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Foundations of Data Science & ML
Welcome to the course
Course Outline (5:35)
Resources & Community
Environment setup for coding in Python
Installing Python on your system (8:25)
Installing Miniconda and setting up a new environment (11:57)
Introduction to Jupyter Notebooks and Google Colabs (15:15)
Google Colabs - Alternative to jupyter notebooks (4:45)
Introduction to Programming in Python
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
Introduction to NumPy
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
Pandas - How to work with real-world data
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)
Data Visualisation with Matplotlib & Seaborn
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)
Basics of Algebra
Variables, constants, coefficients and equation (6:34)
Functions (9:31)
Linear functions (8:55)
Exponential functions (9:51)
Coding exponential and sigmoid functions (2:51)
Logarithmic function (8:55)
Assignment - Exponential function (7:37)
Resource: important mathematical notations for ML
Essential Linear Algebra for Machine Learning
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 (9:12)
Matrix multiplication (9:27)
Special types of matrices (10:22)
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)
Calculus for ML and DL
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)
Descriptive Statistics for Data Science
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)
Why NumPy for ML and DS?
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