Machine Learning with Python Training in Washington, DC, United States

Build Python skills with varied approaches to Machine Learning

  • Supervised & Unsupervised Learning, Regression & Classifications and more
  • Advanced ML algorithms like KNN, Decision Trees, SVM and Clustering
  • Build and deploy deep learning and data visualization models in a real-world project
  • 250,000 + Professionals Trained
  • 250 + Workshops every month
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Grow your Machine Learning skills

In this four-week course, you will dive into the basics of machine learning using the well-known programming language, Python. Get introduced to data exploration and discover the various machine learning approaches like supervised and unsupervised learning, regression, and classifications and more.

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Highlights

  • 48 Hours of Live Instructor-Led Sessions

  • 80 Hours of Assignments and MCQs

  • 45 Hours of Hands-On Practice

  • 10 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

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Why Machine Learning?

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Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand machine learning and Python skills and meet that need.  

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Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on. 

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.

Prerequisites

Prerequisites for Machine Learning with Python training

  • Sufficient knowledge of at least one coding language is required.
  • Minimalistic and intuitive, Python is best-suited for Machine Learning training.

Who should attend this course?

Anyone interested in Machine Learning and using it to solve problems

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Machine Learning with Python Course Schedules

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What you will learn in the Machine Learning with Python course

1

Python for Machine Learning

Learn about the various libraries offered by Python to manipulate, preprocess, and visualize data  

2

Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning. 

3

Optimization Techniques

Learn to use optimization techniques to find the minimum error in your Machine Learning model

4

Supervised Learning

Learn about Linear and Logistic Regression, KNN Classification and Bayesian Classifiers

5

Unsupervised Learning

Study K-means Clustering  and Hierarchical Clustering

6

Ensemble techniques

Learn to use multiple learning algorithms to obtain better predictive performance 

7

Neural Networks

Understand Neural Network and apply them to classify image and perform sentiment analysis

Skill you will gain with the Machine Learning with Python course

Advanced Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Distribution of data: variance, standard deviation, more

Calculating conditional probability via Hypothesis Testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Logistic Regression models

K-means Clustering and Hierarchical Clustering

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for both regression and classification

Hyper-parameter tuning like regularisation

Ensemble techniques: averaging, weighted averaging, max voting

Bootstrap sampling, bagging and boosting

Building Random Forest models

Finding optimum number of components/factors

PCA/Factor Analysis

Using Apriori Algorithm and key metrics: Support, Confidence, Lift

Building recommendation engines using UBCF and IBCF

Evaluating model parameters

Measuring performance metrics

Using scree plot, one-eigenvalue criterion

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Machine Learning with Python Training Curriculum

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Learning objectives
In this module, you will learn the basics of statistics including:

  • Basics of statistics like mean (expected value), median and mode 
  • Distribution of data in terms of variance, standard deviation, and interquartile range; and explore data and measures and simple graphics analyses  
  • Basics of probability via daily life examples 
  • Marginal probability and its importance with respect to Machine Learning 
  • Bayes’ theorem and conditional probability including alternate and null hypotheses  

Topics

  • Statistical Analysis Concepts  
  • Descriptive Statistics  
  • Introduction to Probability 
  • Bayes’ Theorem  
  • Probability Distributions  
  • Hypothesis Testing and Scores  

Hands-on

  • Learning to implement statistical operations in Excel 

Learning objectives
In the Python for Machine Learning module, you will learn how to work with data using Python:

  • How to define variables, sets, and conditional statements 
  • The purpose of functions and how to operate on files to read and write data in Python  
  • Understand how to use Pandas - a must have package for anyone attempting data analysis with Python 
  • Data Visualization using Python libraries like matplotlib, seaborn and ggplot 

Topics

  • Python Overview  
  • Pandas for pre-Processing and Exploratory Data Analysis  
  • NumPy for Statistical Analysis  
  • Matplotlib and Seaborn for Data Visualization  
  • Scikit Learn 

Learning objectives
Get introduced to Machine Learning via real-life examples and the multiple ways in which it affects our society. You will learn:

  • Various algorithms and models like Classification, Regression, and Clustering.  
  • Supervised vs Unsupervised Learning 
  • How Statistical Modelling relates to Machine Learning 

Topics

  • Machine Learning Modelling Flow  
  • How to treat Data in ML  
  • Types of Machine Learning  
  • Performance Measures  
  • Bias-Variance Trade-Off  
  • Overfitting and Underfitting  

Learning objectives
Gain an understanding of various optimisation techniques such as:

  • Batch Gradient Descent 
  • Stochastic Gradient Descent 
  • ADAM 
  • RMSProp

Topics

  • Maxima and Minima  
  • Cost Function  
  • Learning Rate  
  • Optimization Techniques  

Learning objectives
In this module you will learn about Linear and Logistic Regression with Stochastic Gradient Descent via real-life case studies

  • Hyper-parameters tuning like learning rate, epochs, momentum, and class-balance 
  • The concepts of Linear and Logistic Regression with real-life case studies 
  • How KNN can be used for a classification problem with a real-life case study on KNN Classification  
  • About Naive Bayesian Classifiers through another case study 
  • How Support Vector Machines can be used for a classification problem 
  • About hyp

Topics

  • Linear Regression Case Study  
  • Logistic Regression Case Study  
  • KNN Classification Case Study  
  • Naive Bayesian classifiers Case Study  
  • SVM - Support Vector Machines Case Study

Hands-on

  • Build a regression model to predict the property prices using optimization techniques like gradient descent based on attributes describing various aspect of residential homes 
  • Use logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers 
  • Predict if a patient is likely to get any chronic kidney disease based on the health metrics 
  • Use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham 
  • Build models to study the relationships between chemical structure and biodegradation of molecules to correctly classify if a chemical is biodegradable or non-biodegradable 

Learning objectives
Learn about unsupervised learning techniques:

  • K-means Clustering  
  • Hierarchical Clustering  

Topics

  • Clustering approaches  
  • K Means clustering  
  • Hierarchical clustering  
  • Case Study 

