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Course Introduction

Courses Features

10 x 2 Hours LIVE Instructor-led Sessions

7 Modules

All Sessions Recorded Videos available

4 Projects

Quizzes & Assessment

Additional Resources

Certificate

Course Overview

This course spans 2 weeks, 10 LIVE online sessions of 2 hours each, totaling 20 hours.

In this course, you will learn the basics of Python Language, Exploratory Data Analysis, Inferential Statistics for Data Scientists, Machine Learning Techniques such as Linear Regression and Logistic Regression for solving Regression and Classification problems.

This course is part of the Teksands High Impact Series and is designed specifically for the busy professionals who would want to develop the maximum understanding on the topics in the shortest time possible. This course uses a completely practical based approach to run through as much as projects/code/demo as possible and explain both the concepts and coding/solutions parts on the go with the demo. The learners are then given additional projects as practice assignments for them to solve them on their own and solidify their understandings.

Course Overview

Detailed Description Of the Courses

This LIVE course, Machine Learning Mastery will provide 20 hours of intense LIVE Training to the Learners.

    1. Basics of Python Language: Helps learners to understand the Language Elements of Python and data structures including Pandas and Numpy Libraries. This will enable you to code Machine Learning solutions covered in subsequent chapters.
    2. Exploratory Data Analysis: Learn to make meaning of the data in context of the problem statement and solution. Run a gamut of tools on your data to identify patterns and anomalies, take corrective action to cleanse them before feeding your data to the algorithms.
    3. Professional Visualisation: Learn the art of creating professional Visualisation for advanced technical analysis of your Input Data. Master Visualisations using two strong libraries - Matplotlib and Seaborn.
    4. Statistics: Learn Descriptive and Inferential Statistics and their application to Analytics and Machine Learning. Learn to understand data distributions and patterns, rules, inferences and treatments of various distributions. Learn to apply advanced statistical concepts like Hypothesis testing on your data to make complex decisions on a population of data by testing samples.
    5. Linear Regression Algorithm: Learn to apply Linear Regression Algorithm techniques on prediction problems walking through a real-world project.
    6. Logistic Regression Algorithm: Learn to apply the Logistic Regression Algorithm to understand the foundations of Classification techniques.
The course is completely based on practical approaches of teaching. Learners will have intense exposure to real code and data while learning the concepts on the go. We will also provide you all the codes used in training and also additional problems for you to work on and practice.

What will you learn?

Python Language

Machine Learning Foundations

Exploratory Data Analysis

Linear Regression with Projects

Inferential Statistics

Developing Visualization

Logistic Regression with Projects

The course includes a detailed insight into the how Data is Analysed, Prepared and Presented for Data Science challenges and also incorporates two Machine Learning Algorithms to solve Prediction and Classification problems using Python in a total of 20 hours to give the maximum value to our learners out of their busy schedule.

Lesson Content
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Python Language elements

Python Data Structures

Working with Numpy and Pandas Libraries

Lesson Content
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Data Collection and Data Organisation

Understanding Features and Data

Analysis of Patterns

Finding Data Issues

Cleansing Data

Lesson Content
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Understanding Types of Visualisations

Creating Visualisations using Matplotlib Library

Advanced Visualisations using Seaborn

Lesson Content
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Introduction to Statistics

Descriptive Statistics

Normal Distribution

Central Limit Theorem

Understanding Probability

Hypothesis Testing

Lesson Content
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Real Life Use Cases

Types of Learning Algorithms

Measuring Model Accuracy

Using Hyperparameters to Optimise Model Performance

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Real Life Use Cases of Linear Regression

Understanding Linear Regression Concepts

Walk through Complete Real Life Industry Project

Understanding Classification Techniques more broadly Copy

Measuring Model Accuracy

Real Life Industry Assignment

Lesson Content
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Real Life Use Cases of Logistic Regression

Understanding Logistic Regression Concepts

Walk through Complete Real Life Industry Project

Measuring Model Accuracy

Real Life Industry Assignment

Real Life Projects

Real Life Projects

House Price Prediction

Example

Car Price or House Price prediction given historical transaction data. You will learn how the Linear Regression algorithm learns patterns and helps predict new Car or House price based on parameters given.
Real Life Projects

Customer Churn Analysis

Example

Predict which customers are likely to leave the current provider based on their behavioural data from past. We will look at a Telecom or Insurance industry case study.
Real Life Projects

Exploratory Data Analysis

Example

you will learn how to perform Univariate and Bivariate Analysis of data from a Micro-Credit organisation to determine what attributes from their credit transactions are influencing the probability of Default by borrowers.

