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👉 Machine Learning course has 150+hours of industry focused and extremely simplified content with no prerequisites covering Python, Maths, Data Analysis, Machine Learning and Deep Learning. 

💫 If you are familiar with python, you can skip #module - 1 - 9

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#Module 1 : Fundamental of programming

👉 1. How to utilise course

👉 2. Python for Data Science Introduction

👉 3. Python for Data Science: Functions

👉 4. Python for Data Science : Numpy

👉 5. Python for Data Science: Matploitb

👉 6. Python for Data Science: Pandas

👉 7. Python for Data Science: Computational Complexity

👉 8. SQL

Let's start our content

💫 Use #Module to travel easily throughout the channel

#Module 1 : How to utilise this course

Follow these steps to get a great result

#Module 2 : Python for Data Science Introduction
Section 2 is divided into sub sections

👉 2.1 Python, Anaconda and relevant packages installations

👉 2.2 Why learn Python?

👉 2.3 Keywords and identifiers

👉 2.4 comments, indentation and statements

👉 2.5 Variables and data types in Python

👉 2.6 Standard Input and Output

👉 2.7 Operators

👉 2.8 Control flow: if else

👉 2.9 Control flow: while loop

👉 2.10 Control flow: for loop

👉 2.11 Control flow: break and continue

#Module 3 : Python for Data Science: Data Structures
Section 3 is divided into sub sections

👉 3.1 Lists

👉 3.2 Tuples part 1

👉 3.3 Tuples part 2

👉 3.4 Sets

👉 3.5 Dictionary

👉 3.6 Strings

#Module 4 : Python for data science: Functions
Section 4 is divided into sub sections
👉 4.1 Introduction

👉 4.2 Types of functions

👉 4.3 Function arguments

👉 4.4 Recursive functions

👉 4.5 Lambda functions

👉 4.6 Modules

👉 4.7 Packages

👉 4.8 File Handling

👉 4.9 Exception Handling

👉 4.10 Debugging Python

#Module 5 : Python for Data Science: Numpy
Section 5 is divided into sub sections

👉 5.1 Numpy Introduction

👉 5.2 Numerical operations on Numpy

#Module 6 : Python for Data Science: Matplotlib

👉 6.1 Getting started with Matplotlib

#Module 7 : Python for Data Science: Pandas
Section 7 is divided into sub sections

👉 7.1 Getting started with pandas

👉 7.2 Data Frame Basics

👉 7.3 Key Operations on Data Frames

#Module 8 : Python for Data Science: Computational Complexity

Section 8 is divided into sub sections

👉 8.1 Space and Time Complexity: Find largest number in a list

👉 8.2 Binary search

👉 8.3 Find elements common in two lists

👉 8.4 Find elements common in two lists using a Hashtable/Dict

#Module 9 : SQL

Section 9 is divided into sub sections

👉 9.1 Introduction to Databases

👉 9.2 Why SQL?

👉 9.3 Execution of an SQL statement.

👉 9.4 IMDB dataset

👉 9.5 Installing MySQL

👉 9.6 Load IMDB data.

👉 9.7 USE, DESCRIBE, SHOW TABLES

👉 9.8 SELECT

👉 9.9 LIMIT, OFFSET

👉 9.10 ORDER BY

👉 9.11 DISTINCT

👉 9.12 WHERE, Comparison operators, NULL

👉 9.13 Logical Operators

👉 9.14 Aggregate Functions: COUNT, MIN, MAX, AVG, SUM

👉 9.15 GROUP BY

👉 9.16 HAVING

👉 9.17 Order of keywords.

👉 9.18 Join and Natural Join

👉 9.19 Inner, Left, Right and Outer joins.