Hands-on

  • Perform a real-life case study on K-means Clustering  
  • Use K-Means clustering to group teen students into segments for targeted marketing campaigns

Learning objectives
Learn the ensemble techniques which enable you to build machine learning models including:

  • Decision Trees for regression and classification problems through a real-life case study 
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID 
  • Basic ensemble techniques like averaging, weighted averaging and max voting 
  • You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting 
  • Random Forest, with a real-life case study, and how it helps avoid overfitting compared to decision trees 
  • The Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis 
  • The comprehensive techniques used to find the optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • PCA/Factor Analysis via a case study 

Topics

  • Decision Trees with a Case Study 
  • Introduction to Ensemble Learning  
  • Different Ensemble Learning Techniques  
  • Bagging  
  • Boosting  
  • Random Forests  
  • Case Study  
  • PCA (Principal Component Analysis)  
  • PCA 
  • Its Applications  
  • Case Study

Hands-on

  • Build a model to predict the Wine Quality using Decision Tree (Regression Trees) based on the composition of ingredients 
  • Use AdaBoost, GBM, and Random Forest on Lending Data to predict loan status and ensemble the output to see your results 
  • Apply Reduce Data Dimensionality on a House Attribute Dataset to gain more insights and enhance modelling.  

Learning objectives
Learn to build recommendation systems. You will learn about:

  • Association Rules 
  • Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift 
  • UBCF and IBCF including how they are used in Recommender Engines 

Topics 

  • Introduction to Recommendation Systems  
  • Types of Recommendation Techniques  
  • Collaborative Filtering  
  • Content-based Filtering  
  • Hybrid RS  
  • Performance measurement  
  • Case Study

Hands-on

  • Build a Recommender System for a Retail Chain to recommend the right products to its customers 

FAQs on the Machine Learning with Python Course

Machine Learning with Python Training

KnowledgeHut’s Machine Learning with Python workshop is focused on helping professionals gain industry-relevant Machine Learning expertise. The curriculum has been designed to help professionals land lucrative jobs across industries. At the end of the course, you will be able to: 

  • Build Python programs: distribution, user-defined functions, importing datasets and more 
  • Manipulate and analyse data using Pandas library 
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot 
  • Build data distribution models: variance, standard deviation, interquartile range 
  • Calculate conditional probability via Hypothesis Testing 
  • Perform analysis of variance (ANOVA) 
  • Build linear regression models, evaluate model parameters, and measure performance metrics 
  • Use Dimensionality Reduction 
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics 
  • Perform K-means Clustering and Hierarchical Clustering  
  • Build KNN algorithm models to find the optimum value of K  
  • Build Decision Tree models for both regression and classification problems  
  • Use ensemble techniques like averaging, weighted averaging, max voting 
  • Use techniques of bootstrap sampling, bagging and boosting 
  • Build Random Forest models 
  • Find optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • Perform PCA/Factor Analysis 
  • Build Apriori algorithms with key metrics like Support, Confidence and Lift 
  • Build recommendation engines using UBCF and IBCF 

The program is designed to suit all levels of Machine Learning expertise. From the fundamentals to the advanced concepts in Machine Learning, the course covers everything you need to know, whether you’re a novice or an expert. 

To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities. 

This immersive and interactive workshop with an industry-relevant curriculum, capstone project, and guided mentorship is your chance to launch a career as a Machine Learning expert. The curriculum is split into easily comprehensible modules that cover the latest advancements in ML and Python. The initial modules focus on the technical aspects of becoming a Machine Learning expert. The succeeding modules introduce Python, its best practices, and how it is used in Machine Learning.  

The final modules deep dive into Machine Learning and take learners through the algorithms, types of data, and more. In addition to following a practical and problem-solving approach, the curriculum also follows a reason-based learning approach by incorporating case studies, examples, and real-world cases.

Yes, our Machine Learning with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job. 

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Machine Learning with Python course is ideal for:
  1. Anyone interested in Machine Learning and using it to solve problems  
  2. Software or Data Engineers interested in quantitative analysis with Python  
  3. Data Analysts, Economists or Researchers 

There are no prerequisites for attending this course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

To attend the Machine Learning with Python training program, the basic hardware and software requirements are as mentioned below

Hardware requirements 

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors 
  • 4 GB RAM 
  • 10 GB of free space  

Software Requirements  

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox  

System Requirements 

  • 32 or 64-bit Operating System 
  • 8 GB of RAM 

On adequately completing all aspects of the Machine Learning with Python course, you will be offered a course completion certificate from KnowledgeHut.  

In addition, you will get to showcase your newly acquired Machine Learning skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects. 

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a Machine Learning project in the real business world.  

Workshop Experience

The Machine Learning with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.  

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.    

The Machine Learning with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Machine Learning. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects. 

Our Machine Learning course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.  

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Machine Learning with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it. 

Schedules for our upcoming workshops in Machine Learning with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.   

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.  

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing. 

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger. 

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us on support@knowledgehut.com and we will be happy to get back to you. 

What learners are saying

E
Elyssa Taber IT Manager.
3

I would like to thank the KnowledgeHut team for the overall experience. My trainer was fantastic. Trainers at KnowledgeHut are well experienced and really helpful. They completed the syllabus on time, and also helped me with real world examples.

Attended Agile and Scrum workshop in June 2020

L
Lauritz Behan Computer Network Architect.
5

Overall, the training session at KnowledgeHut was a great experience. I learnt many things. I especially appreciate the fact that KnowledgeHut offers so many modes of learning and I was able to choose what suited me best. My trainer covered all the topics with live examples. I'm glad that I invested in this training.

Attended PMP® Certification workshop in May 2020

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Yancey Rosenkrantz Senior Network System Administrator
5

The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Attended Agile and Scrum workshop in April 2020

R
Raina Moura Network Administrator.
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I would like to extend my appreciation for the support given throughout the training. My special thanks to the trainer for his dedication, and leading us through a difficult topic. KnowledgeHut is a great place to learn the skills that are coveted in the industry.

