Machine Learning with Python Training in Austin, TX, 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

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

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

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.

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 

Meet your instructors

Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

View Profile

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 Austin, Texas

Machine Learning is a study that uses concepts of Artificial Intelligence to give systems the ability to efficiently learn and improve upon set tasks. It enables them to work without any reprogramming or human intervention. It focuses on computer system development and on the development of programs that can access data on their own, analyse it and deal with it without any human intervention. 

Machine Learning starts with different ways of data observation including direct experience and examples. The programs look for patterns in data and then use these patterns to make better decisions for the future based on the examples and datasets at their disposal.

There exist several methods of Machine Learning. These can all be categorized into two categories as follows:

  • Supervised machine learning algorithms: These are the algorithms that take the learnings from the past data and apply them to the new data made available by making use of labelled examples in order to predict events that will occur in the future. 
    • A dataset is fed into the system which the system then uses to train itself and learn. 
    • This training and learning then gives you the learning algorithm and it can be used to make predictions. 
    • These algorithms can provide results with new inputs after they’ve been trained enough.
  • Unsupervised machine learning algorithms: When the information necessary for the training isn’t labelled properly, unsupervised ML algorithms are used. 
    • Unsupervised learning systems can infer a function to describe hidden structures in unlabeled data.
    • They can easily predict the correct result and can draw inferences from the given datasets to accurately find hidden structures in unlabelled data.

ML is used to deal with large amounts of data, analyse it and solve problems so they can predict the best outcome to a problem. This way humans can come up with solutions without having to understand the problem completely or analysing why a certain approach to the problem does or doesn’t work.

  • It's easy and it works

Machines can work and solve problems faster than humans. If there are a million solutions to a problem, a machine can systematically evaluate all possible options and find the best possible outcome.

  • Being used in a wide range of applications today

Machine Learning has many practical applications and helps businesses save time and money. It lets people work efficiently and every industry ranging from finance to hospitality uses ML. It has indeed become an indispensable part of our society.

Every organization, right from startups to Fortune 500 companies are working tirelessly to collect data that is generated every day so they can use it to study trends and generate profits. Big and small data is rephrasing businesses and technology.

The state of Machine Learning in companies in Austin and in your daily life

Tech users have been using machine learning for years now. Functions like the surge prices on Uber, social media feeds for Facebook and Instagram, and even the detection of financial fraud can now be done using powerful algorithms associated with Machine Learning, with limited human interference. 

Everyone uses one or the other product of Machine Learning with or without their knowledge. That is why it is important for professionals, especially those involved with Information Technology or Data Science to learn Machine Learning, so that they can stay relevant. 

Learning Machine Learning in Austin has many benefits, some are listed below: 

  1. It reels in better job opportunities: 

The technology companies present in Austin generate a large amount of tech-related revenue in Texas. These companies are 3M, Apple, Amazon, AT&T, Adobe, etc. A report published by Tractica reported that services that use Artificial Intelligence had a worth of $1.9 billion in 2016 and this value is predicted to rise to about $19.9 billion by the end of 2025. Every company is now joining the bandwagon of using Machine Learning. Since every company wants to expand their domain in ML and AI, a knowledge in these domains is bound to attract more job opportunities.

  1. Machine Learning engineers earn a pretty penny: 

The worth of a Machine Learning expert is comparable to that of a prospective quarterback at the NFL. The average salary for a Machine Learning Engineer is $129,273 per year in Austin, TX.

  1. Demand for Machine Learning skills is only increasing: 

There are several companies in Austin that are hiring Machine Learning engineers including Forcepoint, eBay, Clockwork solutions, Asuragen, Cubic Corporation, OJO Labs, GE Power, Red Ventures, Spectrum, BlackLocus, UnitedHealth Group, EY, Bun & Bradstreet, Resideo, Novi Labs, etc. There is a large gap between the demand and supply for Machine Learning engineers. That is why the demand for Machine Learning engineers in increasing and so is their salary, a trend that is only expected to increase in the future.

