What you'll learn

A comprehensive course for beginners who want to step into the world of data science and ML.



⭐️ The three main pillars of data science and ML - Programming, Math, and Statistics.



⭐️ How to set up a python environment for Data Science / ML with Anaconda



⭐️ Everything from basic data structures to data extraction using python programming.



⭐️ How to work with essential 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.



⭐️ All pre-requisites and pre-work before starting Google’s(or any) ML program.

Course Outline

Course Curriculum

To make sure this course aligns well with your learning plan, you can start learning for free right now by checking out the lessons that are marked free for preview. More lectures coming soon!

Course Outline

Resources & Community

Installing Python on your system

Installing Miniconda and setting up a new environment

Introduction to Jupyter Notebooks and Google Colabs

Google Colabs - Alternative to jupyter notebooks

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 comprehensions

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

[GUIDE]: Best practices for writing Python code

Extracting data from APIs

Assignment - Data extraction and formatting

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

Broadcasting

Pseudorandom number generation

Performing vectorized functions

Coding Maclaurin series expansions without loops

Assignment - Series expansion

The most important data manipulation package

Introduction to Pandas Series

Introduction to DataFrames

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 I

EDA - part II - data cleaning using pandas

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

What's Next!

Variables, constants, coefficients and equation

Functions

Linear functions

Exponential functions

Coding exponential and sigmoid functions

Logarithmic function

Assignment - Exponential function

Resource: important mathematical notations for ML

Linear Algebra - A pillar of Machine Learning

Introduction to scalars and vectors

Vector arithmetics

Norms

L1 Norm

L2 and Squared L2 Norm

Dot product

Matrices and Tensors

Common operations on matrices

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

Assignment - Solve a real world problem using linear regression

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 G.D. from scratch

-----Assignment on how to train a LR model for both m and c-----

Why - Quantify 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

Why inferential statistics

Introduction to hypothesis testing

How to interpret statistical tests

Confidence Intervals

Errors in statistical tests

Critical values

P-values

Parametric significance tests

Student's t-Test

Chi-square test

ANOVA test

Assignment - Explaining a hypothesis test

To help you decide



Take the course if...

✓ You are looking for the first steps to start your career in data science or ML.


✓ You want to learn how math and statistics when paired with programming drive ML algorithms.


✓ You’ve struggled to find a resource that covers all the basic foundations of data science with practical examples and projects.


✓ You want to learn to work with all the essential Python libraries for Data Analysis, Cleaning, and Visualization.





Don't take it if...



✖️ You are looking for a quick way to hack into data science.



✖️ You think ML or data science is simply using libraries and pre-defined functions.



✖️ You aren’t serious about a long-lasting successful career in the field.

Top challenges with learning Data Science & Machine Learning

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It is hard to be a successful Data Scientist or ML Engineer without understanding the three main founding pillars - Math, Statistics, and Programming.

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Every good data science or ML course in the market asks for a good understanding of Python programming, linear algebra, calculus, etc. but there is no course that explains why you need these and how they are used.

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Most folks go on a snooze fest😴 when learning theoretical concepts of Math and Statistics.

How this course solves these challenges for you

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This course covers all the pre-requisites and prework listed by the official Google’s ML course and more.

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I am delivering this course to not only get you started in data science but to give you a strong foundation that will further help you in breaking down hard problems.

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To save you from boredom, I have added interesting examples, quality videos, and practical code-based verifications that will keep you engaged.

Pricing - The most cost-effective course on Internet!

Complete Course Pricing
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To clarify your doubts



Answers to some of your questions

Are there any prerequisites for this course?

A computer (Windows/Mac/Linux). You must know basic school-level arithmetics. That's it! No previous coding experience is needed. All tools and software used in this course will be free.



Who is this course for?

  • An aspiring data professional who is looking to build some solid foundations for data science.
  • You are aiming to become a Data Analyst, Data Scientist, ML/DL Practitioner.
  • You want to learn how to analyze data with python, pandas, numpy, and matplotlib.
  • You want to learn how linear algebra and calculus train ML models.



Is it even necessary to learn math and statistics?

We feel you do need to have a decent understanding of how math drives data algorithms. You don't need to be a Gold Medalist. But without understanding the working of an algorithm(which is mostly math-based), you'll have a hard time growing beyond a certain level.



I hate math and I find it boring. Is there a workaround?

I have tried my best to make the lectures as intuitive as possible. Not only have I taught the concept, but I also walk you through real-world examples of where it is used in Python.



Do you provide a certificate after completion?

Yes, we definitely do.



Can I download the videos?

Definitely. You can download any and all lessons for personal use. I understand how important time is when you are commuting, flying, or in a poor network zone.



Why are you using Python?

Because I haven't found a more versatile and easy-to-learn language that gets the job done.



How long will I have access to the course?

You have access to the course for at least 2 years. You'll need to renew it after 2 years if you'll still need it.

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