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5

Knowledgehut is known for the best training. I came to know about Knowledgehut through one of my friends. I liked the way they have framed the entire course. During the course, I worked on many projects and learned many things which will help me to enhance my career. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

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Prisca Bock Cloud Consultant
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KnowldgeHut's training session included everything that had been promised. The trainer was very knowledgeable and the practical sessions covered every topic. World class training from a world class institue.

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Rafaello Heiland Prinicipal Consultant
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I am really happy with the trainer because the training session went beyond my expectations. Trainer has got in-depth knowledge and excellent communication skills. This training has actually prepared me for my future projects.

Attended Agile and Scrum workshop in April 2020

B
Barton Fonseka Information Security Analyst.
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This is a great course to invest in. The trainers are experienced, conduct the sessions with enthusiasm and ensure that participants are well prepared for the industry. I would like to thank my trainer for his guidance.

Attended PMP® Certification workshop in July 2020

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Learn Machine Learning

Learn Machine Learning in Washington, DC, USA

 

Machine Learning uses the concept of artificial intelligence to help the systems learn, perform, and improve solutions to problems. They do not require any additional reprogramming or human help. The field focuses on data analysis without human intervention.

In Machine Learning, the available data is observed using examples to derive insights and information. The pattern is deciphered by systems and programs after analyzing the data. 

All the major algorithms can be divided into the following categories:

  • Supervised Machine Learning Algorithms: It uses labeled examples for taking the information from the past and applying it to the new data, which is used to predict events in the future. This is how the whole process works:
    • The dataset is fed into the system. This data is then used to train the model.
    • A learning algorithm is derived from training and learning and used to make predictions in the form of inferred function.
    • Lastly, the learning algorithm is used to derive results from the new inputs.
  • Unsupervised Machine Learning Algorithms: In these, unclassified and unlabelled data is provided to the system to help it learn and train. These are used to find a hidden structure and infer a function from the unlabeled data. But, these systems are not able to get the correct results. They can just draw inferences and describe hidden structures.

According to a recent study, Washington, DC one is now the 4th-most desirable city for startups in the U.S., behind New York and the Bay Area. There are more than 2000 startups in Washington, DC, including Optoro, EverFi, Inc., Mapbox, Virtru, Fundrise, FiscalNote, etc. It is also home to several leading companies such as Amazon, Starbucks, Costco Wholesale, Boeing, Microsoft, etc. From building more robust forecasting models to enhancing the customer support experience, Machine Learning is all these leading companies in improving current practices and find new revenue streams. 

Machine Learning is all about data. The data is gathered, cleaned, analyzed and used to train the model to get the best outcomes. Here is why it is important in our society:

  • It's easy and it works

Machines can provide faster solutions than human. With the right algorithm, a machine can evaluate, resolve, and work out a problem to get better results.

  • Being used in a wide range of applications today

The field has found applications in several real-world domains. It saves money, time and efforts. Organizations can get the work done effectively and efficiently. Sectors like health care, banking, customer service, finance, government institutes, transport, etc. have started incorporating machine learning.

Every day tons of data is generated. More and more companies in Washington, DC have started to see the importance of it and are shifting towards data-driven decision making. From Fortune 500 companies to small startups, every company is now a data company.

The state of Machine Learning in companies and your daily life

Machine Learning is still considered a new field in which a lot of research is required. Companies like Google and Facebook collect data and provide it to other companies for solutions like Amazon's product recommendations, Uber’s surge pricing, predictions of financial frauds in banks, etc. With every passing day, these systems are becoming less and less dependent on humans. 

The benefits of Machine Learning include - 

  1. It reels in better job opportunities: 

The fields of Machine Learning and Artificial Intelligence are expanding. With this expansion, comes the demand for professionals. In Washington, DC, companies like Google, Fannie Mae, Atlas Research, KPMG, Booz Allen Hamilton, Guidehouse, etc. are looking for Machine Learning engineers to join their team.

  1. Machine Learning engineers earn a pretty penny: 

Machine Learning engineers are paid handsomely. In Washington, DC, they can earn an average of about $139,040 per year.

  1. Demand for Machine Learning skills is only increasing: 

There is a shortage of machine learning engineers. Many experts have pointed out this gap in demand and supply. This has led to an increase in the salary of machine learning professionals. And as more and more companies are shifting towards the field, this demand is only going to rise.

  1. Most of the industries are shifting to Machine Learning: 

Industries in Washington, DC have started to see the benefits of data and have started to deal with data. This data production will only increase in the future. With data, companies are not only able to work efficiently, but can get ahead in the competition.

Machine learning is a big and diverse field that is expanding every single day. Here is how you can stay motivated and learn Machine Learning:

Below are the steps you can follow:

  • Structural Plan: Create a plan that will include a list of all the topics you will be learning. It should be in a proper, structured form and include all the required topics.
  • Prerequisite: Select a programming language that you will be using in your machine learning projects. Also, brush up your mathematical and statistical skills.
  • Learning: Start learning according to the plan that you have created. There are several resources like books, video tutorials, etc. that are available online for free. You can also various boot camps in Washington, DC to learn how to apply Machine Learning to real-world problems. 
  • Implementation: Build a project on the algorithms that you are studying. Pick a dataset and start solving it. The more you practice it, the better your skills will be. You can also try participating in online competitions.

Here is how you can get started in Machine Learning as an absolute beginner:

  • Adjust your mindset: You need to make up your mindset that you are going to learn Machine Learning. Remember that the more you practice, the better you will be able to understand the concepts. Surround yourself with people working in the same field to help you stay motivated. You can also join Meetup groups in Washington, DC focused on providing a place for machine learning engineers.  
  • Pick a process that suits you best: Figure out a systematic and structured process of working your problems and getting a solution.
  • Pick a tool: Next step is to select a tool on which you will be practicing. Here are a few tools that you can use:
    • Beginners - Weka Workbench
    • Intermediate - Python Ecosystem
    • Advanced - R Platform
  • Practice on Datasets: There are several datasets available on which you can practice your machine learning skills. Use small datasets in the beginning. Also, make sure that the dataset is a real-world problem.
  • Build your portfolio: Start creating projects to build your CV and demonstrate your machine learning skills.