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

In today’s market, data is the largest currency which is why many industries deal with large amounts of data and have realised the importance of data analysis. Companies want to work efficiently and gain an edge over their competitors by gaining insights from data. Industries ranging from financial sector to gas companies to government agencies now work in the field of Machine learning. 

Machine learning is a field that is changing every day due to its open culture. There are a number of certification courses in Austin that will help you learn Machine Learning including:

  1.    General Assembly
  2.    NobleProg
  3.    ONLC Training Centers
  4.    Hartmann Software group
  5.    Great Learning

However, learning Machine Learning will remain effective as long as you are motivated and keep the following in mind:

  • A knowledge of ML helps you learn the skills necessary to deal with practical situations by hands-on learning instead of making you go through academic papers. 
  • You will be able to build your profile as an ML enthusiast by building on projects. These projects are a great way to implement your skills and gain a prospective employer’s attention. 

Below are the steps you can follow:

  1. Structural Plan: Before anything, you need to have a structured plan about the topics you need to familiarize yourself on priority and what you can leave for later. 
  2. Prerequisite: Choose a programming language you are comfortable with and start enhancing your skills in maths and statistics since ML involves a lot of statistical data.
  3. Learning: Start following the plan made in step (1) and start learning. You can refer to any reliable sources online or books. Ensure that you understand the workflow of ML algorithms. 
  4. Implementation: Try to use the algorithm you learn to build projects that use those algorithms. Take part in online competitions like Kaggle and also take datasets from the internet and start solving task problems. 

Keep a track of ML problems and keep solving them to polish your skills. It’ll also enhance your out-of-the-box thinking.

One of the best ways to get started with Machine Learning is to connect with other professionals. Here is a list of Machine Learning meetups in Austin where you can connect with other Machine Learning Engineers:

  1.     Austin Women in Machine Learning and Data Science
  2.     Austin Deep Learning
  3.     Azure Machine Learning
  4.     Austin AI Developers Group
  5.     Austin Big Data AI

The best recommendation to get started on Machine Learning as a beginner includes a 5 step process, which goes as follows:

  • Adjust your mindset:
    • Try to figure out what is holding you back from taking up Machine Learning and completing your targets.
    • Remember that Machine Learning is not as hard as it is believed. 
    • Think of ML as a concept that gives you more to discover the longer you practice it.
    • Look for people who will support you on your journey of learning ML.
  • Pick a process that suits you best: Everyone works differently. So, pick a process that you’re comfortable with.
    • Pick a tool: Find your comfort level with ML concepts and choose a tool accordingly.
    • Beginners could opt for the Weka Workbench
    • Intermediate level learners are recommended to choose the Python Ecosystem
    • Advanced level learners should go for the R Platform
  • Practice on Datasets: There are many datasets that’ll help you work with data collection and manipulation. Practice your own Machine Learning skills with relatively small, installed in memory datasets
  • Build your own portfolio: Add the skills you learn to your portfolio.

In Austin, companies like Amazon, Revionics, Siemens, Arm, KPMG, Smarter Sorting, Resideo, Whole Foods Market, Cerebri AI, DELL, Macmillan Learning, Cisco Careers, Oracle, CDK Global, CCC Information Services Inc., etc. are looking for Machine Learning professionals with suitable experience that will help the organization make crucial marketing decisions.

In order to thoroughly understand the concepts of Machine Learning and to develop successful Machine Learning projects, it is important to know the following:

  • Programming languages: Someone who decides to learn Machine Learning should be able to operate programming languages like Python, Java, Scala, etc, efficiently. A knowledge of these languages helps the learner understand ML better. It is also helpful to learn data formats and how to process data to make it compatible to ML algorithms. 
  • Database skills: Knowledge of MySQL is required to properly understand Machine Learning concepts. While they learn these concepts, learners will have to use many different data sets from various data sources so that they can convert it into a format that can be read by ML frameworks.
  • Machine Learning visualization tools: There are many tools in ML that can be used to visualise data. A basic understanding of these tools enables a data scientist to apply these ML skills to real life. 
  • Knowledge of Machine learning frameworks: To design a Machine Learning model, many mathematical and statistical algorithms are used. Learning one or more of these frameworks like Apache Spark ML, TensorFlow etc, enhances your understanding of Machine Learning concepts.
  • Mathematical skills: Machine learning models are formed when data is processed and analysed using mathematical algorithms. Below are some maths concepts that help you better implement Machine Learning concepts: 
    • Optimization
    • Linear algebra
    • Calculus of variations
    • Probability theory
    • Calculus
    • Bayesian Modeling
    • Fitting of a distribution
    • Probability Distributions
    • Hypothesis Testing
    • Regression and Time Series
    • Mathematical statistics
    • Statistics and Probability
    • Differential equations
    • Graph theory

