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Data Science & Machine Learning

Topics:

1. Basics of Data Science

i. What is data science

ii. AI vs DS vs Machine learning

iii. Fields of data science

iv. Applications of Data Science

v. Big Data

a. Definition of Big data

b. Applications of Big data

c. Hadoop and Spark

i. Hadoop

1. Map reduce

2. HDFS

ii. Spark

d. Tools and language

vi. Natural Language Processing

a. Definition of NLP

b. Application of NLP

c. Tools and Language

vii. Machine learning

a. Definition of Machine learning

b. Types of Machine Learning

c. Applications of Machine learning

d. Tools and Languages

viii. NoSQL Data bases

a. Definition

b. SQL vs NoSQL Databases

c. NoSQL databases tools

d. Search Engine technologies

2. Python Basics

a. Installation

i. Anaconda

ii. Environment creation

iii. Pycharm

b. Interpreter

c. Data types in Python

d. String data types

e. List

f. Dictionary

g. Tuple

h. Set

i. Functions

j. Classes

k. OOPS

i. Inheritance

ii. Encapsulation

iii. Abstraction

l. Exceptional handling

3. Numpy, Pandas

a. Numpy Tutorial

b. Pandas Tutorial

4. Natural Language Processing

a. Basics of NLP

b. Applications of NLP

c. Tokenization

d. Stopwords

e. Stemming and lemmatization

f. Part of Speech tagging

g. Named entity recognition

h. Custom NER system using OpenNLP (java)

i. Phrase Handling Application

j. Sentiment Analysis Application

i. Feature Extraction process

1. True/False model

2. Count Vectorizer

3. TF-IDF Vectorizer

ii. Creating Model using NLTK Naïve Bayes algorithm

k. Recommendation System Application

5. Web Crawling

a. Scrapy Introduction

b. Xpath Introduction

c. Crawling Application

6. Machine learning

a. Basics of Machine Learning

b. Types of Machine Learning Algorithms

i. Supervised

1. Classification

a. Logistic Regression

b. K Nearest Neighbors

c. SVM

d. Decision Tree

e. Random Forest

f. Gradient Boosting

g. Naïve Bayes

2. Regression

a. Linear Regression

b. Polynomial Regression

c. SVR

d. Decision Tree Regressor

e. Random Forest Regressor

f.

ii. Unsupervised

1. Clustering

a. K Means Clustering

b. Hierarchical Clustering

7. Machine Learning Model Evaluation

a. Backward elimination Process

b. P value

c. R Squared

Course Id:
PYT007 
Course Fees:
301 USD