Here are the key technical skills required to learn machine learning and become a machine learning engineer in Washington, DC:

  1. Programming languages: It is one of the most basic and important prerequisites for machine learning. You must be familiar with languages like Python, Java, Scala, etc. Having programming skills will help you understand the concepts of machine learning better. Make sure that you are familiar with topics like data processing, data formats, etc.
  2. Database skills: You need to have a complete understanding of relational databases, SQL, and MySQL. While working with machine learning, you will be dealing with databases from different sources at the same time. You must be able to read the data and convert into a format that is compatible with the framework.
  3. Machine Learning visualization tools: Visualization is an important skill required to be a machine learning engineer. You must be familiar with the tools used for data visualization.
  4. Knowledge of Machine learning frameworks: You must have experience in working with frameworks like Apache Spark, Scala, NLP, TensorFlow, R, etc.
  5. Mathematical skills: These skills are required for creating a machine learning model and processing the data. You must be an expert in mathematics concepts like Calculus, Calculus of variations, Bayesian modeling, graph theory, distribution fitting, calculus of variations, linear algebra, optimization, hypothesis testing, probability theory, probability distributions, mathematical statistics, time series, regression, etc.

If you want to use python for executing a successful Machine Learning project, you need to follow these steps:

  • Gathering data: Collect the data required for the project. Make sure that the quality of the data is good because it will determine the performance of your model.
  • Cleaning and preparing data: Next step is to clean the gathered data. You cannot inject the raw data into your model. You need to find the missing data and remove the unnecessary ones. Feature engineering is used to convert the data into a format that can be used by the model. This data is then divided into two halves, one for training and the other for testing.
  • Visualize the data: Data visualization is required to show the relation between the data. It provides you an understanding of the data that will help you select the right model.
  • Choosing the correct model: Next step is selecting the algorithm and model you will be working on. This is an important step as it will play a major role in determining how well your model works.
  • Train and test: This step involves injecting the prepared data into the model. After this, you can train your model. In the end, the model is tested using the testing data.
  • Adjust parameters: The last step is to fine-tune the parameters of the model to get a more accurate result.

Algorithms are the backbone of machine learning. Here is how you can learn the top essential machine learning algorithms:

  1. List the various Machine Learning algorithms: Write down a list of all the algorithms that you want to learn. Remember that each algorithm is different and has its purpose. Categorize these algorithms according to their types and classes.
  2. Apply the Machine Learning algorithms that you’ve listed down: Don’t just focus on the theory. Instead, start implementing the algorithms. Take on real-world datasets and apply your algorithm on it. Algorithms like decision trees, support vector machines, etc. must be thoroughly understood and implemented.
  3. Describe these Machine Learning algorithms: Write a description of the algorithm you are learning. This will help you keep track of them in the future. Explore the algorithm and write everything that you discover. It will be like a small encyclopedia of machine learning algorithms.
  4. Experiment on Machine Learning Algorithms: Start experimenting with standardizing datasets, variables, the functioning of the algorithm, etc. 

Machine Learning Algorithms

The most essential machine learning algorithm for beginners is the k nearest neighbor algorithm. It is simple and uncomplicated that makes it perfect for beginners. Here is how it works:

  • In this algorithm, you need to store a number as ‘k’. This defines the number of training samples that are near to the new data point.
  • A label will be assigned to this new data point. This label is already defined by and assigned to the neighbors.
  • It works on the principle of radius based classification. For identifying and classifying the samples under a fixed radius, the density of the neighbor data points is measured.
  • Known as a non-generalizing machine learning method, this algorithm remembers the provided training data.
  • Classification is performed after the vote is taken among the unknown sample’s neighbors.

The decision to study algorithms while learning Machine Learning depends on the following two factors:

  • If you are planning to just use the machine learning algorithms, you don't need to know any classic algorithms.
  • If you want to use your machine learning concepts to innovate, you need to have a basic understanding of algorithms. This will help you design a new algorithm. You need to know how correct an algorithm is, its complexity, how long does it takes, what are the constraints, etc. Only after this, you will be able to experiment with concepts of machine learning.

The top different types of Machine Learning Algorithms include:

  • Supervised Learning: This involves usage of classified, historical data for mapping input variables to output variables. The following algorithms use supervised learning:
    • Linear Regression –  It follows relationship between input variables (x) and output variable (y) in the equation, y = a + bx
    • Logistic Regression – This is similar to the linear regression. The only difference is that the outcome is probabilistic instead of an exact value. Using a transformative function, this probabilistic outcome is converted into a binary classification.
    • CART - Classification and Regression Trees (CART) uses decision trees. Each outcome's possibility is determined by defined nodes and branches. Each single input variable is the non-terminal node which is then split into various outcomes.
    • Naïve Bayes – This algorithm predicts the possibility of an outcome by using the variable’s basic value. It is called Naïve Bayes theorem because it is based on Bayes theorem and it assumes that all the variables are independent.
    • K-Nearest Neighbours – In this algorithm, first the dataset is charted. Next, to figure out the outcome of the value of the variable, the value of k is predefined. Then, all the nearest instances of k are collected and their average is used for producing the output. 
  • Unsupervised Learning: In this type of learning, output variables are not provided, just the input ones. The dataset’s underlying structure is analyzed for revealing possible associations and clusters. The algorithms that use unsupervised learning include:
    • Apriori - In this algorithm, instances of two items having frequent associations or occurring simultaneously are identified using transactional databases. The relationships are predicted using these associations.
    • K-Means -  In this, similar data are clustered together where each data point is associated with the supposed cluster’s centroid. By ensuring that the distance between the data point and the centroid is closest, the real centroid is determined.
    • PCA -  The Principal Component Analysis (PCA) algorithm is used for data visualization by decreasing the variables' count. The maximum variance of each point is mapped to a new coordinate system. The selected components correspond to the axes.
  • Ensemble Learning: In the ensemble learning, each learner’s results are used and combined to get a better representation of the outcome. Here are a few examples of algorithms that use Ensemble Learning:
    • Bagging – In this, the original dataset is used to generate multiple datasets on which an algorithm is applied to get multiple outcomes. The results are then compiled and performed upon to get the real outcome.
    • Boosting –It is similar to bagging algorithm, except that it works sequentially instead of in parallel. This means that every new dataset created learns from the miscalculations and errors of the previous datasets.

