Course Content - Machine Learning

Machine Learning with Python Course Content    

Introduction of Python    
    Overview of Python
    Why Python?
    When to use Python?
    Python types?

Python Installation    
    Setting Enivronment- Install Python Windows , unix, linux.
    Running basic Python commands.

Basic Syntax/ Construct of Python    
    "Programming(interactive/script),identifiers,Reserved words, line/indentation,multi-
line statements."
    Accessing/Parsing command -line arguments.

Python Variable Types:    
    Variables and Naming rules
    Built-in Data Types in Python – Numeric: int, float, complex.
    Sequence Types: list, tuple, range
    Text Sequence: Str (String).
    Set Types : Set , Forzenset
    Mapping Types  : Dictionary
    Data type Conversions between built-in types
    Constants: False, True, None, NotImplemented, Ellipsis,debug.

Python Basic Operators:    
    Basic Operators: Arithmetic, Comparison, Assignment, Identity,Logical, Bitwise, Membership.
    Python Operatots Precedence : highest to lowest

Python Decision Making:    
    if, else, nested if, range(), break, continue, elif, Single Statement Suites.

Python Loops:    
    while, for, Iterating by Sequence Index, nested, Loop Control Statements, break.

Python Functions:    
    Built-in Functions : len(),slice(),zip() ,random()etc
    User Defined Functions : How to create / call a function ,Function arguments
    "Anonymous Function :  Lambda 

Python Strings:    
    "Accessing Values in Strings, Updating Strings, Escape Characters, String Formatting
    Built-in String Methods."

Python Lists:    
    "Accessing Values in Lists, Basic List Operations, Built-in List Functions & Methods.
    List Slicing ,List comprehension ,sorting,deletion

Python Sets & Tuples :    
    Python Sets and Tuples and its operations.

Python Dictionaries :    
    Accessing Values in dictionary, Basic dictionary Operations, Built-in dictionary Functions & Methods.

Python Modules & Packages :    
    Exploring Built-in modules ,writing modules
    Packages and create your own packages

Python Classes/Objects     
    Creating Classes, Class Inheritance , Objects and Instances
    Encapsulation of data ,Functions vs Methods
    Iterators , Generators and its expressions

Python Errors & Exceptions :    
    Syntax Errors , Exceptions
    Handling and Raising an exception, User Defined Exception

Python Standard Libraries :    
    Operating System(OS) Interface , Command Line arguments 
    Regular Expression (String Pattern matching)
    Date and Time ,Mathematics
    Networking: Sending Email, Multithreading, GUI Programming.

Python File Handling    
    Open a File, Read from a File, Write into a File, File Position, Looping over a file object.
     Pickle (Serialize and Deserialize Python Objects).
    Shelve (Python Object Persistence)

Machine Learning    
NumPy(Mathematical computing with python)    
    "Arrays and Matrices, ND-array object, Array indexing, Datatypes, Array math
    Std Deviation, Conditional Prob, Covariance and Correlation."
SciPy(Scientific computing with python)    
    Builds on top of NumPy, SciPy and its characteristics, subpackages.
    Cluster, fftpack, linalg, signal, integrate, optimize, stats; Bayes Theorem using SciPy.
Data Visualization (Matplotlib)    
    Plotting Grapsh and Charts (Line, Pie, Bar, Scatter, Histogram, 3-D).
    The Matplotlib API.

Data Analysis and Data Manipulation with Python (Pandas)    
    Dataframes, NumPy array to a dataframe.
    Import Data (csv, json, excel, sql database).
    "Data operations: View, Select, Filter, Sort, Groupby, Cleaning, Join/Combine,
Handling Missing Values."

Machine Learning    
    "Introduction to Machine Learning(ML) : Definition, Concepts and Terminology , Lifecycle

    Problem categories of ML : Classification ,Clustering,Regression,Optimization
    Learning Sub-Fields : Supervised ,Unsupervised,Semi-Supervised , Reinforcement,Deeplearning
    "Basic Performance measures  : MSE , MAE , NMSE , ROC / AUC , Confussion Matrix 
Accuracy , 
    Precision / Recall etc."
    Installation of Python Packages for ML and Setting up Environment ( Anaconda Distribution) 
    Supervised Algorithms : Linear , Logistic , CART , Naïve Bayes , KNN ,  Decision Tree, 
    Random Forest , SVM and with case study(for all)
    Unsupervised Algorithms  : K-Means , PCA  and with case study(for all)
    Recommender Systems 
    Dimensionality Reduction

"Natural Language Processing, Machine
Learning (Scikit-Learn)"    
    Introduction to Natural Language Processing (NLP) ~ NLTK package
    Text Processing   :  Tokenization , Stemming , Lemmatization ,Stop word removal
    Text Feature Engineering : Syntactical Parsing , Entity Parsing , Statistical Features
    NLP Applications 
    Text Mining with Python ( web scraping)
    Text Analysis  
    Sentiment Analysis with the Twitter case study

Advanced Topics :    
    Deep Learning ( Tensorflow ) Overview
    Computer Vision Overview 
    Chatbot overview
    Important Scientific Research Papers 
    "1.) IBM Certification for Machine learning 
2.) IBM Certification for Chatbot."

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