A.I. & Machine Learning

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About Course

Artificial Intelligence Course Syllabus

Introduction to Deep Learning & AI

Limitations of Machine Learning

What is Deep Learning?

Need for Data Scientists

Foundation of Data Science

What is Business Intelligence

What is Data Analysis

What is Data Mining

What is Machine Learning?

Analytics vs Data Science

Value ChainTypes of Analytics

Lifecycle Probability

Analytics Project Lifecycle

Advantage of Deep Learning over Machine learning

Reasons for Deep Learning

Real-Life use cases of Deep Learning

Review of Machine Learning

DataBasis of Data Categorization

Types of Data

Data Collection Types

Forms of Data & Sources

Data Quality & Changes

Data Quality Issues

Data Quality Story

What is Data Architecture

Components of Data Architecture

OLTP vs OLAP

How is Data Stored?

Big DataWhat is Big Data?

5 Vs of Big Data

Big Data Architecture

Big Data Technologies

Big Data Challenge

Big Data Requirements

Big Data Distributed Computing & Complexity

HadoopMap Reduce Framework

Hadoop Ecosystem

Data Science Deep Dive

What Data Science is

Why Data Scientists are in demand

What is a Data Product

The growing need for Data Science

Large Scale Analysis Cost vs Storage

Data Science Skills

Data Science Use Cases

Data Science Project Life Cycle & Stages

Data Acuqisition

Where to source data

TechniquesEvaluating input data

Data formats

Data Quantity

Data Quality

Resolution Techniques

Data Transformation

File format Conversions

Annonymization

PythonPython Overview

About Interpreted Languages

Advantages/Disadvantages of Python pydoc.

Starting Python

Interpreter PATH

Using the Interpreter

Running a Python Script

Using Variables

KeywordsBuilt-in Functions

StringsDifferent Literals

Math Operators and Expressions

Writing to the Screen

String Formatting

Command Line Parameters and Flow Control.

ListsTuplesIndexing and Slicing

Iterating through a Sequence

Functions for all Sequences

Operators and Keywords for Sequences

The xrange() function

List Comprehensions

Generator Expressions

Dictionaries and Sets.

Numpy & Pandas

Learning NumPy

Introduction to Pandas

Creating Data Frames

GroupingSorting

Plotting Data

Creating Functions

Slicing/Dicing Operations

Deep Dive – Functions & Classes & Oops

FunctionsFunction Parameters

Global Variables

Variable Scope and Returning Values. Sorting

Alternate Keys

Lambda Functions

Sorting Collections of Collections

Classes & OOPs

StatisticsWhat is Statistics

Descriptive Statistics

Central Tendency Measures

The Story of Average

Dispersion Measures

Data Distributions

Central Limit Theorem

What is Sampling

Why Sampling

Sampling Methods

Inferential Statistics

What is Hypothesis testing

Confidence Level

Degrees of freedom

what is pValue

Chi-Square test

What is ANOVA

Correlation vs Regression

Uses of Correlation & Regression

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What Will You Learn?

  • Become a Data Scientist and get hired
  • Master Machine Learning and use it on the job
  • Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
  • Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
  • Present Data Science projects to management and stakeholders
  • Learn which Machine Learning model to choose for each type of problem
  • Real life case studies and projects to understand how things are done in the real world
  • Learn best practices when it comes to Data Science Workflow
  • Implement Machine Learning algorithms
  • Learn how to program in Python using the latest Python 3
  • How to improve your Machine Learning Models
  • Learn to pre process data, clean data, and analyze large data.
  • Build a portfolio of work to have on your resume
  • Developer Environment setup for Data Science and Machine Learning
  • Supervised and Unsupervised Learning
  • Machine Learning on Time Series data
  • Explore large datasets using data visualization tools like Matplotlib and Seaborn
  • Explore large datasets and wrangle data using Pandas
  • Learn NumPy and how it is used in Machine Learning
  • A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
  • Learn to use the popular library Scikit-learn in your projects
  • Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
  • Learn to perform Classification and Regression modelling

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