Simple algorithms can be used for solving simple ML problems. It must have the following characteristics:

  • Easy to understand and implement
  • Less time and resources are required

Based on the above-mentioned points, the k-nearest neighbor algorithm is considered to be the easiest algorithm. Here is why:

  • It is the simplest supervised learning algorithm that is best suited for beginners.
  • It can be used for regression and classification problems.
  • It is non-parametric that uses similarity measure for classification.
  • Labeled data is used during the training phase.
  • K nearest surroundings are used to predict the object’s location, where k is the number of neighbors.
  • Some real-life examples of kNN include:
    • Detecting patterns in credit card usage
    • Recognizing vehicle number plate
    • Searching documents containing similar topics.

The algorithm is the backbone of your project. So, it is very important that you select the right algorithm along with the right tools and models. Here, we have compiled a list of things you need to keep in mind before choosing the algorithm for your project:

  • Understanding your data: Your algorithm will depend on the data you are working on. So, you need to have a thorough understanding of the data. To do this, visualize the data using graphs and find a correlation between them. Next step is cleaning the data to remove unnecessary data and find the missing one. Once this is done, you need to perform feature engineering on the data to make it ready for injecting into the model.
  • Get the intuition about the task: You need to have a clear understanding of what your task is. Only after this, you will be able to figure out what type of learning to use. The types of learning include:
    • Supervised learning
    • Semi-supervised learning
    • Unsupervised learning
    • Reinforcement learning
  • Understand your constraints: For every project, you can't just select the best algorithm or tools. Some restraints prohibit this:
    • The available data storage will determine how much data you can use for training and testing.
    • Your hardware will affect the algorithm that you can use. Some algorithms require high computational power that can’t be offered by low-end machines.
    • Time constraints determine the length of the training phase.
  • Find available algorithms: After going through the above steps, you can select the one that follows all the constraints and requirements.

Here is how you can design and implement a machine learning algorithm using python:

  1. Select a programming language: First things first. You need to select a programming language that you will be using for creating Machine Learning algorithms. 
  2. Select the algorithm that you wish to implement: The next step is selecting the algorithm that you want to implement. You need to be precise and select the type, classes, description, and special implementation of the algorithm.
  3. Select the problem you wish to work upon: Next, you need to select the problem set you will be working on. You will have to implement your algorithm on it and validate its efficiency.
  4. Research the algorithm that you wish to implement: Before you implement your algorithm, you need to go through multiple resources to understand its description, outlook, and implementation. This will help you get a perspective on various methods of implementing that algorithm.
  5. Undertake unit testing: Last step is to develop and run unit tests for every function used in the algorithm. Consider this as a test drive of your algorithm.

When you are learning Machine Learning, it is important that you go through every concept. But there are a few topics that require more focus than others. Here, we have compiled a list of the most essential topics of machine learning that one needs to master:

  • Decision Trees: This is a classification problem that is used for creating a supervised learning algorithm. It helps the system in selecting what features need to be selected, what conditions are required for splitting, and what are the conditions for ending that splitting and a particular iteration. Here are a few advantages offered by decision trees:
    • Simple, easy to interpret, understand, and visualize
    • Performs feature selection and variable screening
    • Not affected by the non-linear relationship among the parameters
    • Fewer efforts are required for preparing data
    • Analyzes numerical and categorical data
    • Deals with problems that require multiple outputs
  • Support Vector Machines:  This classification method has a high accuracy rate and can be used for regression problems as well. They offer optimal solutions as they use global minimum instead of local minima. Also, they can be used for linearly and nonlinearly data. With the help of Support Vector Machines' Kernel Trick, feature mapping has become quite easy.
  • Naive Bayes: Based on Bayes theorem, this is a classification technique that assumes that the different predictors are independent of each other. It is highly scalable and requires less training data. It also converges quickly.
  • Random Forest algorithm: It is a supervised learning algorithm that randomizes the input while creating a forest of decision trees. This is done so that no pattern is identified based on its order. This algorithm can be used for classification and regression problems easily. 

Machine Learning Engineer Salary in Washington, D.C.

The median salary of Machine Learning Engineer in Washington, D.C. is $1,16,000/yr. The range differs from  $60,500 to as high as $1,37,000

The average salary of a machine learning engineer in Washington, D.C compared with Austin is $1,16,000/yr whereas, in Austin, TX, it’s $1,20,000/yr.

The average salary of a machine learning engineer in Washington, D.C is $1,16,000/yr whereas in New York it is $124,882.

Washington, D.C. is considered among the top developed states of the USA. The city is home to several startups that offer multiple opportunities to freshers as well as experienced employees. All the major organizations in the world are producing data. There is so much data and fewer skilled people to make sense of it. Since industries are realizing that Machine Learning is the future,  this why the machine learning engineer jobs have witnessed a massive expansion of 9.5 times of what it was a few years back. Also according to Indeed, machine learning engineer is an extremely promising position with an average salary base of 146,085 U.S. dollars and a whopping 344-per cent growth in job postings.These facts do reveal that the demand for an ML engineer is very high and likely to rise in the coming years.

From the high salary, large bonus, acknowledgment, career growth to endless possibilities, Machine learning offers all of it and that perhaps the reason why it is the fastest growing job sector and the pace doesn’t seem to slow down anytime soon.

