White Noise
• AR model
• MA model
• ARMA model
• ARIMA model
• Stationarity
• ACF & PACF

• What is Model Selection?
• Need of Model Selection
• Cross – Validation
• What is Boosting?
• How Boosting Algorithms work?
• Types of Boosting Algorithms
• Adaptive Boosting

• Why do we need Graphical Models?
• Introduction to Graphical Model
• How does Graphical Model help you deal with uncertainty and complexity?
• Types of Graphical Models
• Graphical Modes
• Components of Graphical Model
• Representation of Graphical Models
• Inference in Graphical Models
• Learning Graphical Models
• Decision theory
• Applications

• What is Bayesian Network?
• Advantages of Bayesian Network for data analysis
• Bayesian Network in Python Examples
• Independencies in Bayesian Networks
• Criteria for Model Selection
• Building a Bayesian Network

• Inference
• Complexity in Inference
• Exact Inference
• Approximate Inference
• Monte Carlo Algorithm
• Gibb’s Sampling
• Inference in Bayesian Networks

• General Ideas in Learning
• Parameter Learning
• Learning with Approximate Inference
• Structure Learning
• Model Learning: Parameter Estimation in Bayesian Networks
• Model Learning: Parameter Estimation in Markov Networks

For the first week of this course, you will learn how to understand the exploration-exploitation trade-off in sequential decision-making, implement incremental algorithms for estimating action-values, and compare the strengths and weaknesses to different algorithms for exploration. For this week’s graded assessment, you will implement and test an epsilon-greedy agent.

When you’re presented with a problem in industry, the first and most important step is to translate that problem into a Markov Decision Process (MDP). The quality of your solution depends heavily on how well you do this translation. This week, you will learn the definition of MDPs, you will understand goal-directed behavior and how this can be obtained from maximizing scalar rewards, and you will also understand the difference between episodic and continuing tasks. For this week’s graded assessment, you will create three example tasks of your own that fit into the MDP framework.

Once the problem is formulated as an MDP, finding the optimal policy is more efficient when using value functions. This week, you will learn the definition of policies and value functions, as well as Bellman equations, which is the key technology that all of our algorithms will use.

This week, you will learn how to compute value functions and optimal policies, assuming you have the MDP model. You will implement dynamic programming to compute value functions and optimal policies and understand the utility of dynamic programming for industrial applications and problems. Further, you will learn about Generalized Policy Iteration as a common template for constructing algorithms that maximize reward. For this week’s graded assessment, you will implement an efficient dynamic programming agent in a simulated industrial control problem.

• 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

• Tokenization
• Frequency Distribution
• Different Types of Tokenizers
• Bigrams, Trigrams & Ngrams
• Stemming
• Lemmatization
• Stopwords
• POS Tagging
• Named Entity Recognition

• Syntax Trees
• Chunking
• Chinking
• Context Free Grammars (CFG)
• Automating Text Paraphrasing

• Machine Learning: Brush Up
• Bag of Words
• Count Vectorizer
• Term Frequency (TF)
• Inverse Document Frequency (IDF)

• Converting text to features and labels
• Multinomial Naive Bayes Classifier
• Leveraging Confusion Matrix