Foundations of Data Science & Machine Learning

Essentials of Programmning, Mathematics and Statistics to get started with Data Science and Machine Learning.

4.75 (29 reviews)
Udemy
platform
English
language
Programming Languages
category
instructor
298
students
21 hours
content
Feb 2022
last update
$64.99
regular price

What you will learn

Learn the essentials - the three main pillars of data science and ML - Programming, Math, and Statistics.

Everything from basic data structures to data extraction using python programming. Learn to work with data libraries: NumPy, Pandas, Matplotlib, and Seaborn.

How linear algebra and calculus underpin the training of ML models.

How Statistics enables you to describe data and quantify uncertainty in an experiment.

Cover all pre-requisites and pre-work before starting any Google’s(or any) data science or ML program.

Build models from scratch, learn the math behind, program

Description

To have a successful, long-lasting career in Data Science or Machine Learning, you'll need a solid understanding of the three pillars of DS and ML namely, Programming, Math, and Statistics.

The course is based on Google's recommendations before starting any ML course.

It is a comprehensive yet compact course that not only covers all the essentials, pre-requisites, & pre-work but also explains how each concept is used computationally and programmatically (python).

We follow the following path in this course:

  • Learn to set up a professional python environment

  • Learn to program in python using fundamental data structures and methods.

  • Learn to work with data science libraries

  • NumPy for Multidimensional Arrays

  • Pandas for Data Manipulation

  • Matplotlib and Seaborn for Data Visualization

  • Basics of Algebra - From variables to all important functions

  • Linear Algebra for Machine Learning - data representation, vector norms, solving linear regression problems.

  • Calculus that trains ML models - learn how gradient descent works to minimize the loss function.

  • Training a linear regression model from scratch without using any ML package

  • Statistics, data distributions, and basics of probability

After completing this course, you'll be ready to straight away start working on:

  • Data Analysis projects

  • Pick up any ML course

  • Start with a Data Science course

  • Start with the Predictive analytics course

  • Enroll for any fast-paced Bootcamp course after covering all the basics.

Content

Welcome to the course!

Course Outline
Resource & Community

Environment setup for coding in Python

Installing Python on the system
Installing Miniconda and setting up a new environment
Introduction to Jupyter Notebooks and Google Colabs
Google Colabs - Alternative to jupyter notebooks

Introduction to Python Programming

Why learn programming?
Variables and data types
Working with Variables
Working with Strings
Formatted strings to add variables to strings
Introduction to python lists
Indexing and slicing lists
Control flow - if/else, conditional blocks & operators
Loops - doing something repeatedly
Dictionaries
Iterating over a dict
List comprehension
Sets and tuples
Functions
Functions with multiple parameters
Object oriented programming - classes and objects
Object-oriented contd.
Python scripts, modules and libraries
Working with external libraries
Files - I/O
Extracting data from APIs
Assignment - Data extraction and formatting

Essential NumPy - Working with multidimensional arrays

Why NumPy for ML and DS?
NumPy Arrays
Placeholder functions to create NumPy arrays
Indexing, slicing and subsetting
Boolean Indexing and filtering
Arithmetic operations with NumPy arrays
Reshaping arrays
Transposing and flattening
Concatenating and splitting
Pseudorandom number generation
Broadcasting
Performing vectorised operations
Coding Maclaurin series expansions without loops
Assignment - Series expansion

Pandas - How to work with real-world tabular data

The most important data manipulation package
Introduction to Pandas Series
Introduction to Pandas DataFrame
Indexing in DataFrames
LOC and ILOC
Comparison operators and filtering
Inserting, modifying and deleting data
Merging two dataframes
Merging contd.
Applying arithmetic operations
Mapping and applying a function to a series
Checking for null values
Grouping data
Describing data
Sorting and ranking data
Reading files and writing dataframes to files
Exploratory Data Analysis - part 1
EDA - part 2 - data cleansing using pandas

Data Visualisation with Matplotlib & Seaborn

Why learning to visualize data is crucial
Understanding the Matplotlib API hierarchy
Adding color, styles to plots
Adding labels, ticks, and legends to plots
Plotting from DataFrames and Series
Line plots
Bar plots
Scatter plots
Histograms and KDEs - Density plots
Boxplots
Seaborn for beautiful plots

Basics of Algebra

Variables, constants, coefficients and equation
Functions
Linear functions
Exponential functions
Coding exponential and sigmoid functions
Logarithmic functions
Assignment - exponential functions
Resource: important mathematical notations for ML

Essential Linear Algebra for Machine Learning

Linear Algebra - A pillar of Machine Learning
Introduction to scalars and vectors
Vector Arithmetics
Vector Norms
L1 Norm
L2 and L2 squared Norm
Dot product
Matrices and Tensors
Common matrix operations
Matrix multiplication
Special types of matrices
Transforming vector spaces
Linear transformation using matrix notation
Representing linear equations using matrices and vectors
Solving systems of linear equations
Introduction to linear regression using matrix notation
Solving a linear regression problem

Calculus for ML and Deep Learning

Calculus for ML - How does a model learn?
Introduction to derivatives and limits
Computing derivatives of linear functions
Derivative of a non-linear function
Derivative rules
Chain rule
Local and global minima
Introduction to partial derivatives
Gradient descent
Math behind linear regression model
Training the linear regression model using gradient descent from scratch

Descriptive Statistics for Data Science

Describing data and Quantifying risk and uncertainty with Statistics
Types of Data
Estimates of Location - Mean, median, and others
Estimate of Variability - Variance, Std deviation
Correlation and Covariance
Random Variables - Discrete and Continuous
Probability Mass Function(PMF) - Describing discrete variables
Probability Density Function(PDF) - describing continuous variables
Introduction to Conditional Probability
Cumulative Distribution Function(CDF)
Gaussian Distribution
Binomial Distribution
Poisson Distribution
Law of Large Numbers
Central Limit Theorem (CLT)
Verifying CLT by programming

Screenshots

Foundations of Data Science & Machine Learning - Screenshot_01Foundations of Data Science & Machine Learning - Screenshot_02Foundations of Data Science & Machine Learning - Screenshot_03Foundations of Data Science & Machine Learning - Screenshot_04

Reviews

Aniket
February 23, 2022
I am about to finish this course and I have to say that it's the best choice I've ever made by choosing this course. It actually covers all the fundamentals required for ML.

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4286316
udemy ID
9/7/2021
course created date
9/14/2021
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