Apart from the high payout, these are the perks which a machine learning engineer enjoys -

  • Network 
  • Opportunity to grow
  • Promising career
  • A peek into the future technologies

These are only a few of the many perks. It is no wonder this job is considered to be the dream job of engineering graduates of 2019.

Although there are quite many companies offering jobs to Machine Learning Engineers in Washington, D.C., following are the prominent companies - 

  • MITRE
  • MTEQ
  • OneWeb
  • Microsoft
  • IBM

Machine Learning Conference in Washington, D.C.

S.NoConference NameDateVenue
1.Intro to Machine Learning19 June, 2019

Galvanize Seattle - Pioneer Square

111 South Jackson Street

Seattle, WA 98104

2.Partner Power Hour- What is R, and how can I use it?

21 June, 2019

Code Fellows

2901 3rd Avenue

#300

Seattle, WA 98121

United States

3.

Dynamic Talks: Seattle/Redmond "Machine Learning for Enterprise Operations"

25 June, 2019

Microsoft Building 20

3709 Microsoft Way

Redmond, WA 98052

United States

4.

Data Science II: Practical Machine Learning – Seattle

20 August- 22

August, 2019

Hilton Seattle

1301 6th Avenue

Seattle, WA 98101

United States

5.

Seattle Machine Learning Meetup

17 October, 2019

Whitepages // Rainier Tower

1301 5th Ave

Floor 16

Seattle, WA 98101

United States

6.

Data Science Salon,  Seattle

17 October, 2019

TBD Seattle, WA 98101

United States

7.

Annual Conference of the Association for Computational Linguistics

6 July-11 July, 2020

Hyatt Regency Seattle, downtown Seattle, WA

  1.  Intro to Machine Learning, Washington, D.C. 
    1. About the conference: Attendees will get to learn the basics of Machine Learning and how to implement a machine learning model.
    2. Event Date: June 19, 2019
    3. VenueGalvanize Seattle - Pioneer Square 111 South Jackson Street Seattle, WA 98104
    4. Days of Program: 1
    5. Timings: 6:30 PM – 8:30 PM PDT
    6. Purpose: Learn the basics of machine learning using through examples and exercises given out by the instructor.
    7. Registration cost: Free
    8. Who are the major sponsors: Galvanize community
  1. Partner Power Hour- What is R, and how can I use it?, Washington, D.C.
    1. About the conference: This talk event is about the language 'R' and its history. R is a language designed by statisticians for statisticians.
    2. Event Date: June 21, 2019
    3. Venue: Code Fellows, 2901 3rd Avenue #300 Seattle, WA 9812, United States
    4. Days of Program: 1
    5. Timings: 12:15 PM – 1:00 PM PDT
    6. Purpose: To know about the history of this language, and how we can execute it into existing projects such as machine learning models, interactive visualizations and maps and even web apps.
    7. Speakers & Profile: Francisco Santamarina- PhD student at the University of Washington, D.C.
    8. Registration cost: Free
    9. Who are the major sponsors: Code Fellows
  1. Dynamic Talks: Seattle/Redmond "Machine Learning for Enterprise Operations", Washington, D.C.
    1. About the conference: It is the third event of an ongoing free technical meetup series, "Dynamic Talks".
    2. Event Date: June 25, 2019
    3. Venue: Microsoft Building 20 3709 Microsoft Way Redmond, WA 98052 United States
    4. Days of Program: 1
    5. Timings: 6:00 PM – 9:00 PM PDT
    6. Purpose: Explore Machine Learning for Enterprise Operations.
    7. How many speakers: 4
    8. Speakers & Profile
      1. Ilya Katsov- Head of Practice, Industrial AI Grid Dynamics
      2. Tatiana Dashevskiy- Senior Manager, Data Science, Corporate Strategy, T-Mobile 
      3. Jeffrey Sewell- Lead Technical Architect, Walt Disney Company
      4. Scott Burger- Former Senior Data Scientist, Tableau
    9. Registration cost: Free
    10. Who are the major sponsors: Grid Dynamics
  1. Data Science II: Practical Machine Learning - Seattle, Washington, D.C.
    1. About the conference: It is a 3-day course for practical Machine Learning. Learn about the fundamentals, essentials and building blocks of machine learning to create opportunities. 
    2. Event Date: August 20-August 22, 2019
    3. Venue: Hilton Seattle 1301 6th Avenue Seattle, WA 98101 United States
    4. Days of Program: 3
    5. Timings: 9:00 AM – 4:30 PM PDT
    6. Purpose: Deliver data-driven results and predict the future by building machine learning models that change every aspect of your business. 
    7. Registration cost: Starting from $2,585
    8. Who are the major sponsors: Pragmatic Institute
  1. Seattle Machine Learning Meetup, Washington, D.C.
    1. Event Date: October 17, 2019
    2. Venue: Whitepages // Rainier Tower 1301 5th Ave Floor 16 Seattle, WA 98101 United States
    3. Days of Program: 1
    4. Timings: 6:30 PM – 8:30 PM PDT
    5. With whom can you Network in this Conference: Network with the local like-minded people.
    6. Registration cost: $5
    7. Who are the major sponsors: Whitepages Pro
  1. Data Science Salon, Seattle
    1. About the conference: The application of Machine Learning and Artificial Intelligence in Retail technology and E-commerce will be discussed in this conference.
    2. Event Date: October 17, 2019
    3. Venue: Galvanize 111 S Jackson St Seattle, WA 98104 United States
    4. Days of Program: 1
    5. Timings: 7:45 AM – 8:00 PM PDT
    6. Purpose: To bring together Data Science practitioners and to discuss best practices and innovative solutions for the application of Machine Learning and Artificial Intelligence in Retail technology and E-commerce.
    7. With whom can you Network in this Conference: Science practitioner and other creative attendees.
    8. Registration cost: Starting from $125
    9. Who are the major sponsors: Formulated.by
  1. Annual Conference of the Association for Computational Linguistics, Washington, D.C.