If you want your ML project to be executed successfully, we have compiled the steps for the same below:

  1. Gathering data: To apply your ML skills effectively, you should be able to collect the right data for your project. The quality and quantity of the data is directly proportional to how well your model will perform.
  2. Cleaning and preparing data: Data that is gathered is often raw and thus needs to be cleaned by correcting the missing data so that it can be injected into the model. Once the data is converted into what the model is expecting, it needs to be divided into 2 parts: training data and testing data.  
  3. Visualize the data: Visualisation is the final step that shows the prepared data and co-relates the variables. It helps in understanding the complex data so a model can be chosen properly. 
  4. Choosing the correct model: Once the data has been visualised, you can choose the model that is best suited to deal with it. 
  5. Train and test: We have our prepared data ready to be injected into our chosen model. Since we had divided the data into training and testing data, we train the model using the former and its accuracy is tested using the test data. 
  6. Adjust parameters: After examining the accuracy of your mode, you’ll need to fine tune your parameters.

It is important for all learners to study algorithms since they form an integral part of Machine Learning. Here is how you can include ML algorithms: 

  1. List the various Machine Learning algorithms: Each algorithm is unique and important but you must choose a few that you want to start your Machine Learning journey with. Make a list of all the algorithms you want to learn and place them under categories. 
  2. Apply the Machine Learning algorithms that you listed down: Machine Learning algorithms don’t exist in isolation which means that learning them isn’t enough, you must also know how to practically apply what you learn to data sets. Also practice Applied Machine Learning and start understanding ML algorithms like Support Vector Machines. Applying these to many data sets will build your confidence.
  3. Describe these Machine Learning algorithms: The next step is to better understand Machine Learning algorithms while also exploring the information already available about them. This will help you build a good description of these algorithms. Add any information you get to these descriptions and you’ll learn new things in your ML study. 
  4. Implement Machine Learning Algorithms: The best way to understand Machine Learning algorithms is to implement them yourself and understand the micro decisions involved in the implementation. It also helps you understand mathematical extensions involved.
  5. Experiment on Machine Learning Algorithms: Once you’ve understood a Machine Learning algorithm, you’ll need to understand its behaviour so you can tailor it to suit your future problem needs. You can start experimenting with the algorithm and use standardized data sets, and study the functioning of algorithms.

Machine Learning Algorithms

The K Nearest Neighbours algorithm is a simple Machine Learning algorithm. We can use the K Nearest Neighbour algorithm when we are dealing with a multiclass dataset to be worked on so we can predict the class of a given data point:

  • ‘K’ is the number of training samples that are closest to the new data point that needs classification. This the value of the nearest neighbour classification that needs to be defined.
  • K-nearest neighbor classifiers possess a fixed user-defined constant for the number of neighbors which have to be determined.
  • These algorithms work on the concept of radius based classification. The samples are identified and classified under a fixed radius depending on the density of the data points that surround it. This fixed radius is a metric measure of the distances and is the Euclidean distance between the points.
  • These methods that use the classifications of the neighbours are termed as the non-generalizing Machine Learning methods. This is because these methods remember all the data that is fed into it. 
  • Classification is then performed as a result of a majority vote conducted among the nearest neighbours of the unknown sample.

The K Nearest Neighbour algorithm is the simplest when compared to other machine learning algorithms. The algorithm is preferred because it is very effective when it comes to regression and classification problems like character recognition or image analysis.

