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Classical Machine Learning with Python

The Machine Learning Fast Track is an online instructor program that will take you from novice-to-practitioner in applied machine learning. This program is laser-focused on teaching you practical, modern skills and tools that will take your career to the next level
10 - 12 Weeks
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Upto 50%  Discount on Early Admissions | EMI options Available | Decodr Edge (Learn Now, Pay Later)

What Will You Learn

Collecting data in large quantity and analyzing it
Working with stakeholders for recognizing opportunities throughout the organization to explore business solutions
Data mining with the utilization of methods of state of the art
Advancing the procedure of data collection to include the facts which are relevant for developing analytic systems

Learn First and Pay Later

First Time in India. Just invest time and pay after you get a job
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About This Program

The Machine Learning Fast Track is an online instructor program that will take you from novice-to-practitioner in applied machine learning. This program is laser-focused on teaching you practical, modern skills and tools that will take your career to the next level. The Program will prepare you for the role of a Machine Learning Specialist.

How This Program Works

Learn the latest skills in market

Our corporate partners are deeply involved in curriculum design ensuring that it meets the current industry requirements for data science professionals. Learn through real-life industry projects sponsored by top companies across industries

Network with your future employers

Learn LinkedIn Personal Branding & Image building to showcase your skills and talent. Build Network with industry leaders to build relationships and get opportunities. A portfolio is the #1 way to prove your skills and win the trust of employers… And as you complete those projects, you’ll be building your portfolio too.

Crack the interview

With Decodr’s Corporate Network and Career Services you Land your First job in Data Science.

Syllabus

Syllabus

Module
1
:
Introduction to Data Science
Learning Outcome:

In the First Module you will be introduced to the basic concepts of Data Science such as Definition and application of data, the roles of a data scientist, sources and types of data, Data storage and retrieval methods, Data Pipelines, Data Preparation, Exploratory Data Analysis and Interactive Dashboards

Module
2
:
Introduction to Python
Learning Outcome:

In this part, you’ll learn to install Python and Jupyter notebook, data types and variables, and use conditionals and loops to control the flow of your programs. You’ll harness the power of complex data structures like lists, sets, dictionaries, and tuples to store collections of related data. You’ll define and document your own custom functions, write scripts, and handle errors.

Module
3
:
NumPy
Learning Outcome:

In this module, you will learn about a commonly used data structure in Python for scientific data: NumPy arrays. You will write Python code to import data as numpy arrays and to run calculations and summarize data in NumPy arrays.

Module
4
:
Matplotlib
Learning Outcome:

The objective of this module is to get you familiar with the basic plotting functions of the library. It contains several examples which will give you hands-on experience in generating plots in python.

Module
5
:
Dictionaries
Learning Outcome:

In this module, you will learn about the basic characteristics of Python dictionaries and learn how to access and manage dictionary data. Once you have finished this module, you should have a good sense of when a dictionary is the appropriate data type to use, and how to do so.

Module
6
:
Pandas Overview
Learning Outcome:

In this module, you’ll get started with Pandas and get to know the ins and outs of how you can use it to analyse data with Python.

Module
7
:
Logical Operators
Learning Outcome:

In this module, you’ll see how calculations can be performed on objects in Python. By the end of this tutorial, you will be able to create complex expressions by combining objects and operators.

Module
8
:
Control Flow
Learning Outcome:

In this module, you’ll learn about python control flow structure (or python control flow) which is a programming block that analyses variables and selects a direction to go in based on specified parameters.

Module
9
:
Filtering Pandas Dataframes
Learning Outcome:

After completing this module, you will be able to explain indexing for pandas dataframe and use indexing and filtering to select data from pandas dataframe.

Module
10
:
Loops
Learning Outcome:

When you’re working with data in Python, loops can be a powerful tool. But they can also be a little bit confusing when you’re just starting out. In this module, we’re going to dive headfirst into loops and learn how they can be used to do all sorts of interesting things when you’re doing data cleaning or data analysis in Python.