    1. About the conference: It will cover a broad spectrum of diverse research areas that are concerned with computational approaches to natural language.
    2. Event Date: 6 July-11 July, 2020
    3. Venue: Hyatt Regency Seattle, downtown Seattle, WA
    4. Days of Program: 6
S.NoConference NameDateVenue
1.The Machine Learning Conference19 May, 2017AXIS Pioneer Square Seattle WA, USA
2.

Cloud+ Data Next Conference

15 September- 16 September, 2017

Washington, D.C. State Convention Center 705 Pike St, Seattle, WA 98101

3.

AI NEXTCon

17 January- 20 January, 2018

Meydenbauer Convention Center 11100 NE 6th St, Bellevue, WA 98004

4.

Informs regional Analytics Conference

14 September, 2018

Center for Urban Horticulture NHS Hall

3501 NE 41st Street Seattle, WA 98105

5.

2018 IEEE International Conference on Big Data

10 December- 13 December, 2018

1900 5th Avenue. Seattle, WA 98101, United States

6.

AutoML Workshop

10 December- 13 December, 2018

1900 5th Avenue. Seattle, WA 98101, United States

  1.  The Machine Learning Conference, Washington, D.C.
    1. About the conference: Recent research and application of Machine Learning methodologies and practices were discussed in this conference.
    2. Event Date: May 19, 2017
    3. Venue: AXIS Pioneer Square Seattle WA, USA
    4. Days of Program: 1
    5. Timings: 9:00 AM – 6:00 PM PDT
    6. How many speakers: 7
    7. Registration cost: $150
  1. Cloud+ Data Next Conference, Washington, D.C.
    1. About the conference: This leading conference for AI, Cloud and Data technology discussed 40+ topics and practical experience in tackling the industry's pressing problems.
    2. Event Date: September 15- September 16, 2017
    3. Venue: Washington, D.C. State Convention Center 705 Pike St, Seattle, WA 98101
    4. Days of Program: 2
    5. Timings: 9:00 AM – 6:00 PM PDT
    6. How many speakers: 18+.
    7. Networking in this Conference: Tech engineers, developers, and data scientists.
    8. Registration cost: $ 125 
    9. Who were the major sponsors: Uber and Huawei
  1. AI NEXTCon, Washington, D.C.
    1. About the conference: The conference discussed machine learning, deep learning, computer vision, speech recognition, NLP, data science and analytics.
    2. Event Date: January 17- January 20, 2018
    3. Venue: Meydenbauer Convention Center 11100 NE 6th St, Bellevue, WA 98004
    4. Days of Program: 4
    5. Timings: 9:00 AM – 5:00 PM PDT
    6. How many speakers: 29+
    7. Registration cost: $125 and up
    8. Who were the major sponsors: DIDI
  1. Informs regional Analytics Conference, Washington, D.C.
    1. About the conference: This Conference offered analytics professionals and managers an opportunity to learn from leading brands and leaders.
    2. Event Date: September 14, 2018
    3. Venue: Center for Urban Horticulture NHS Hall 3501 NE 41st Street Seattle, WA 98105
    4. Days of Program: 1
    5. Timings: 8:00 AM – 6:00 PM PDT
    6. Purpose: Learn how the methods of leading companies can be used to create competitive advantages.
    7. How many speakers: 14
    8. Registration cost: $150-$300
    9. Who were the major sponsors: INFORMS Analytics Society and INFORMS Pacific Northwest Chapter
  1. IEEE International Conference on Big Data, Washington, D.C.
    1. About the conference: Big Data Conference is an important conference that features more than 4000 like minded participants and leaders.
    2. Event Date: December 10- December 13, 2018
    3. Venue: 1900 5th Avenue. Seattle, WA 98101, United States
    4. Days of Program: 4
    5. Timings: 8:00 AM – 6:00 PM PDT
    6. Registration cost: $750 and up
    7. Who were the major sponsors: IEEE, IEEE Computer Society, Expedia group and Baidu
  1. AutoML Workshop, Washington, D.C.

    1. About the conference: It was the second international workshop on Automation in Machine Learning and Big Data and focused on Machine Learning methodologies and analytics.
    2. Event Date: December 10- December 13, 2018
    3. Venue: 1900 5th Avenue. Seattle, WA 98101, United States
    4. Days of Program: 4
    5. Timings: 6:00 PM – 9:00 PM PDT
    6. Registration cost: $450

Machine Learning Engineer Jobs in Washington, DC, USA

Below are the responsibilities of a Machine Learning Engineer in Washington, DC:

  • Design and develop Machine Learning and Deep Learning systems
  • Carry out statistical analysis 
  • Implement machine learning algorithms on the data
  • Conduct tests and experiments
  • Prepare and analyze historical data and identify patterns

From recommending related products to credit card purchase fraud detection to speech recognition, machine learning can provide direct value for a variety of industries. As a result, machine learning engineers are some of the most in-demand jobs in Data Science. Currently, there are more than 200 machine learning jobs available in Washington, DC. These companies range from small startups to medium-sized companies to big corporations. 

Here is a list of companies based in Washington, DC that are looking for Machine Learning Engineers:

  • Excella Consulting
  • Decisive Analytics
  • Indeed Prime
  • Econometrica
  • Transaction Network Services

The top professional groups for Machine Learning Engineer in Washington, DC include:

  • Artificial Intelligence and Machine Learning
  • Deep Learning DoJo
  • Washington DC – Artificial Intelligence & Deep Learning
  • DC Deep Learning Working Group

Here are some of the ML job roles that are in demand in 2019:

  • Machine Learning Engineer
  • Cloud Architects
  • Data Mining Specialists
  • Data Scientist
  • Cyber Security Analysts
  • Data Architect

To network with other Machine Learning Engineers in Washington, DC and expand your professional connections, you can try attending machine learning conferences, tech talks, and meetups. You should keep your LinkedIn profile updated and maintained.

