Professional Certificate in Machine Learning

Course Curriculum

Chapter 1: Setting up the Environment for Python Machine Learning

Lecture 1: Python For machine Learning : Setting up the Environment : Anaconda

Lecture 2: Downloading and Setting up Python and PyCharm IDE

Chapter 2: Python Basics For Machine Learning

Lecture 1: Python For Absolute Beginners – Variables – Part 1

Lecture 2: Python For Absolute Beginners – Variables – Part 2

Lecture 3: Python For Absolute Beginners – Variables – Part 3

Lecture 4: Python For Absolute Beginners – Lists

Lecture 5: Python For Absolute Beginners – Lists Part 2

Lecture 6: Python For Absolute Beginners – Lists Part 3

Lecture 7: Software Design – Problem Solving

Lecture 8: Software Design – Flowcharts – Sequence

Lecture 9: Software Design – Repetition

Lecture 10: Flowcharts Questions and Answers # Problem Solving

Chapter 3: Understanding Data With Statistics & Data Pre-processing

Lecture 1: Understanding Data with Statistics: Reading data from file

Lecture 2: Understanding Data with Statistics: Checking dimensions of Data

Lecture 3: Understanding Data with Statistics: Statistical Summary of Data

Lecture 4: Understanding Data with Statistics Correlation between attributes

Lecture 5: Data Pre-processing – Scaling with a demonstration in python

Lecture 6: Data Pre-processing – Normalization , Binarization , Standardization in Python

Lecture 7: feature Selection Techniques : Univariate Selection

Chapter 4: Data Visualization with Python

Lecture 1: Data preparation and Bar Chart

Lecture 2: Data Visualization with Python Histogram , Pie Chart, etc..

Chapter 5: Artificial Neural Networks [ Comprehensive Sessions]

Lecture 1: Introduction to Artificial Neural Networks

Lecture 2: Creating the First ANN from Scratch with Python

Lecture 3: Multiple Input Neuron

Lecture 4: Creating a simple layer of neurons, with 4 inputs. # Python # From scratch

Lecture 5: ANN – Illustrative Example

Lecture 6: KERAS Tutorial – Developing an Artificial Neural Network in Python -Step by Step

Lecture 7: Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]

Chapter 6: Naive Bayes Classifier with Python [Lecture & Demo]

Lecture 1: Lecture & Demo: Naive bayes classifier

Chapter 7: Natural Language Processing for Data Scientists

Lecture 1: Introduction to Natural Language Processing [Theory-Guest Lecture by Professor]

Lecture 2: Setting up the Environment for NLP – ACH

Lecture 3: Introduction to Tokenization

Lecture 4: Downloading and Setting up NLTK

Lecture 5: Tokenization Tutorial

Lecture 6: Introduction to Normalization

Lecture 7: Normalization Tutorial

Lecture 8: Introduction to Part of Speech Tagging

Lecture 9: Part of Speech Tagging Tutorial

Lecture 10: Introduction to Stopwords

Lecture 11: Named Entity Recognition Lecture

Lecture 12: Named Entity Recognition Tutorial

Lecture 13: Classification Lecture

Lecture 14: Classification Tutorial Part 1: Preprocessing movie reviews

Lecture 15: Classification Tutorial Part 2: Feature Sets

Lecture 16: Classification Tutorial Part 3: Naive Bayes

Lecture 17: Classification Homework Exercise

Lecture 18: Real World Applications of NLP [Complete Project] – Introduction

Lecture 19: Twitter Application Descriptions

Lecture 20: Creating a Twitter Application

Lecture 21: Getting the Test Set

Lecture 22: Preparing the Training Set

Lecture 23: Preprocessing

Lecture 24: Classification

Lecture 25: Testing the Model

Lecture 26: Python For Beginners : Variables : Part 1

Lecture 27: Python For Beginners : Variables : Part 2

Lecture 28: Python For Beginners : Variables : Part 3

Lecture 29: Python For Beginners – Lists

Lecture 30: Python For Beginners – Lists Part 2

Lecture 31: Python For Beginners – Lists Part 3

Chapter 8: Linear regression

Lecture 1: Linear regression

Lecture 2: Univariate Linear Regression Demo [Hands-on] Part 1- Linear Regression

Lecture 3: Univariate Linear Regression Demo [Hands-on] Part 2- Linear Regression

Lecture 4: Multivariate Linear Regression Demo [Hands-on] Linear Regression

Chapter 9: Logistic regression

Lecture 1: Logistic regression

Chapter 10: Introduction to clustering [K – Means Clustering ]

Lecture 1: What is clustering in Machine Learning

Lecture 2: K – Means Clustering

Lecture 3: [hands-on] K – Means clustering with python step by step implementation

Lecture 4: K – Means Clustering [Source code – Complete Project]

Lecture 5: K-Means clustering – Code walkthrough with Theory & Practical

Chapter 11: Extra Reading

Lecture 1: Neural Network Optimization

Lecture 2: Popular resources from Top Universities of the world

Lecture 3: Machine Learning – Source codes

Chapter 12: Java programming for Data Scientists

Lecture 1: Major Java Features

Lecture 2: JDK,JRE ,JVM, Platform & Classloader

Lecture 3: Entering the Object oriented programming world – Classes & Objects

Lecture 4: Classes & Objects

Lecture 5: Creating Objects from Classes

Lecture 6: Constructors

Lecture 7: Methods (parameter vs arguement)

Lecture 8: Method Overloading

Lecture 9: Method Overloading Demo

Lecture 10: Data Abstraction

Lecture 11: Encapsulation

Lecture 12: Inheritance

Lecture 13: Inheritance Demo

Lecture 14: Inheritance – instanceof Demo

Lecture 15: Static