Data Science Training

What exactly is data science, and what precisely does a data scientist do?

Various industry examples of data science and how Python is used for data science applications

Numerous aspects in the Data Science process, such as data exploration, data wrangling, and model selection

What is Machine Learning?

What is Deep Learning?

What is AI?

Data Analytics & it’s types

What is Python?

Why Python?

Installing Python

Python IDEs

Introduction to a fundamental Python construct

Understanding indentation such as tabs and spaces

Pound # character, names, variables and other code comments.

Python data types include containers,  numeric, text sequences  constants, and more

Python’s basic operators include logical, bitwise, assignment, comparison, and others, as well as slicing and the slice operator.

Break, if, for, continue, else, range(), and other loop and control statements.

Understanding OOP concepts such as encapsulation, inheritance, polymorphism, and abstraction

What is the difference between access modifiers, instances, class members, classes, and objects?

Function parameters and return types

Lambda expressions are used to connect to a database and get data.

Introduction to Python’s mathematical computing

What are arrays and matrices, array indexing, array math, and the ND-array object

Standard deviation, data types

NumPy conditional probability, correlation, and covariance SciPy for Scientific Computing

SciPy Fundamentals

NumPy on top of NumPy

What are the features of SciPy?

SciPy subpackages include Signal, Integrate, Fftpack, Cluster, Optimize, Stats, and more.

Using SciPy, prove the Bayes Theorem.

Introduction to Machine Learning with Python Tools for Machine Learning in Python include NumPy, ScikitLearn, Pandas, Matplotlib, and more.

Machine Learning Use Cases

Machine Learning Process Flow and Machine Learning Categories

Understanding Logistic Regression and Linear Regression

In Machine Learning, what is gradient descent?

Introduction to Python DataFrames, including importing data from JSON, CSV, Excel, SQL databases, and NumPy arrays into DataFrames. Various data operations such as selecting, filtering, sorting, displaying, joining, and combining, handling missing values, and time series analysis are covered in this data science training.

What is exploratory data analysis and building of hypothesis, plotting, and other techniques

What is a data object and what are its primary functions?

It involves the use Pandas library to manipulate data

Pandas library NumPy requirement, Pandas data loading and handling

Concatenation and several sorts of joins on data objects, as well as how to merge data objects

Exploration and analysis of datasets Matplotlib Data Visualization in data science training

Matplotlib is used for plotting graphs and charts such as Histogram, Scatter, Pie, Bar, Line, and others

It involves the use of Matplotlib API, Subplots, and Pandas built-in data visualization.

Need of Machine Learning

Introduction to Machine Learning

Types of Machine Learning, such as supervised, unsupervised and reinforcement learning

Why Machine Learning with Python and applications of Machine Learning.

What exactly supervised learning and classification are all about?

Decision Tree, an algorithm for inducing Decision Trees

Forest at Random

Matrix of Perplexity

Naive Bayes, how it works, and how to implement the Naive Bayes classifier

Support Vector Machine, Support Vector Mechanism, and Support Vector Mechanism Working Process

What Hyper Parameter Optimization (HPO) is all about?

Using Random Search vs. Grid Search

How to go about putting a Support Vector Machine to work for classification in data science training?

Introduction to supervised learning

Types of supervised learning – regression and classification

Introduction to regression

Simple linear regression

Multiple linear regression

Decision Tree, an algorithm for Decision Tree induction

Confusion Matrix

Random Forest

Naïve Bayes, working of Naïve Bayes, how to implement Naïve Bayes classifier

Support Vector Machine, working process of Support Vector Mechanism

What is Hyper Parameter Optimization

Comparing Random Search with Grid Search

How to implement a Support Vector Machine for classification?

Assumptions in linear regression, and math behind linear regression.

Hands-on Exercise – Linear Regression and Train-Test Implementation

Introduction to classification

Linear regression vs logistic regression

Math behind logistic regression with detailed formulas log it function and odds

Confusion matrix and accuracy

True positive rate v/s false positive rate

Threshold evaluation with ROCR.

Hands-on Exercise – Logistic regression, Confusion matrix Implementation

Introduction to tree-based classification

Understanding a decision tree

Impurity function and entropy to understand the concept of information gain for the right split of node

Gini index

Overfitting

Pruning, pre-pruning, post-pruning, cost-complexity pruning

Introduction to ensemble techniques

Understanding bagging

Introduction to random forests

Finding the right number of trees in a random forest.

Hands-on Exercise – Decision tree Implementation and hyper parameters in the random forest.

Introduction to probabilistic classifiers

Understanding Naïve Bayes

Math behind the Bayes theorem

Understanding a support vector machine (SVM)

Kernel functions in SVM, and math behind SVM.

Hands-on Exercise – Naïve Bayes and SVM implementation.

How a save a model using Pickle

Transfer a saved model

Deploy a saved model

Flask to deploy Machine Learning Model on Server

Unsupervised learning introduction, unsupervised learning usage scenarios

What is K-means clustering, and how does the K-means clustering method work?

Clustering at its best

What is the difference between hierarchical clustering and K-means clustering, and how does hierarchical clustering work in data science training?

Grid search,many parameters, model training, and pipeline construction

Types of unsupervised learning

Clustering and dimensionality reduction

Types of clustering

Introduction to k-means clustering

Math behind k-means

Dimensionality reduction with PCA.

Hands-on Exercise – K-Means and PCA implementation

Importance of Dimensions

Why Dimensionality Reduction

PCA and its implementation

LDA and its implementation

Factor Analysis

Scaling dimensional model

Hands On: – PCA

Hands On: – Scaling

Define Association Rules

Backend of recommendation engines and develop your own using python

What are Association Rules?

Association Rule Parameters

Calculating Association Rule Parameters

Recommendation Engines

How Recommendation Engines work?

Collaborative Filtering

Content Based Filtering

Hands On: – Apriori Algorithm

Hands On: – Market Basket Analysis

Introduction to Text Mining

Introduction to Sentiment

Setting up API Bridge, between Python and Twitter Account

Extracting Tweet from Twitter Account

Scoring the tweet

Introduction to Python web scraping and various web scraping libraries

Scrapy Python and Beautiful Soup packages

Beautiful Soup installation

lxml Python parser installation

Creating a soup object from HTML input

Tree searching, output printing,  full or partial parsing, and tree searching

Introduction to Natural Language Processing (NLP)

Introduction to text mining

Importance and applications of text mining

How NLP works with text mining

Writing and reading to word files

OS modules

Natural Language Toolkit (NLTK) environment and text mining: its cleaning, pre-processing and text classification.