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

Know how Statistical Modeling relates to Machine Learning

  • 48 hours of Instructor led Training
  • Comprehensive Hands-on with Python
  • Covers Unsupervised learning algorithms such as K-means clustering techniques
  • Get introduced to deep learning techniques
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Description

With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.

So what is Machine Learning? Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need of explicit programming.

Our Machine learning course will help you to solve data problems using major Machine Learning algorithms, which includes Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning algorithms. It will help you to understand and learn:

  • The basic concepts of the Python Programming language
  • About Python libraries (Scipy, Scikit-Learn, TensorFlow, Numpy, Pandas,)
  • The data structure of Python
  • Machine Learning Techniques
  • Basic Descriptive And Inferential Statistics before advancing to serious Machine learning development.
  • Different stages of Data Exploration/Cleaning/Preparation in Python

The Machine Learning Course with Python by KnowledgeHut is a 48 hour, instructor-led live training sessions course, with 80 hours of MCQs and assignments. It also includes 45 hours of hands-on practical session, along with 10 live projects.

Why Learn Machine Learning from Knowledgehut?

Our Machine Learning course with Python will help you get hands-on experience of the following:

  1. Learn to implement statistical operations in Excel.
  2. Get a taste of how to start work with data in Python.
  3. Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.
  4. Learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies.
  5. Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering.
  6. Learn about Decision Trees for regression & classification problems through a real-life case study.
  7. Get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID.
  8. Learn the implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines.

What is Machine Learning?

Machine Learning is an application of Artificial Intelligence that allows machines and computers to learn automatically to predict outcomes from examples and experiences, without there being any need for explicit programming. As the name suggests, it gives machines and computers the ability to learn, making them similar to humans.

The concept of machine learning is quite simple. Instead of writing code, data is fed to a generic algorithm. The generic algorithm/machine will build a logic which will be based on the data provided. The provided data is termed as ‘training data’ as they are used to make decisions or predictions without any program to perform the task.

Practical Definition from Credible Sources:

1) Stanford defines Machine Learning as:

“Machine learning is the science of getting computers to act without being explicitly programmed.”

2) Nvidia defines Machine Learning as:

“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”

3) McKinsey & Co. defines Machine Learning as:

“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”

4) The University of Washington defines Machine Learning as:

“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”

5) Carnegie Mellon University defines Machine Learning as:

“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”

Origin of Machine Learning through the years

Today, algorithms of machine learning enable computers and machines to interact with humans, write and publish sport match reports, autonomously drive cars, and find terrorist suspects as well. Let’s peek through the origins of machine learning and its recent milestones.

1950:
Alan Turing created a ‘Turing Test’ in order to determine if a computer has real intelligence. A computer should fool a human into believing that it is also a human to pass the test.

1952:
The first computer learning program was written by Arthur Samuel. The program was a game of checkers. The more that the IBM computer played the game, the more it improved at the game, as it studied the winning strategies and incorporated those moves into programs.

1957:
The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.

1967:
The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.

1981:
Explanation-Based Learning was introduced, where a computer analyses the training data and creates a general rule which it can follow by discarding the unimportant data.

1990:
The approach towards the work on machine learning changes from a knowledge-driven approach to machine-driven approach. Programs were now created for computers to analyze a large amount of data and obtain conclusions from the results.

1997:
IBM’s Deep Blue beat the world champion in a game of chess.

2006:
Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.

2010:
The Microsoft Kinect was released, which tracked 20 human features at a rate of 30 times per second. This allowed people to interact with computers via gestures and movements.

2011:
IBM’s Watson beat its human competitors at Jeopardy.

2011:
Google Brain was developed. It discovered and categorized objects similar to the way a cat does.

2012:
Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.

2014:
Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.

2015:
Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.

2016:
An artificial intelligence algorithm by Google, AlphaGo, beat a professional player at a Chinese board game Go.

How does Machine Learning work?

The algorithm of machine learning is trained using a training data set so that a model can be created. With the introduction of any new input data to the ML algorithm, a prediction is made based on the model.