The answer to this question depends upon what you intend to do with Machine Learning. 

  • There are many online courses that teach you Machine Learning algorithms without any prior knowledge of algorithms in case you just want to learn existing ML algorithms.
  • However, if you want to delve deeper and innovate using Machine Learning, you need to have prior knowledge of the uses of some algorithms. Since you will be involved in the development and creation of a new Machine Learning algorithms, you’ll need the knowledge required to adapt, design, and innovate with Machine Learning.

Machine Learning Algorithms can be classified basically into the following 3 types - 

  1. Supervised Learning: Linear Regression, Logistic Regression, Classification and Regression Trees (CART), Naïve Bayes, K-Nearest Neighbours 
  2. Unsupervised Learning: Apriori, K-Means, Principal Component Analysis (PCA) 
  3. Ensemble Learning: Bagging, Boosting

  • Supervised Learning: This uses historical data (classified) to learn mapping functions from input variable (X) to the output variable (Y). Examples of such include
    • Linear Regression - The relationship between the input variables (x) and output variable (y) is expressed as an equation of the form y = a + bx
    • Logistic Regression - Logistic Regression is similar to the linear regression model; except that in case of logistic regression you get probabilistic values. To force this probability into a binary classification, you then need to apply a Transformation function.
    • CART - Classification and Regression Trees (CART) is an implementation of Decision Trees. This algorithm predicts the results based on defined nodes and branches by considering the possibility of each outcome. Each non-terminal node is a single input variable (x). The many outcomes that can be generated with regards to that variable can be seen using the splitting point of the node and the following leaf node represents the output variable (y).
    • Naïve Bayes - This algorithm can predict the possibility of an outcome based on the basic value of another variable. It works like the Bayes theorem but it's considered to be “naive” since it works on the assumption that all variables are independent. 
    • K-Nearest Neighbours - This algorithm first considers all the data given and assigns “k” a predefined value. After that, it charts the “k nearest instances” of the dataset value and then either averages them for the output (regression) or finds a mode of the averages (frequent class problem).  
  • Unsupervised Learning: In these cases, only the input variables are given. Then the given data is analysed to reveal possible associations. Examples of such algorithms include the following -
    • Apriori -  This algorithm is used in various databases containing transactions to identify frequent associations in two terms that occur together and it can be used to predict future patterns as well. 
    • K-Means - This algorithm groups similar data into clusters and joins each data point to a centroid that it “assumes”. It then performs actions to ensure that the distance between the data point and the centroid is the least.
    • PCA -  Principal Component Analysis (PCA) makes the data visualization easier and this is done by reducing the variables. It uses a new coordinate system to map the maximum variances of each point. 
  • Ensemble Learning: A group of learners are more likely to perform better than singular learners. These algorithms combine the results of every learner and analyse them to obtain a combined representation of the actual outcome. Following are some examples of such algorithms:
    • Bagging - This algorithm generated multiple datasets (based on the original one), and then model the same algorithm to produce variant outputs and these are then compiled to get a real outcome.
    • Boosting - This algorithm is similar to the one above but it uses sequencing instead of parallels. Each dataset is created by learning from the previous one’s error. 

The simplest of machine learning algorithms can be used to solve the simplest ML problems (simple recognition). We have selected the algorithm based on the following criteria:

  • Easy to understand.
  • Easy to implement and understand the underlying principles.
  • Train and test the data faster and with lesser resources when compared to high-level algorithms. 

Now we introduce you to the algorithm itself which is a stepping stone in your journey towards mastery in ML: k-nearest neighbor algorithm. We have listed some of the reasons why we chose kNN as the simplest ML algorithm and why it is popularly used for solving some of the basic, but important, real-life problems:

  • One of the simplest supervised learning algorithms and best suited for beginners is the k-nearest neighbor algorithm.
  • It can be used for regression as well.
  • The classification is based on the similarity measure. It is non-parametric.
  • Labeled data (supervised learning) is used for the training phase. The algorithm aims at predicting a class for the object based on its k nearest surroundings where k is the number of neighbors.
  • Some practical and real-life examples where KNN is used are:
    • Searching for documents containing similar topics.
    • Used to detect patterns in credit card usage.
    • Vehicular number plate recognition.

