Machine Learning with Python

Lecture 2.1

Descriptive Statistics: Population vs samples

Lecture 2.2

Descriptive Statistics : Measures of Central Tendency

Lecture 2.3

Descriptive Statistics : Measures of Variability

Lecture 2.4

Descriptive Statistics : Detection of Outliers

Lecture 2.5

Probability : Probability Laws

Lecture 2.6

Probability : Probability Distribution

Lecture 2.7

Probability : Bayesian Theorem

Lecture 2.8

Probability : Central Limit Theorem

Lecture 2.9

Probability : Confidence Interval

Lecture 2.10

Inferential Statistics : ANOVA

Lecture 2.11

Inferential Statistics : Pearson Correlation Coefficient

Lecture 2.12

Inferential Statistics : Spearman Correlation Coefficient

Lecture 2.13

Inferential Statistics : Regression Analysis

Lecture 2.14

Inferential Statistics : Hypothesis Testing

Lecture 2.15

Linear Algebra : Matrix Operations, Inverse and Decomposition

Lecture 2.16

Linear Algebra : Vectors

Lecture 3.1

Basic Syntax

Lecture 3.2

Data Types

Lecture 3.3

Operators

Lecture 3.4

Control flow statements

Lecture 3.5

Decisions

Lecture 3.6

Loops

Lecture 3.7

Functions

Lecture 4.1

Classes

Lecture 4.2

Objects

Lecture 4.3

Data members

Lecture 4.4

Overloading

Lecture 4.5

Inheritance

Lecture 5.1

List and tuples

Lecture 5.2

Sets

Lecture 5.3

Dictionaries

Lecture 5.4

Strings

Lecture 6.1

Reading from Files

Lecture 6.2

Writing into files

Lecture 6.3

Database connections

Lecture 6.4

Dealing with files using pandas.

Lecture 7.1

Numpy

Lecture 7.2

Matplotlib

Lecture 7.3

Plotly

Lecture 7.4

Seaborn

Lecture 7.5

Pandas

Lecture 7.6

Sklearn

Lecture 8.1

Machine Learning & Data Science Techniques :Machine Learning vs. Deep Learning vs. Data Science

Lecture 8.2

Machine Learning & Data Science Overview : Supervised Learning vs. Unsupervised Learning vs. Reinforcement Learning

Lecture 8.3

Supervised Learning :Multiple Linear Regression in Python

Lecture 8.4

Supervised Learning :Simple Linear Regression in Python

Lecture 8.5

Supervised Learning :Multiple Linear Regression in Python

Lecture 8.6

Supervised Learning :Logistic Regression in Python

Lecture 8.7

Supervised Learning : Nearest neighbor

Lecture 8.8

Unsupervised Learning : Kmeans clustering

Lecture 9.1

Big data

Lecture 9.2

NLP

Lecture 9.3

Cloud computing

Lecture 9.4

Deep Learning

Lecture 9.5

Neural Network Architecture and How it works

Lecture 9.6

Tensor Flow & keras

Lecture 9.7

Recommender System

Lecture 10.1

Statistics refreshment

Lecture 10.2

Linear Algebra refreshment

Lecture 11.1

Linear Regression Review

Lecture 11.2

Logistics Regression Review

Lecture 11.3

SVM Review

Lecture 11.4

K-Nearest Neighbor (K-NN) classification

Lecture 11.5

Decision Tree Classification

Lecture 11.6

Naive Bayes

Lecture 12.1

K Means clustering : K-Means Random Initialization Trap

Lecture 12.2

K Means clustering : K-Means Selecting The Number Of Clusters

Lecture 12.3

Hierarchical clustering : Hierarchical Clustering How Dendrograms Work

Lecture 12.4

Hierarchical clustering : Hierarchical Clustering Using Dendrograms

Lecture 12.5

Principal Component Analysis (PCA)

Lecture 12.6

DB scan

Lecture 13.1

Upper Confidence Bound

Lecture 13.2

Thompson Sampling

Lecture 14.1

Neural Network Neural : Network Architecture and How it Works

Lecture 14.2

Neural Network : Artificial Neural Network

Lecture 14.3

Neural Network : Convolutional Neural Networks

Lecture 14.4

Neural Network : Recurrent neural network

Lecture 14.5

Natural Language Processing (NLP)

Lecture 14.6

Tensor Flow

Lecture 14.7

Recommender System