👉 9.20 Sub Queries/Nested Queries/Inner Queries

👉 9.21 DML:INSERT

👉 9.22 DML:UPDATE , DELETE

👉 9.23 DDL:CREATE TABLE

👉 9.24 DDL:ALTER: ADD, MODIFY, DROP

👉 9.25 DDL:DROP TABLE, TRUNCATE, DELETE

👉 9.26 Data Control Language: GRANT, REVOKE

👉 9.27 Learning resources

👉 9.28 Assignment-22: SQL Assignment on IMDB data

#Module 10 : Plotting for exploratory data analysis (EDA)

Section 10 is divided into sub sections

👉 10.1 Introduction to IRIS dataset and 2D scatter plot

👉 10.2 3D scatter plot

👉 10.3 Pair plots

👉 10.4 Limitations of Pair Plots

👉 10.5 Histogram and Introduction to PDF(Probability Density Function)

👉 10.6 Univariate Analysis using PDF

👉 10.7 CDF(Cumulative Distribution Function)

👉 10.8 Mean, Variance and Standard Deviation

👉 10.9 Median

👉 10.10 Percentiles and Quantiles

👉 10.11 IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)

👉 10.12 Box-plot with Whiskers

👉 10.13 Violin Plots

👉 10.14 Summarizing Plots, Univariate, Bivariate and Multivariate analysis

👉 10.15 Multivariate Probability Density, Contour Plot

👉 10.16 Assignment-1: Data Visualization with Haberman Dataset

#Module 11 : Linear Algebra

Section 11 is divided into sub sections

👉 11.1 Why learn it ?

👉 11.2 Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector

👉 11.3 Dot Product and Angle between 2 Vectors

👉 11.4 Projection and Unit Vector

👉 11.5 Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane

👉 11.6 Distance of a point from a Plane/Hyperplane, Half-Spaces

👉 11.7 Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)

👉 11.8 Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)

👉 11.9 Square ,Rectangle

👉 11.10 Hyper Cube,Hyper Cuboid

👉 11.11 Revision Questions

#Module 12 : Probability and Statistics
Section 12 is divided into sub sections

👉 12.1 Introduction to Probability and Statistics

👉 12.2 Population and Sample

👉 12.3 Gaussian/Normal Distribution and its PDF(Probability Density Function)

👉 12.4 CDF(Cumulative Distribution function) of Gaussian/Normal distribution

👉 12.5 Symmetric distribution, Skewness and Kurtosis

👉 12.6 Standard normal variate (Z) and standardization

👉 12.7 Kernel density estimation

👉 12.8 Sampling distribution & Central Limit theorem

👉 12.9 Q-Q plot:How to test if a random variable is normally distributed or not?

👉 12.10 How distributions are used?

👉 12.11 Chebyshev’s inequality

👉 12.12 Discrete and Continuous Uniform distributions

👉 12.13 How to randomly sample data points (Uniform Distribution)

👉 12.14 Bernoulli and Binomial Distribution

👉 12.15 Log Normal Distribution

👉 12.16 Power law distribution

👉 12.17 Box cox transform

👉 12.18 Applications of non-gaussian distributions?

👉 12.19 Co-variance

👉 12.20 Pearson Correlation Coefficient

👉 12.21 Spearman Rank Correlation Coefficient

👉 12.22 Correlation vs Causation

👉 12.23 How to use correlations?

👉 12.24 Confidence interval (C.I) Introduction

👉 12.25 Computing confidence interval given the underlying distribution

👉 12.26 C.I for mean of a random variable

👉 12.27 Confidence interval using bootstrapping

👉 12.28 Hypothesis testing methodology, Null-hypothesis, p-value

👉12.29 Hypothesis Testing Intution with coin toss example

👉 12.30 Resampling and permutation test

👉 12.31 K-S Test for similarity of two distributions

👉 12.32 Code Snippet K-S Test

👉 12.33 Hypothesis testing: another example

👉 12.34 Resampling and Permutation test: another example

👉 12.35 How to use hypothesis testing?

👉 12.36 Proportional Sampling

👉 12.37 Revision Questions

#Module 13 : Interview Questions on Probability and statistics

👉 13.1 Questions

#Module 14 : Dimensionality reduction and Visualization
Section 14 is divided into sub sections

👉 14.1 What is Dimensionality reduction?

👉 14.2 Row Vector and Column Vector

👉 14.3 How to represent a data set?

👉 14.4 How to represent a dataset as a Matrix.

👉 14.5 Data Preprocessing: Feature Normalisation

👉 14.6 Mean of a data matrix

👉 14.7 Data Preprocessing: Column Standardization

👉 14.8 Co-variance of a Data Matrix

👉 14.9 MNIST dataset (784 dimensional)

👉 14.10 Code to Load MNIST Data Set