Machine Learning using Python

Pre-Requisites: There is no pre-requisite as such. Anyone with an aptitude for learning programming and interest for doing analysis on data can be the good fit for this course. Knowledge on Data Science using Python is good to have but not a must have to do this program.

5-stars

Key Features

  • Online live classroom available
  • Quality learning materials
  • Small Class Sizes
  • State of the Art Facility
  • Free Retakes
  • Instructor Led Classroom training
  • Certified Industry Experienced Teachers
  • 100% Job Placement assistance

Individuals looking for a career in programming or are currently working as developers, data analysts, researchers, programmers, or web developers should attend this course. The course covers basic data science, Machine Learning with Python, along with all advance features that an individual will perform as a data science professional. So, this course will help IT Developer, Project Manager and Analytics Professional to grow in their analytics journey.

  • Fundamentals of Machine Learning
  • Tools supporting Machine Learning solutions
  • Understanding concept of Classification
  • Understanding concept of Clustering
  • Understanding concept of Regression
  • Understanding concept of Recommender Systems
  • Advanced Machine Learning
  • Capstone Project with the usage of Machine Learning

Data science jobs are the most demanding jobs in the Information Technology field today. Prospective job seekers have numerous opportunities. It is the fastest growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. This makes Data Science a highly employable job sector.

Data Science is a vastly abundant field and has a lot of opportunities. The field of Data Science is high in demand but low in supply of Data Scientists.

Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option.

There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field. Therefore, you will have the opportunity to work in various fields.

Companies require skilled Data Scientists to process and analyze their data. They not only analyze the data but also improve its quality. Therefore, Data Science deals with enriching data and making it better for their company.

Data Scientists allow companies to make smarter business decisions. Companies rely on Data Scientists and use their expertise to provide better results to their clients. This gives Data Scientists an important position in the company.

In this course, you will learn about how the data is used with different analytics steps such as business understanding, data collection, data wrangling, data exploration, data modelling and data visualization etc.

Machine Learning with Python

  • Introduction to Machine Learning
  • Regression
  • Classification
  • Clustering
  • Recommender Systems

Advanced Machine Learning

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Concept of Text Analytics & NLP
  • Assignment on advanced machine learning

Machine Learning Capstone Project

  • Use case & Identify datasets
  • ETL & Feature creation
  • Model definition and Training
  • Model Evaluation
  • Model Tuning
  • Model Deployment
  • Documentation

What is Machine Learning with Python Course?

Machine learning enables a system to learn from data rather than through explicit programming. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine-learning model is the output generated when you train your machine-learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model. Machine learning enables models to train on data sets before being deployed. Some machine- learning models are online and continuous. This iterative process of online models leads to an improvement in the types of associations made between data elements. Due to their complexity and size, these patterns and associations could have easily been overlooked by human observation. After a model has been trained, it can be used in real time to learn from data. The improvements in accuracy are a result of the training process and automation that are part of machine learning.

This course focuses on the practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with python, Machine learning, Use case & Identify datasets, ETL & Feature creation, Model definition and Training, Model Evaluation, Model Tuning, Model Deployment and Documentation.

Why Machine Learning with Python Course?

Where machine learning – at its core – is about the use and development of learning algorithms, exploratory analysis, discovery and building predictive models for the extraction of knowledge from data to answer particular question or solve particular problems. Data science is more about the extraction of knowledge from data to answer particular question or solve particular problems. Machine learning is often a big part of a “data science” project.

Data science jobs are the most demanding jobs in the Information Technology field today. Prospective job seekers have numerous opportunities. It is the fastest-growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. Data Science is one of the most highly paid jobs. According to Glassdoor, Data scientists make an average of $116,100 per year. There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field. Therefore, you will have the opportunity to work in various fields as a highly lucrative career option.

More than ever before, companies are relying on data to make business decisions. Without data science, these industry trends stay undiscovered — no story to tell and no insights to share. To determine business goals, more and more companies are looking for data scientists to fill in the gaps. Data science is one of the fastest-growing and sectors of the tech industry.

This course will qualify you for a position as a data scientist or a data analyst. If you have a professional background in programming, you may also be able to get a position as a data engineer or a machine learning engineer.

Who should go for this Training?

Individuals looking for a career in programming or are currently working as developers, data analysts, researchers, programmers, or web developers should attend this course. The course covers basic data science along with all advance features that an individual will perform as a data science professional. So, this course will help IT Developer, Project Manager and Analytics Professional to grow in their analytics journey.

What background knowledge is necessary?

Not required as such. Anyone with an aptitude for learning programming and has interest for doing analysis on data can be a good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.

What will I learn in this course?

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning ). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

  • Fundamentals of Machine Learning
  • Tools supporting Machine Learning solutions
  • Understanding concept of Classification
  • Understanding concept of Clustering.
  • Understanding concept of Regression
  • Understanding concept of Recommender Systems
  • Advanced Machine Learning
  • Capstone Project with the usage of Machine Learning

What is the duration of this Course?

Total Duration is 48 hours

How much does a Data Scientist earn?

Salary Estimates as on October 8th, 2020 in for a Data Scientist in USA are

Zip Recruiter – 76K-160K per Year

Glass Door – 83K-150K per Year

PaySclae USA – 67K-130K per Year

The average salary for a Data Scientist in Detroit, Michigan is $87815 as per PaySclae USA.