ML is the most popular prospect in the tech world right now and it thus, has loads of tools, algorithms, and models that you can choose from. You need to remember the points below while selecting the algorithm that works for you:

  • Understanding your data: Firstly, you must consider what kind of data you’re going to apply the algorithm to. So, understand your data before you choose the algorithm:
    • Plot graphs for data visualization. 
    • Try correlate data that have strong relationships.
    • Clean your data since there can be some data that is either missing or bad.
    • Prepare your data by feature engineering so it can be injected into a model.  
  • Get the intuition about the task: Many times ML is needed because we don’t understand the actual aim of the task and thus, need ML to solve the problem. After that, you need to choose the model that will work the best. There are 4 types: 
    • Supervised learning
    • Unsupervised learning
    • Semi-supervised learning
    • Reinforcement learning
  • Understand your constraints: We can’t just choose the best tools and algorithms blindly. We need to understand that some of the high-end models work on expensive machines. Constraints can involve hardware or software as well.
    • The amount of data that we can store for training or testing can be limited by the data storage available. 
    • A self-learning ML enthusiast won’t run a high-level algorithm which needs high computational power on a low-end machine. Thus, hardware constraints also need to be considered.
    • You must check to see if you have enough time to run long duration training phases or not.
  • Find available algorithms: Only after going through the above three phases, we can check which algorithms adhere to our requirements, and constraints and finally go and implement it! 

Follow these steps to implement the ML algorithms:

  1. Select a programming language: The programming language you choose will affect the standard libraries you’ll have access to and the APIs that you will use for your implementation. 
  2. Select the algorithm that you wish to implement: Once you choose your programming language you can move to choosing the algorithm you want to implement from scratch. You need to decide everything about the algorithm from the type to the specific implementations you expect. 
  3. Select the problem you wish to work upon: Next select the canonical problem set that you want to use to test and validate the efficiency of your algorithm implementation. 
  4. Research the algorithm that you wish to implement: Go through blogs, books, academic research, etc.that contain information about the algorithm you’ve chosen and its implementation. Considering many different descriptions of your algorithm is important to gain a proper perspective of its uses and implementation methodologies.
  5. Undertake unit testing: Run tests for every function of your algorithm. You need to consider the test driven development of your algorithm in the initial phases. This helps you understand what you should expect and the purpose of each unit of your algorithm’s code.

Other than the basic concepts of Machine Learning, here are few topics a learner should focus on: 

  • Decision Trees: A Decision tree is a type of a supervised learning algorithm that is used for classification problems. They are useful for splitting since they decide what features and conditions need to be chosen. Advantages of decision tree methods:
    • They are relatively simplistic
    • They are easier to understand, visualize and interpret
    • You can use them to perform feature selection and variable screening
    • They are not affected by non linear relationships between parameters
    • Decision trees require minimal effort from the user when it comes to data preparation
    • Decision trees can handle and analyze both categorical and numerical data
    • Decision trees are also able to handle problems that require multiple outputs
  • Support Vector Machines: Support Vector Machines are a type of classification methodology that is comparatively more accurate while dealing with classification problems. They can be used for regression problems also. Some of the benefits of a Support Vector Machine include the following:
    • Owing to the nature of convex optimizations, Support Vector Machines provide optimal solutions. They provide global minimum solutions to guarantee optimality.
    • They are useful in both, Linearly Separable (also known as Hard margin) as well as Non-linearly separable (also known as Soft Margin) data.
    • Support Vector Machines provide a ‘Kernel Trick’ which reduced the complexity of feature mapping which used to be a huge burden earlier.
  • Naive Bayes: The Naive Bayes algorithm is a classification technique that is based on Bayes' theorem. It assumes that different predictors are interdependent. The Naive Bayes algorithm assumes that a particular presence in some data is completely unrelated to any other presence in the same data sample. Below are listed some advantages of the Naive Bayes algorithm:
    • It is very simplistic technique of classification - all that the system is doing is performing a bunch of counts. 
    • It requires less training data as compared to other techniques used for classification.
    • It is a highly scalable classification technique.
    • It converges quicker than other traditional discriminative models.
  • Random Forest algorithm: The Random Forest algorithm is a supervised learning algorithm. It creates a collection or forest of decision trees and completely randomizes the inputs so that the system doesn’t identify any other patterns in the input. It is a collection of random decision trees that use the bagging method. 

