Machine Learning
About this course:
+ 35 Hours
20 Hours Live classes + 15 Hours Project work
Industry experts
Taught by expert industry professionals.
Basics to Advanced
No programming experience? No worries, we start from the basics!
career mentoring
Placement Assistance, Interview Preparation and more
Who is this course for?

Analytics professionals who want to fasten their growth path

IT and Software professionals who are looking to get into the field of Analytics.

Students and graduates who want to start their career with Analytics, or

Anyone who wants to get started with Analytics
Prerequisites:
No prior knowledge of programming is assumed.
No prior knowledge of any subject is assumed.
Course Contents:
Section 1: Data Science Overview

Data Science

Data Analytics & Business Analytics

Data Scientists / Data Analysts / Business Analysts What they do?

Examples of Data Science

Python for Data Science
Section 2: Data Analytics Overview

Introduction to Data Visualization

Processes in Data Science

Data Wrangling, Data Exploration, and Model Selection

Exploratory Data Analysis or EDA

Data Visualization

Plotting

Hypothesis Building and Testing
Section 3: Statistical Analysis and Business Applications

Introduction to Statistics

Statistical and NonStatistical Analysis

Some Common Terms Used in Statistics

Data Distribution: Central Tendency, Percentiles, Dispersion

Histogram

Bell Curve

Hypothesis Testing

ChiSquare Test

Correlation Matrix

Inferential Statistics
Section 4: Python: Environment Setup and Essentials

Introduction to Anaconda

Installation of Anaconda Python Distribution  For Windows, Mac OS, and Linux

Jupyter Notebook Installation

Jupyter Notebook Introduction

Variable Assignment

Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting

Creating, accessing, and slicing tuples

Creating, accessing, and slicing lists

Creating, viewing, accessing, and modifying dicts

Creating and using operations on sets

Basic Operators: 'in', '+', '*'

Functions

Control Flow
Section 5: Mathematical Computing with Python (NumPy)

NumPy Overview

Properties, Purpose, and Types of ndarray

Class and Attributes of ndarray Object

Basic Operations: Concept and Examples

Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays

Copy and Views

Universal Functions (ufunc)

Shape Manipulation

Broadcasting

Linear Algebra
Section 6: Data Manipulation with Python (Pandas)

Introduction to Pandas

Data Structures

Series

DataFrame

Missing Values

Data Operations

Data Standardization

Pandas File Read and Write Support

SQL Operation
Section 7: Scientific computing with Python (Scipy)

SciPy and its Characteristics

SciPy subpackages

SciPy subpackages –Integration

SciPy subpackages – Optimize

Linear Algebra

SciPy subpackages – Statistics

SciPy subpackages – Weave

SciPy subpackages  I O
Section 8: Machine Learning with Python – Part 1

Introduction to Machine Learning

Machine Learning Approach

How Supervised and Unsupervised Learning Models Work

ScikitLearn

Supervised Learning Models  Linear Regression

Supervised Learning Models: Logistic Regression

K Nearest Neighbors (KNN) Model

Unsupervised Learning Models: Clustering
Section 9: Regression
Learning objectives: This lesson will take you into your past and help you brush up on those math and statistics concepts highly necessary to understand the Machine Learning algorithms.

Regression and its types

Linear regression: Equations and algorithms
Section 10: Classification
Learning objectives: In this lesson, you will learn about classification, logistic regression, Knearest neighbors, support vector machines, and Naive Bayes.

Meaning and types of classification

Logistic regression

Knearest neighbors

Support vector machines

Kernel support vector machines

Naive Bayes

Decision tree classifier

Random forest classifier
Section 11: Unsupervised learning: Clustering
Learning objectives: In this lesson, you will learn and implement a few more algorithms within the unsupervised learning category.

Clustering algorithms

Kmeans clustering
Section 12: Introduction to Deep Learning
Learning objectives: This last lesson of the course, gives you a peek into the world of deep learning and how it is related to machine learning.

Meaning and importance of Deep Learning

Artificial Neural Networks

TensorFlow
Section 13: Capstone Project 1: Predicting Customer Churn
Churn prediction is essential for businesses as it helps you detect customers who are likely to cancel a subscription, product or service. You will use variety of tools learned in the class to predict the customers who are likely to churn in future.
Section 14: Capstone Project 2: Text Analytics
Text Data from Social Media platforms could be used to analyze various things. It’s one such use is sentiment analysis. You will extract the data from twitter to work on the sentiment analysis.
How we help you get into Data Science Job?

Resume Preparation: We help you customize your resumes for various jobs.

Mock interviews (Technical + HR Round)

Coops & Placement Assistance
Expected Salary for Data Scientists in GTA region:

Contractbased: $45 to $95 per hour incorporated (Based on experience)

Full Time: $80k to $120k yearly (Based on experience)
Contact:
+1 6478061429