Upcoming Batches

Start Date Schedule Timings
18th Oct Mon-Fri (10 weekdays) 6 - 8 pm Register Request Brochure/ Register for Demo Class
15th Nov Mon-Fri (10 weekdays) 6 - 8 pm Request Brochure/ Register for Demo Class

Course Fee: INR 3850 (INR 8200)


FAQs

Data Science and Predictive Analytics has served a multitude of functions and job needs and a lot of Job Roles are created in organizations in the last few years. Some of the prominent Job Roles in this space are listed below: Data Scientist: Data Scientists would have the responsibility of understanding and analysing all the data the organisation has and create Data Driven products and solutions to create businesses processes more efficient, drive automation, create decision systems, future prediction systems, etc. Data Architect: Data Architects would typically analyse the organisational Data Schemas, design new schemas for newer data driven systems , tune existing data schemas, optimise organisational Mete Data and all data repositories including ETL Systems. Data and Analytics Manager: Responsible for managing and leading Data initiatives in the organisation, including leadership in ETL programs, Decision Systems programs, leading analytics teams, etc. Data Analyst:Data Analysts typically gather and analyse data within divisions and organisation for the purpose of building Insights and Analytics solutions and systems using a range of tools, techniques including statistics. This role is highly important for the leadership of any organisation to develop understanding of business trends. Machine Learning Engineer: Responsible for developing sophisticated Machine Learning Models that are to create various Decision, Prediction, Classification, Clustering systems on Business Data. All the roles above and the plethora of roles this space is offering are growing rapidly in demand and skills shortfall is even expanding leading to high salaries for every skilled personnel in these fields. Given "Data is the new Fuel", demand for professionals in these fields in the many years to come will continue to expand unabated creating massive opportunities for data professionals.
With Data Science applications booming through businesses leading to saving costs, better profitability and driving newer business models and products, the demand for these skills have skyrocketed. Literally, every business today is after quality skilled professionals in Data Science and Analytics. Not only they are looking for Data Science and Predictive Analytics skills to create new solutions, but preferring these as must-have skills in all other fields to drive continuous automation and efficiency. Even Business and Operations personnel are today are equipping themselves with foundational knowledge in these areas to save costs through automation. Some statistics:
  • 70-80% Year on Year New Job Numbers Growth in Data Science and related skills
  • 15-20% Year on Year Average Salary Growth in these fields
  • 85% of the Companies are Investing and expanding their Data Science Teams rapidly
  • In 2020-21, there is a net shortage of 250,000+ skilled resources in these fields
  • 2 Years is approximate Data Science Staff Tenure in companies
The course is completely based on practical approaches of teaching. Learners will have intense exposure to real code and data while learning the concepts on the go. We will also provide you all the codes used in training and also additional problems for you to work on and practice. The Delivery method is Online, Live Classes led by Professional, Industry Experienced Instructors.
20 Hours Weekday Courses: Over 2 Weeks, all Weekdays (Monday to Friday), 2 hour Sessions per day. Weekend Courses: Over 3 Weekends, Saturdays and Sundays, 3.5 hour Sessions per day. (Please check your specific course schedule)
  1. Laptop with Windows 7, 8, 10 / MacOS / Linux
  2. Internet Connectivity
  3. Latest Chrome / Firefox Browser
  4. Microsoft Excel
  5. Python Version 3 or above (https://www.python.org/downloads/)
  6. Anaconda Platform (https://www.anaconda.com/distribution/)
All courses on Teksands are taught by Industry Professionals, highly qualified and focused Research Scholars from Reputed University

Certificate

Upon successful completion of the programme, participants will be awarded a verified digital certificate by Teksands.

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certificate

Teksands.ai is a fast growing start-up in the EdTech Industry. We provide Online Courses on Deep Tech including Data Science, Machine Learning, Artificial Intelligence, Python, Deep Learning, Neural Network, to name a few. Our courses are intended to primarily help working professionals achieve career augmentation or career switch in Deep Tech areas by delivering very high quality, application driven training suited to the needs of our learners needs and goals.

Demo Class Video

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