Following are the advantages of the Random Forest algorithm:

    • A Random forest may be used for regression and classification problems. 
    • It is easier to view what importance a random forest gives to its input features.
    • It is very easy to use and handy algorithm.
    • The number of hyper parameters included in a random forest are not high and are relatively easy to understand.
    • A Random Forest produces a reliable prediction result.

Machine Learning Engineer Salary in Austin, Texas

The median salary of a Machine Learning Engineer in Austin, TX is $1,20,000/yr. The range differs from $72,800 to as high as $1,70,000.

The average salary of a machine learning engineer in Austin, TX is $1,16,000/yr whereas, in Portland, it’s $1,09,000/yr.

The United States is the birthplace of tech giants such as Google, Facebook, Amazon, Microsoft and others. These companies are accountable for more than the majority of the 34% rise in machine learning patents developed in recent years. These companies have understood the importance of machine learning and the promise it carries which is precisely the reason behind the huge demand for Machine learning engineers in Austin.

Following are the benefits of landing into the ‘dream job’ of the engineering graduates - 

  • Career growth - Machine learning engineering sector is 9.5 times the size of what it was just 5 years back. This is barely the beginning and there is so much left to be explored. It is perhaps this curiosity and opportunity to grow that attracts the skilled professionals to this field.
  • High Salary - This one is the obvious one yet one of the most important factors when deciding a career.

The very fact that ML engineering is believed to outrun data scientist which is hailed as the sexiest job of the 21st century is enough to talk about the endless promise, potential and scope that this job holds. But more than that it is the opportunity it presents. AI and ML are practically the gateways to future technology. Moreover, the acknowledgment and appreciation that this career brings is also very glorious.

Although there are quite many companies offering jobs to Machine Learning Engineers in Austin, following are the prominent companies - 

  • Apple
  • Telenav
  • Utilant LLC
  • Memorial Sloan-Kettering
  • Microsoft
  • CognitiveScale

Machine Learning Conference in Austin, Texas

S.NoConference NameDate

Venue

1.The Business of Data ScienceJuly 30-31, 2019AT&T Executive Education and Conference Center, 1900 University Ave, Austin, TX 78705
2.

5th Annual Data Center Conference

September 24-25, 2019

Brazos Hall, 204 East 4th Street, Austin, TX 78701

3.

Developer Week

November 5-7, 2019

Palmer Events Center, 900 Barton Springs Road, Austin, TX 78704

4.

Data Day Texas 2020

January 25th, 2020

AT&T Executive Education & Conference Center, 1900 University Avenue, Austin, TX 78705

5.