The accuracy of the prediction is checked and if the accuracy is acceptable, the ML algorithm is deployed. For cases where accuracy is not acceptable, the Machine Learning algorithm is trained again with supplementary training data set.

There are various other factors and steps involved as well. This is just an example of the process.

Advantages of Machine Learning

  1. It is used in multifold applications such as financial and banking sectors, healthcare, publishing, retail, social media, etc.
  2. Machine learning can handle multi-variety and multi-dimensional data in an uncertain or dynamic environment.
  3. Machine learning algorithms are used by Facebook and Google to push advertisements which are based on the past search behaviour of a user.
  4. In large and complex process environments, Machine Learning has made tools available which provide continuous improvement in quality.
  5. Machine learning has reduced the time cycle and has led to the efficient utilization of resources.
  6. Source programs like Rapidminer have helped increase the usability of algorithms for numerous applications.    

Industries using Machine Learning

Various industries work with Machine Learning technology and have recognized its value. It has helped and continues to help organisations to work in a more effective manner, as well as gain an advantage over their competitors.

  1. Financial services:

Machine Learning technology is used in the financial industry due to two key reasons: to prevent fraud and to identify important insights in data. This helps them in deciding on investment opportunities, that is, helps the investors with the process of trading, as well as identify clients with high-risk profiles.

  1. Government:

Machine learning has various sources of data that can be drawn used for insights. It also helps in detecting fraud and minimizes identity theft.

  1. Health Care:

Machine Learning in the health care sector has introduced wearable devices and sensors that use data to assess a patient’s health in real time, which might lead to improved treatment or diagnosis.

  1. Oil and Gas:

There are numerous use cases for the oil and gas industry, and it continues to expand. A few of the use cases are: finding new energy sources, predicting refinery sensor failure, analyzing minerals in the ground, etc.

  1. Retail:

Websites use Machine Learning to recommend items that you might like to buy based on your purchase history.

What is the future of Machine Learning?

Machine learning has transformed various sectors of industries including retail, healthcare, finance, etc. and continues to do so in other fields as well. Based on the current trends in technology, the following are a few predictions that have been made related to the future of Machine Learning.

  1. Personalization algorithms of Machine Learning offer recommendations to users and attract them to complete certain actions. In future, the personalization algorithms will become more fine-tuned, which will result in more beneficial and successful experiences.
  2. With the increase in demand and usage for Machine Learning, the usage of Robots will increase as well.
  3. Improvements in unsupervised machine learning algorithms are likely to be observed in the coming years. These advancements will help you develop better algorithms, which will result in faster and more accurate machine learning predictions.
  4. Quantum machine learning algorithms hold the potential to transform the field of machine learning. If quantum computers integrate to Machine Learning, it will lead to faster processing of data. This will accelerate the ability to draw insights and synthesize information.

What You Will Learn

1. Statistical Learning

Understand the behavior of data as you build significant models

2. Python for Machine Learning

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

3. Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning.

4. Optimization Techniques

Learn to use optimization techniques to find the minimum error in your machine learning model

5. Machine Learning Algorithms

Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail

6. Dimensionality Reduction

Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction

7. Neural Networks

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

8. Ensemble Learning

Learn to use multiple learning algorithms to obtain better predictive performance

PREREQUISITES

For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.

Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey, and earn an industry-recognized Machine Learning Certification from KnowledgeHut upon successful completion of the Machine Learning course.

3 Months FREE Access to all our E-learning courses when you buy any course with us

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Who Should Attend?

  • If you are interested in the field of machine learning and want to learn essential machine learning algorithms and implement them in real life business problem
  • If you're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis and machine learning

Knowledgehut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Certification Process

Learn Machine Learning in Washington, DC
Machine Learning Algorithms
Machine Learning Engineer Salary in Washington, DC
Machine Learning Conference in Washington, DC
Machine Learning Engineer Jobs in Washington, DC
Machine Learning with Python Washington, DC

Curriculum

Learning Objectives:

In this module, you will visit the basics of statistics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range; and explore data and measures and simple graphics analyses.Through daily life examples, you will understand the basics of probability. Going further, you will learn about marginal probability and its importance with respect to data science. You will also get a grasp on Baye's theorem and conditional probability and learn about alternate and null hypotheses.

