Machine Learning using Python 

Course Overview


This Machine learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. I learn about Supervised vs. Unsupervised Learning, look into how statistical Modeling relates to Machine 

Expectations and Goals:

This course helps participants understand what data scientists do, the problems they solve, and the tools and techniques they use. Through in-class simulations, participants apply data science methods to real-world challenges in different industries and, ultimately, prepare for data scientist roles in the field.

This course is suitable for students, developers, data analysts, and statisticians with basic knowledge of Computer Science and python programming.


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Python Data Structure
Introduction to list
Work on Tuples
List Comprehension
Set Comprehension
Dictionary Comprehension

Module 4:

Conditional and Iterative statements
if Statements
Looping Techniques
for Statements
The range function
BREAK Statement

Module 5:

Basic Operators
Types of Operators
Python Arithmetic Operators
Python Comparison Operators
Python Assignment Operators
Python Logical Operators
Python identify Operators
Python Operators Precedence

Module 6:

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Introduction to Threads in Python
thread module
threading module
Introduction to Pipes in python
anonymous pipes

Module 10:

The Machine Learning Landscape
What is Machine Learning?
Why Use Machine Learning?
Types of Machine Learning Systems
Supervised / Unsupervised Learning
Batch and Online Learning
Instance-Based Versus Model-Based Learning
Main Challenges of Machine Learning
Insufficient Quantity of Training Data

Module 11:

NumPy Basics: Arrays and Vectorized Computation
The NumPy ndarray: A Multidimensional Arry Object
Creating ndarrays
Data Types for ndarrays
Data Types for ndarrays
Arithmetic with NumPy Arrays

Basic Indexing and Slicing

Boolean Indexing

Module 12:

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End-to-End Machine Learning Project
Working with Real Data
Look at the Big Picture
Frame the Problem
Select a Performance Measure
Check the Assumptions
Get the Data
Create the Workspace
Download the Data
Take a Quick Look at the Data Structure
Create a Test Set
Discover and Visualize the Data to Gain insights
Visualizing Geographical Data
Looking for Correlations
Experimenting with Attribute Combinations
Prepare the Data for Machine Learning Algorithms

Data Cleaning
Handling Text and Categorical Attributes
Custom Transformers
Feature Scaling
Transformation Pipelines
Select and Train a Model
Training and Evaluating on the Training Set
Better Evaluation Using Cross-Validation
Fine-Tune Your Model


Module 14:

Dimensionality Reduction 
The Curse of Dimensionality 
Main Approaches for Dimensionality Reduction 
Manifold Learning 
Preserving the Variance 
Principal Components 
Projecting Down to d Dimensions 
Using Scikit-Learn 
Explained Variance Ratio 
Choosing the Right Number of Dimensions 
PCA for Compression 
Randomized PCA 
Incremental PCA 


Module 11:

Elastic NetEarly StoppingLogistic RegressionEstimating ProbabilitiesTraining and Cost FunctionDecision BoundariesSoftmax Regression

Support Vector Machines
Linear SVM Classification
Soft Margin Classification
Nonlinear SVM Classification
Polynomial Kernal
Adding Similarity Features
Gaussian RBF Kernel

Module 17:

Decision Tree
Training and visualizing a decision tree
Making predictions
Estimating class probabilities
The CART(Classification and Regression Tree) training algorithm
Computational Complexity
Gini impurity or Entropy?
Regularization hyperparameters

Module 18:

Project work and documentation

Module 19:

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Introduction to Python
History of Python
Using Python interpreter
The Interpreter and its Environment
Using Python as Calculator
First Step towards Programming

Topics Covered:

Module 1:

Basic Syntax
Python Identifiers
Python Keywords
Multi- Line Statements
Quotation in Python
Python Comments in Python
Command Line Arguments
Parsing Command-Line Arguments

Module 2:

Variable Types
Assigning Values to Variables
Multiple Assignment
Standard Data Types
Python Numbers
Python Strings
Data Type Conversion

Module 3:

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Defining a Function
Calling a Function
Global vs. Local variables
Locating Modules
Creating Modules
Packages in Python

Module 7:

Classes in Python
Classes and instances
Classes methods calls
inheritance and Compositions
Static and Class Methods
Bound and Unbound Methods
Operator Overloading

Module 8:

Exception Handling in Python Programming
Default Exception Handler
Catching Exceptions
Raise an Exception
Userdefined Exception

Module 9:

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Fancy Indexing

Transposing Arrays and Swapping Axes

Universal Functions: Fast Element-Wise Array Functions

Array-Oriented Programming with Arrays

Expressing Conditional Logic as Array Operations

Mathematical and Statistical Methods

Methods for Boolean Arrays


Unique and Other Set Logic

File Input and Output with Arrays
Linear Algebra
Pseudorandom Number Generation
Example: Random Walks
Simulating Many Random Walks at Once

Getting Started with pandas
Introduction to pandas Data Structures
Index Objects
Essential Functionality
Dropping Entries from an Axix
Indexing, Selection, and Filtering
Integer Indexes
Arithmetic and Data Alignment

Module 13:

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Grid SearchRandomized SearchEnsemble MethodsAnalyze the Best Models and Their ErrorsEvaluate Your System on the Test SetLaunch, Monitor and Maintain Your System

Training a Binary Classifier
Performance Measures
Measuring Accuracy Using Cross-Validation
Confusion Matrix

Module 15:

Training Models
Linear Regression
The Normal Equation
Computational Complexity
Gradient Descent
Stochastic Gradient Descent
Mini-batch Gradient Descent
Polynomial Regression
Learning Curves
Regularized Linear Models
Ridge Regression
Lasso Regression


Module 16:

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Thank You