Data Science Salon

February 20-21, 2020

Austin, Texas

  1. The Business of Data Science, Austin
    1. About the conference: The aim of this conference is to teach business leaders about the basics of data science, artificial intelligence and machine learning while imparting them various ways of using it to their advantage in their organizations.
    2. Event Date: July 30-31, 2019
    3. Venue: AT&T Executive Education and Conference Center, 1900 University Ave, Austin, TX 78705
    4. Days of Program: 2
    5. Timings: 9 am onwards
    6. Registration cost: $1,725 – $2,190
  1. 5th Annual Data Center Conference, Austin
    1. About the conference: The conference aims to bring the most relevant thought leaders in the data centre, machine learning industry under one roof, in order to collaborate, innovate, and motivate.
    2. Event Date: September 24-25, 2019
    3. Venue: Brazos Hall, 204 East 4th Street, Austin, TX 78701
    4. Days of Program: 2
    5. Timings: 8 am onwards
    6. Purpose: Discuss what they are doing to support technological developments that are changing the world.
    7. Registration cost: $0 - $800
  1. Developer Week, Austin
    1. About the conference: Join 1,500+ developers, tech executives, and entrepreneurs and discover the latest in App Development, VR Dev, FinTech Dev, and Machine Learning.
    2. Event Date: November 5-7, 2019
    3. Venue: Palmer Events Center, 900 Barton Springs Road, Austin, TX 78704
    4. Days of Program: 3
    5. Timings: 8 am onwards
    6. Purpose: Showcase of workshops and exhibitors who are revolutionizing Artificial Intelligence, Machine Learning and dozens of other topics.
    7. Registration cost: $395 – $695
    8. Who are the major sponsors: The Home Depot
  1. Data Day Texas 2020, Austin
    1. About the conference:Known for its uniquely Austin experience, this conference has continued to motivate developers with innovative ideas and impractical approaches to machine learning for the past 10 years. 
    2. Event Date: January 25th, 2020
    3. Venue: AT&T Executive Education and Conference Center, 1900 University Ave, Austin, TX 78705
    4. Days of Program: 1
    5. Timings: 8 am to 8 pm
    6. Purpose: Entering its 10 year in 2020, Data Day Texas highlights the latest in data science with a focus on artificial intelligence and machine learning.
    7. Registration cost: $245 – $495
    8. Who are the major sponsors: Global Data Geeks, Geekaustin.
  1. Data Science Salon, Austin

    1. About the conference: Get face to face with the powerful decision makers in data science and learn about machine learning and AI with the best in the business. 
    2. Event Date: February 20-21, 2020
    3. Venue: Austin, Texas.
    4. Days of Program: 2
    5. Timings: 8 am onwards
    6. Purpose: The flagship conference attempts to bring together practitioners under one roof to motivate and help each other with best ideas and solutions to follow. It covers all major applications of AI and Machine Learning.
    7. How many speakers: 50+
    8. Registration cost: $175 – $595
    9. Who are the major sponsors: Opera Solutions
S.NoConference NameDateVenue
1.Data Day Texas 2017January 14, 2017AT&T Executive Education and Conference Center, 1900 University Avenue, Austin, TX 78705
2.

AnacondaCON 2018

April 8-11, 2018
JW Marriott,110 E 2nd St. Austin, Texas
3.TEXATA Data Analytics Summit
October 19, 2018
AT&T Hotel and Conference Center, 1900 University Avenue, Zlotnik Ballroom (Level M1), Austin, TX 78705
4.Developer Week 2018
November 6-8, 2018
Palmer Events Center, 900 Barton Springs Road, Austin, TX 78704
5.KNIME Fall Summit 2018

November 6-9, 2018

AT&T Executive Education and Conference Center, 1900 University Avenue, Austin, Texas 78705

  1. Data Day Texas 2017, Austin
    1. Event Date: January 14, 2017 
    2. Venue: AT&T Executive Education and Conference Center, 1900 University Ave, Austin, TX 78705
    3. Days of Program: 1
    4. Timings: 8 am onwards
    5. Purpose: The annual conference brought developers face to face to promote ideas on machine learning and artificial intelligence.
    6. Registration cost: $345
    7. Who were the major sponsors: Geekaustin
  1. AnacondaCON 2018, Austin
    1. Event Date: April 8-11, 2018
    2. Venue: JW Marriott,110 E 2nd St. Austin, Texas
    3. Days of Program: 4
    4. Purpose: Thought leaders shared how they used Artificial Intelligence and Machine Learning to solve the issues they face in their fields and innovate further. 
    5. Who were the major sponsors: Anaconda
  1. TEXATA Data Analytics Summit, Austin
    1. Event Date: October 19, 2018
    2. Venue: AT&T Executive Education and Conference Center, 1900 University Ave, Austin, TX 78705
    1. Days of Program: 1
    2. Timings: 8:30 am to 5pm
    3. Purpose: This event focused especially on the developments in data science and machine learning that have happened in the state of Texas, proving to be an excellent opportunity for local developers to indulge and learn.
    4. Registration cost: $100 – $600
    5. Who were the major sponsors: Cisco, IBM.
  1. Developer Week 2018, Austin
    1. Event Date: November 6-8, 2018
    2. Venue: Palmer Events Center, 900 Barton Springs Road, Austin, TX 78704
    3. Days of Program: 3
    4. Timings: 6 pm onwards
    5. Purpose: This was the largest developer event in the South USA with the focus on workshops and conferences on machine learning and data science for local developers.
    6. How many speakers: 100+
    7. Registration cost: $200
    8. Who were the major sponsors: The Home Depot
  1. KNIME Fall Summit 2018, Austin