Topics:
  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
Hands-on :
Learn to implement statistical operations in Excel.
Learning Objectives:

In this module, you will get a taste of how to start work with data in Python. You will learn how to define variables, sets and conditional statements, the purpose of having 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 in Python. Towards the end of the module, you will learn to visualization data using Python libraries like matplotlib, seaborn and ggplot.

Topics:
  • Python Overview
  • Pandas for pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Matplotlib & Seaborn for Data Visualization
  • Scikit Learn

Hands-on: No hands-on

Learning Objectives :

This module will take you through real-life examples of Machine Learning and how it affects society in multiple ways. You can explore many algorithms and models like Classification, Regression, and Clustering. You will also learn about Supervised vs Unsupervised Learning, and look into how Statistical Modeling 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 & Underfitting

Hands-on: No hands-on

Learning Objectives:

This module gives you an understanding of various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.

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

Hands-on: No hands-on

Learning Objectives:

In this module you will learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies. It covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance.You will be able to grasp the concepts of Linear and Logistic Regression with real-life case studies. Through a case study on KNN Classification, you will learn how KNN can be used for a classification problem. You will further explore Naive Bayesian Classifiers through another case study, and also understand how Support Vector Machines can be used for a classification problem. The module also covers hyper-parameter tuning like regularization and a case study on SVM.

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:
  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
  • This dataset classifies people described by a set of attributes as good or bad credit risks. Using 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 depending on the health metrics.
  • We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
  • Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable and non-biodegradable.
Learning Objectives:

Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering

Topics:
  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study
Hands-on :
In marketing, if you're trying to talk to everybody, you're not reaching anybody.. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns. 
Learning Objectives:

This module will teach you about Decision Trees for regression & classification problems through a real-life case study. You will get  knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index,CHAID.The module covers basic ensemble techniques like averaging, weighted averaging & max-voting. You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting.
Going further, you will learn Random Forest with a real-life case study and learn how it helps avoid overfitting compared to decision trees.You will gain a deep understanding of the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It covers comprehensive techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. Finally, you will examine a case study on PCA/Factor Analysis.

Topics:
  • Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study
Hands-on:
  • Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
  • In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform than a single model.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights &  better modeling.
Learning Objectives: 

This module helps you to understand hands-on implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines. The courseware covers concepts like cold-start problems.You will examine a real life case study on building a Recommendation Engine.

Topics:
  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study
Hands-on:
You do not need a market research team to know what your customers are willing to buy.  Netflix is an example of this, having successfully used recommender system to recommend movies to its viewers. Netflix has estimated, that its recommendation engine is worth a yearly $1 billion.
An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build Recommender System for a Retail Chain to recommend the right products to its users 

Projects

Predict Property Pricing using Linear Regression

With attributes describing various aspects of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.

Classify good and bad customers for banks to decide on granting loans.

This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.

Classify chemicals into 2 classes, biodegradable and non-biodegradable using SVM.

Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set contains 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable

Read More

Cluster teen student into groups for targeted marketing campaigns using Kmeans Clustering.

In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

Read More

Predict quality of Wine

Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note: These were the projects undertaken by students from previous batches.  

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. Venue:  Galvanize 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:

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/salaries/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.

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Faqs

The Course

Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data. Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!

You will:
  • Get advanced knowledge on machine learning techniques using Python
  • Be proficient with frameworks like TensorFlow and Keras

By the end of this course, you would have gained knowledge on the use of machine learning techniques using Python and be able to build applications models. This will help you land lucrative jobs as a Data Scientist.

There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

Yes, KnowledgeHut offers this training online.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Machine Learning experts who have years of industry experience.

Finance Related

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.

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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.