Module
11
:
Descriptive Statistics Using Python
Learning Outcome:

A necessary aspect of working with data is the ability to describe, summarize, and represent data visually. Python statistics libraries are comprehensive, popular, and widely used tools that will assist you in working with data. In this module, you’ll learn What numerical quantities you can use to describe and summarize your datasets How to calculate descriptive statistics in pure Python How to get descriptive statistics with available Python libraries How to visualize your datasets

Module
12
:
Inferential Statistics Using Python‍
Learning Outcome:

In this module, we will explore basic principles behind using data for estimation and for assessing theories. We will analyse both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately.

Module
13
:
Pandas
Learning Outcome:

The pandas package is the most important tool at the disposal of Data Scientists and Analysts working in Python today. The powerful machine learning and glamorous visualization tools may get all the attention, but pandas is the backbone of most data projects. Learn some of the most important pandas features for exploring, cleaning, transforming, visualizing, and learning from data.

Module
14
:
Merging Dataframes Using Pandas
Learning Outcome:

This module is all about the act of combining—or merging—DataFrames, an essential part of any data scientist's toolbox. You'll hone your pandas skills by learning how to organize, reshape, and aggregate multiple datasets to answer your specific questions.

Module
15
:
Introduction to Data Visualisation Using Matplotlib
Learning Outcome:

In this module you’ll be learning: Explain what data visualization is and its importance in our world today Understand why Python is considered one of the best data visualization tools Describe matplotlib and its data visualization features in Python List the types of plots and the steps involved in creating these plot

Module
16
:
Data Visualization with Seaborn
Learning Outcome:

In this module you’ll learn about Seaborn which is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Module
17
:
Introduction to Importing Data in Python
Learning Outcome:

In this module you will get an introduction to flat files, importing data from other file types using Python and also learn to work with Relational Databases in Python.

Module
18
:
Data Cleaning in Python
Learning Outcome:

In this module, we’ll leverage Python’s libraries to deal with common data problems and to make them ready for analysis.

Module
19
:
Exploratory Data Analysis in Python
Learning Outcome:

In this module you’ll learn about EDA which is a phenomenon under data analysis used for gaining a better understanding of data aspects like:

– main features of data

– variables and relationships that hold between them

– identifying which variables are important for our problem

Module
20
:
Machine Learning for Everyone
Learning Outcome:

In this module we look at machine learning (ML), what it is and how it works. We take a look at a couple supervised learning algorithms and 1 unsupervised learning algorithm. No coding is required in this module. You will also get an introduction of deeplearning.

Module
21
:
Supervised Learning with scikit-learn
Learning Outcome:

Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib.

Module
22
:
Unsupervised Learning in Python
Learning Outcome:

By the end of the course, you’ll apply clustering and dimensionality reduction in Machine Learning using Python as well as Master Unsupervised Learning to solve real-world problems!

Module
23
:
Machine Learning with Tree-Based Models in Python
Learning Outcome:

In this module, you'll learn how to use Python to train decision trees and tree-based models. You'll understand the advantages and shortcomings of trees and demonstrate how ensembling can alleviate these shortcomings, all while practicing on real-world datasets. Finally, you'll also understand how to tune the most influential hyperparameters in order to get the most out of your models.

Module
24
:
Cluster Analysis in Python
Learning Outcome:

After completing the module, you will be able to quickly apply various clustering algorithms on data, visualize the clusters formed and analyse results.

Module
25
:
Extreme Gradient Boosting with XGBoost
Learning Outcome:

Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting, with models using XGBoost regularly winning online data science competitions and being used at scale across different industries. In this module, you'll learn how to use this powerful library to build and tune supervised learning models.

Module
26
:
Dimensionality Reduction in Python
Learning Outcome:

In this module we will study What is Dimensionality Reduction. Also, will cover every related aspect of Dimensionality Reduction like components & Methods of Dimensionality Reduction, Principle Component analysis & Importance of Dimensionality Reduction, Feature selection & Extraction.

Module
27
:
Pre-processing for Machine Learning in Python
Learning Outcome:

This module covers the basics of how and when to perform data pre-processing. You'll learn how to standardize your data so that it's in the right form for your model, create new features to best leverage the information in your dataset, and select the best features to improve your model fit.