The average salary for a Machine Learning Engineer is $139,040 per year in Washington, DC.

https://www.indeed.com/career/machine-learning-engineer/salaries/Washington--DC 

Machine Learning with Python Washington, DC, USA

To get started with using Python for Machine Learning, you need to follow these steps:

  1. The first step is to find the motivation to get started with Machine Learning.
  2. Install Python SciPy Kit along with all its packages.
  3. Get an understanding of the functions available in python and how they can be used.
  4. Download a dataset and load it. Understand its structure and working using statistical summaries and data visualization.
  5. Practice your programming skills on as many datasets as you can. This will also help you get an in-depth knowledge of ML concepts.
  6. When you start working with projects, begin with easy and simple projects and gradually move towards more complicated projects.

Here are the top python libraries used for implementing Machine Learning with Python:

  • Scikit-learn: This python library helps in data mining, data analysis, and data science.
  • Numpy: When dealing with N-dimensional arrays, this library offers high performance.
  • Pandas: This library is used for extracting and preparing data, and high-level data structures.
  • Matplotlib: It can help in plotting 2D graphs that can be used to better visualize the data.
  • TensorFlow: Used for implementing deep learning, this library trains, setups, and deploys artificial neural networks.

For successfully executing a successful Machine Learning project using python programming language, you need to follow the below-mentioned steps:

  • Gathering data: First, you need to gather the data relevant to your project. You will have to apply machine learning concepts on this data. So, remember that better data quality will result in better performance of the ML model.
  • Cleaning and preparing data: After you have collected the data, the next step is cleaning and preparing this data. The data that is generated every day is in the raw form meaning that it is not organized or labeled. Some of the important data can be missing. To fix this, feature engineering is used. This involves two steps – converting the data into the form ready for processing and dividing it into two parts, testing data and training data.
  • Visualize the data: To show the prepared data and find the relationship between the variables, data visualization is required.
  • Choosing the correct model: After you have completely understood the data, you have to choose the algorithm and the model best suited for the data. Your selection will determine how your project performs.
  • Train and test: The next step is using the data for training the ML model. After this, the testing data is used to check how accurate your model is.
  • Adjust parameters: After you have trained and tested the model, you need to adjust the parameters to improve accuracy. 

If you are a beginner and want to start programming with python, here are 6 best tips for you:

  • Consistency is Key: Practice is the key to becoming a great programmer. You need to practice every day. Start with just 30 minutes a day and then gradually increase the coding time. Muscle memory will help you a lot while learning to program.
  • Write it out: Write about what you are learning from the start. It will help you retain things for a longer period. 
  • Go interactive!: The interactive Python shell will help you implement your programming skills, especially the use of structures like strings, lists, dictionaries, etc. To open the python shell, fire up the terminal, type ‘python' and hit enter.
  • Assume the role of a Bug Bounty Hunter: Bugs are a part of a programmer's life. Instead of getting frustrated, you can take this as a challenge. The more bugs you can solve mean you are getting better.
  • Surround yourself with other people who are learning: Surrounding yourself with fellow Python developers will help you learn better. Get in touch with people who are either learning the language or working on it. Their experience will help you get tips and tricks.
  • Opt for Pair programming: In pair programming, there are 2 people involved. The first one is the driver who writes the code. The other one is the navigator who guides the driver through the process and confirms if the code is correct or not. This helps in getting two perspectives on a problem and the debugging process.

Python has several libraries that can be used for Machine Learning including the following:

  • Scikit-learn: This library comes in handy while dealing with data analysis, data mining, and data science.
  • SciPy: It used for manipulating concepts of mathematics, statistics, and engineering.
  • Numpy: It is used for handling vector and matrix operations fast and efficiently.
  • Keras: This library is used for dealing with a neural network.
  • TensorFlow: It provides quick setup, training, and deployment of artificial neural networks using multi-layered nodes.
  • Pandas: This library is used for extracting and preparing data through high-level data structures
  • Matplotlib: It helps plot 2D graphs and provides data visualization
  • Pytorch: It helps in dealing with Natural Language Processing.

Machine Learning with Python Course in Washington, DC

Machine Learning with Python Training in Washington

Washington, D.C., the capital of the United States of America, has a thriving economy. The city has a growing percentage of professional and business service and jobs that provide skilled professionals with growth opportunities. Diverse industries in finance, education, information technology, and scientific research contribute to Washington's economy. The opportunities in these sectors make it important for you to keep your skills and knowledge updated. Software professionals can benefit from Knowledge Hut's certification course in data analysis using Python. A New Alternative In the machine learning using Python course in Washington, you will learn why Python is useful for data analysis and machine learning. The online classes are designed in such a way that they help you understand and apply Python to real-life data analysis scenarios efficiently. The design methodologies that are covered in the data analysis training using Python in Washington include clustering, parallel processing, machine learning, data dimension reduction, data classification, etc. This training will give you a proper insight into the various frameworks available for data analysis. The instructors delivering this five-day data analysis using Python course in Washington are certified, experienced industry experts. These instructors ensure that you get a thorough understanding of Python packages useful for machine learning and data analysis, including Scipy, Matplotlib, IPython, Numpy, and Pandas. The training will enable you to use these Python packages to build solutions. The online training lessons of this course will allow you to efficiently manipulate, process, analyze, and clean data with requisite tools and libraries.

Keeping Ahead of the Curve

The machine learning using Python course in Washington is beneficial for webmasters, entrepreneurs, scientists, professional software developers, programmers, and analysts. Anyone interested in getting an in-depth knowledge of Python should attend this course. You will receive a certificate on finishing the machine learning training using Python. The price of the training is inclusive of this course completion certification. By the end of the course, you will be well aware of the advanced data structures and the various algorithms that are available in the Python packages useful for data analysis and machine learning. You will be adept at using Matplotlib for plotting. As a trained and certified professional in data analysis and machine learning using Python, you will get the credibility of being able to use the appropriate strategies for selecting the right data models.

KnowledgeHut Empowers You

The comprehensive training offered by KnowledgeHut and delivered by certified instructors is a convenient way to learn the concepts of data analysis and machine learning using Python. The combination of e-learning and hands-on practice assignments will reinforce the concepts covered in the training.

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