What are the prerequisites for this course?

There is no pre-requisite as such. Anyone with an aptitude for learning programming and interest for doing analysis on data can be the good fit for this course. The course will use python programming language for doing data analysis so python programming language will be taught as part of this course.

What skills do I learn in this course – Machine Learning with Python?

Here are few to mention practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with python, statistics for model building and evaluation using the following tools and methods.

  • Jupyter Notebook for Python
  • Python Programming for Data Science
  • Databases and SQL for Data Science
  • Machine Learning with Python
  • Deep Learning

How do I become a Data Scientist?

This course is designed to give you an insight into Industry driven Data Science tools and methodologies, which is enough to prepare you to excel in your next role as a Data Scientist. The program will train you on Python, Machine Learning techniques, data reprocessing, regression, clustering, data analytics, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operational zing an analytics project, and data visualization techniques.

Are Machine Learning and Data Science the same?

No, Machine Learning and Data Science are not the same. They are two different domains of technology that work on two different aspects of businesses around the world. While Machine Learning focuses on enabling machines to self-learn and execute any task, Data science focuses on using data to help businesses analyze and understand trends. However, that’s not to say that there isn’t any overlap between the two domains. Both Machine Learning and Data Science depend on each other for various kinds of applications as data is indispensable and Machine Learning technologies are fast becoming an integral part of most industries.

What software/technology stacks do you use?

While we do work Python during the training, knowledge of any programming language will work- as we teach the principles from a “software agnostic” point of view, the principles transfer across programming languages. We teach how to interpret data, and then how to apply machine learning to take that to the next level.

Is R used extensively today in data science?

We would say that R was probably THE language for doing statistics or “data science” work about 5-10 years ago. Today, as the Python sci-stack caught up and keeps growing, it’s about as widely used as Python for similar tasks. I can see a shift more towards Python in future though because there seems to be more development going on at the moment towards scalability and computational efficiency.

Which is better, Machine Learning or Data Science?

To begin with, one cannot compare the two domains to decide which is better – precisely because they are two different branches of studies. It is like comparing science and arts. However, one cannot deny the obvious popularity of data science today. Almost all the industries have taken recourse to data to arrive at more robust business decisions. Data has become an integral part of businesses, whether it is for analyzing performance or device data-powered strategies or applications. Machine Learning, on the other hand, is still an evolving branch which is yet to be adopted by a few industries which only goes on to say that ML technologies will have more demand relevance in the near future. So, professionals of both these domains will be in equal demands in the future.

Is Data Science required for Machine Learning?

Since both Machine Learning and Data Science are closely connected, a basic knowledge of each is required to specialize in either of the two domains. Having said that, more than data science the knowledge of data analysis is required to get started with Machine Learning. Learning programming languages like R, and Python are required to understand and clean data to use it for creating Machine Learning algorithms. Most Machine Learning courses include tutorials on these programming languages and basic data analysis and data science concepts.

How much statistics will I need to know?

We work hard to ensure that no prior statistics knowledge is required. We will teach you all the basics you need to know before and during the bootcamps. We cover correlations, hypothesis testing, and Linear Regression in the Course, all at a level appropriate for someone with no/little statistics experience.

What is the difference between a Data Scientist, Data Analyst, and Data Engineer?

We’ve certainly seen variation in regards to what employers have in mind when they use these terms, so please consider the answers below as general guidelines.

A Data Analyst is someone who creates and communicates insights from data to measure outcomes, make predictions, and guide business decisions. Often, there is a lighter coding burden placed upon someone with the title Data Analyst, though they may be expected to know certain languages or packages in R or python.

A Data Engineer is the designer, builder, and manager of the information or “big data” infrastructure. Each develops the architecture that helps analyze and process data in the way the organization needs it – and they make sure those systems are performing smoothly.

The term Data Scientist is used the most broadly. A job posting for a Data Scientist might describe a role identical to others calling for “data analyst,” though there is usually more diverse coding skills needed for a data scientist job. For the most part, data scientists are asked to participate in the entire cycle of problems and solutions. They help identify opportunities for companies to use data, while also finding, collecting, and integrating relevant data sources, performing analyses of varying degrees of complexity, writing code and creating tools that teams and businesses can use over time, and telling the story of what they’ve done to company stakeholders.

What is the Future of Data Science?

Putting it slightly differently – Data Science is the future. No businesses or industries for that matter will be able to keep up without data science. A large number of transitions have already happened worldwide where businesses are seeking more data-driven decisions, more is to follow suit. Data science quite rightly has been dubbed as the oil of the 21st century which can mean endless possibilities across industries. So, if you are keen on pursuing this path, your efforts will be highly rewarded with not just a fulfilling career and fat pay cheques but also a lot of job security.

Course Number : DATA-0-102

Duration : 48 hours

[]
1 Step 1

Request Information!

Who you are?
First Nameyour first name
Last Nameyour last name
Address
City
ZipZip
Phone Number
Commentsmore details
0 /

By submitting this form, you are giving your express written consent for Global Information Technology to contact you regarding our programs and services using email, telephone or text.  This consent is not required to purchase goods/services, and you may always call us directly at 1-866-464-4846.

Previous
Next