##
**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!

- Why learn programming? (10:34)
- Variables and data types (13:56)
- Working with variables (8:22)
- Working with strings (9:48)
- Formatted strings to add variables to strings (11:27)
- Introduction to Python Lists (10:44)
- Indexing and slicing lists (8:41)
- Control flow - if/else, conditional blocks & operators (21:21)
- Loops - doing something repeatedly (16:13)
- Dictionaries (9:15)
- Iterating over a dict (15:45)
- List comprehensions (7:18)
- Sets and tuples (10:52)
- Functions (10:08)
- Functions with multiple parameters (11:18)
- Object oriented programming - classes and objects (10:43)
- Object-oriented contd. (21:42)
- Python scripts, modules and libraries (15:01)
- Working with external libraries (6:29)
- Files - I/O (13:32)
- Extracting data from APIs (14:42)
- Assignment - Data extraction and formatting

- Why NumPy for ML and DS? (7:42)
- NumPy arrays (17:03)
- Placeholder functions to create NumPy arrays (10:00)
- Indexing, slicing and subsetting (11:16)
- Boolean Indexing and filtering (9:54)
- Arithmetic operations with NumPy arrays (8:57)
- Reshaping arrays (10:51)
- Transposing and flattening (3:17)
- Concatenating and splitting (10:37)
- Broadcasting (12:36)
- Pseudorandom number generation (11:00)
- Performing vectorized functions (13:22)
- Coding Maclaurin series expansions without loops (18:35)
- Assignment - Series expansion

- The most important data manipulation package (3:59)
- Introduction to Pandas Series (11:52)
- Introduction to DataFrames (11:41)
- Indexing in DataFrames (11:05)
- LOC and ILOC (11:22)
- Comparison operators and filtering (8:49)
- Inserting, modifying and deleting data (12:37)
- Merging two DataFrames (14:14)
- Merging contd. (4:54)
- Applying arithmetic operations (13:36)
- Mapping and applying a function to a series (11:13)
- Checking for null values (8:44)
- Grouping data (12:56)
- Describing data (9:13)
- Sorting and ranking data (11:01)
- Reading files and writing dataframes to files (9:04)
- Exploratory data analysis - part I (15:46)
- EDA - part II - data cleaning using pandas (22:07)

- Why learning to visualize data is crucial (4:56)
- Understanding the Matplotlib API hierarchy (12:18)
- Adding color, styles to plots (10:20)
- Adding labels, ticks, and legends to plots (12:16)
- Plotting from DataFrames and Series (8:42)
- Line plots (6:10)
- Bar plots (11:36)
- Scatter plots (11:39)
- Histograms and KDEs - Density plots (12:14)
- Boxplots (13:58)
- Seaborn for beautiful plots (11:38)

- Linear Algebra - A pillar of Machine Learning (7:52)
- Introduction to scalars and vectors (8:31)
- Vector arithmetics (8:52)
- Norms (8:23)
- L1 Norm (6:08)
- L2 and Squared L2 Norm (6:51)
- Dot product (9:27)
- Matrices and Tensors (11:45)
- Common operations on matrices
- Matrix multiplication (9:27)
- Special types of matrices
- Transforming vector spaces (10:06)
- Linear transformation using matrix notation (10:06)
- Representing linear equations using matrices and vectors (7:04)
- Solving systems of linear equations (14:20)
- Introduction to linear regression using matrix notation (11:15)
- Solving a linear regression problem (10:07)
- Assignment - Solve a real world problem using linear regression

- Calculus for ML - How does a model learn? (7:11)
- Introduction to derivatives and limits (8:37)
- Computing derivatives of linear functions (8:48)
- Derivative of a non-linear function (10:48)
- Derivative rules (7:20)
- Chain rule (6:20)
- Local and global_minima (6:50)
- Introduction to partial derivatives (7:04)
- Gradient Descent (12:37)
- Math behind linear regression model (11:46)
- Training the linear regression model using G.D. from scratch (22:35)

- Why - Quantify risk and uncertainty with Statistics (6:47)
- Types of data (7:03)
- Estimates of Location - Mean, median and others (12:40)
- Estimate of Variability - Variance, Std deviation (10:50)
- Correlation and Covariance (15:22)
- Random Variables - Discrete and Continuous (8:58)
- Probability Mass Function(PMF) - Describing discrete variables (9:24)
- Probability Density Function(PDF) - describing continuous variables (11:26)
- Introduction to Conditional Probability (9:17)
- Cumulative Distribution Function(CDF) (6:24)
- Gaussian distribution (13:55)
- Binomial distribution (13:01)
- Poisson distribution (10:51)
- Law of large numbers (12:20)
- Central limit theorem (CLT) (5:02)
- Verifying CLT (4:39)

**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**

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

**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.**

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

##
**How this course ****solves**** these challenges for you**

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

**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.**

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

**For students in India 👉**

You can pay via UPI or cards using this Razorpay link.

Make sure you add the registered email address while making the payment.

You will have access to the course within 24 hours of the payment. For any queries, write to [email protected]

**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.