Module
28
:
Machine Learning for Time Series Data in Python
Learning Outcome:

The Time Series Forecasting course provides students with the foundational knowledge to build and apply time series forecasting models in a variety of business contexts. You will learn:

The key components of time series data and forecasting models

How to use ETS (Error, Trend, Seasonality) models to make forecasts

Module
29
:
Feature Engineering for Machine Learning in Python
Learning Outcome:

In this module, you will learn to create new features to improve the performance of your Machine Learning models.

Module
30
:
Model Validation in Python
Learning Outcome:

In this module, we will cover the basics of model validation, discuss various validation techniques, and begin to develop tools for creating validated and high performing models.

Module
31
:
Hyperparameter Tuning in Python
Learning Outcome:

In this module, Learn the difference between Hyperparameters and Parameters, how a model's hyperparameters affect the model's performance, how to use grid search, random search and informed search to try different hyperparameter values.

Download Full Curriculum

Program Schedule

Week 1

Program Structure

Platform Discussions

Python Installation

Linkedin Branding & Networking Introduction

Week 2

Introduction to Data Science

Introduction to Python

Profile Optimisation & Basic Hygiene

Week 3

Case Study : Bike Sharing

Supervised Learning with scikit-learn - Logistics Regression

Case Study : Telecom Churn Case Study

Resume Building

Week 4

Numpy

Matplotlib

Dictionaries

Pandas

Overview

Logical Operators

Control Flow

Filtering Pandas

Dataframes

Loops

Doubt Clarification Session 1

Creating Content & building credibility

Week 5

Pandas

Introduction to Importing Data in Python

Introduction to Data Visualisation Using Matplotlib

Growing the Network of TG

Week 6

Pandas Contd

Introduction to Importing Data in Python

Introduction to Data Visualisation Using Matplotlib

Data Visualization with Seaborn

Doubt Clarification Session 2

Asking for work/job, reading the stats and tracking your growth

Week 7

Case Study : Movie Reviews - Pandas

Practice Questions

Linkedin doubt clearing session

Creating a GitHub account

Creating a Repository - Assignment

Creating and Uploading Projects

Week 8

Descriptive Statistics Using Python

Inferential Statistics Using Python

Week 9

Inferential Statistics Using Python

Feature Engineering for Machine Learning

Week 10

Feature Engineering for Machine Learning contd.

Exploratory Data Analysis

Case Study : Credit EDA

Doubt Clarification Session 4

Week 11

Case Study : Statistics and Hypothesis

Practice Questions

Hackathon 1

Week 12

Introduction to Machine Learning

Supervised Learning with scikit-learn - K - Nearest Neighbors

Supervised Learning with scikit-learn - KNN Contd.

Supervised Learning with scikit-learn - Linear Regression

Supervised Learning with scikit-learn - Regularised Regression

Doubt Clarification Session 5

Week 13

Case Study : Bike Sharing

Case Study : US based Housing Company

Week 14

Practice Questions

Supervised Learning with scikit-learn - Logistics Regression

Case Study : Telecom Churn Case Study

Doubt Clarification Session 6

Week 15

Case Study : Lead Scoring

Unsupervised Learning - KMeans

Unsupervised Learning - Hierarchical Clustering

Week 16

Dimensionality Reduction - PCA

Case Study - Clustering of Countries

Practice Questions

Doubt Clarification Session 7

Week 17

Machine Learning for Time Series Data

Week 18

Case Study - Time Series

Case Study - GDP Analysis

Doubt Clarification Session 8

GitHub Review

Week 19

Practice Questions

Resume Building Session

Capstone Project Initiated

Hackathon 2

Week 20

Interview Preparation Live Session

Written Interview Test 1

Doubt Clarification Session 9

Week 21

Mock Interviews - Group Practice 1

Mock Interviews - Group Practice 2

Week 22

Written Interview Test 2

Practice interview with outside experts - 1

Doubt Clarification Session 10

Week 23

Practice interview with outside experts - 2

Interview Drives for Selected Candidates

Mentors

Shreyas Raghavan
Data Scientist, Dun & Bradstreet

Data Scientist | Machine Learning Engineer Part of team developing D&B Flagship projects on Risk Management. Whilst Working on projects involving D&B internal projects and their clients (Microsoft , T-Mobile , AT&T ).

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Certification

Certificate of Completion

You can share your Course Certificates in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

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