    1. Event Date: November 6-9, 2018
    2. Venue: AT&T Executive Education and Conference Center, 1900 University Ave, Austin, TX 78705
    3. Days of Program: 4
    4. Purpose: It covered topics of data science and machine learning right from the beginning to the core and complex ones.
    5. Who were the major sponsors: KNIME

Machine Learning Engineer Jobs in Austin, Texas

Machine Learning is a vast field. As a machine learning engineer, you will have to take on the following responsibilities:

  • Design and Development of Machine Learning and Deep Learning system
  • Running experiments and tests
  • Implementing ML algorithms
  • Performing statistical Analysis
  • Extending ML frameworks and libraries
  • Researching and implementing ML tools and algorithms

In Austin, there are several openings for machine learning engineers in public as well as private enterprises. From small startups to big corporations, machine learning engineers are needed everywhere. You just need to figure out which industry domain you want to work on and find a job that suits you best.

In Austin, the following companies are looking for Machine Learning Engineer:

  • Amazon.com Services, Inc.
  • Revionics
  • Siemens
  • Cerebri AI
  • CCC Information Services Inc.

The following ML jobs are in demand right now:

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

As a machine learning engineer, you can network with other fellow professionals through one of the following:

  • Online platforms like LinkedIn
  • Social gatherings like meetups
  • Machine Learning conferences

Machine Learning with Python Austin, Texas

Here's how you can get started using Python for Machine Learning:

  1. Believe that you can learn and apply Machine Learning Concepts.
  2. Download and install the Python SciPy Kit for Machine learning and install all useful packages.
  3. Take a tour of the tool in order to get an idea of all the functions and their uses.
  4. Load a dataset and make use of statistical summaries and data visualization to understand its structure and workings.
  5. Find some popular datasets and practice on them to better understand the concepts. 
  6. Start small and work your way to bigger and more complicated projects.
  7. Gathering all this knowledge will eventually give you the confidence of slowly starting to apply Python for Machine Learning Projects.

Thanks to the large and diverse open source community of Python there are several many useful libraries:

  • Scikit-learn: Primarily for data mining, data analysis and in data science as well.
  • Numpy: Useful for N-dimensional arrays because of its high performance.  
  • Pandas: Useful for high-level data structures and incredibly useful for data extraction and preparation.
  • Matplotlib: We usually need to plot a graph for ML problems. Matplotlib helps with such data visualization needs, like 2D graph plotting..
  • TensorFlow: This is a library created by Google which is the ideal choice if you’re working with deep learning since it uses multi-layered nodes which facilitate quick training, set-up, and can deploy artificial neural networks.

The following are some tips to help you learn basic Python skills:

  1. Consistency is Key: Code every day. Consistency is very important when you are learning a new programming language. You need to commit to it and code every day. Muscle memory actually plays a very important role in programming. Smart small by coding for about 25 minutes each day and keep increasing your efforts. It seems daunting but it’s worth it.
  2. Write it out: Before you’ve become a programmer and regret not having taken notes in the beginning, let us tell you, you should! Studies have proven that writing down something leads to long-term retention. This is beneficial to programmers who are learning Python and want to become full-time developers. Another tip to keep in mind is to write down your code on paper before putting it into a system. 
  3. Go interactive!: The interactive Python shell is one of the best learning tools, irrespective of whether it’s your first time writing code or you’re learning about Python data structures like lists, dictionaries, strings, etc., or debugging an application. To initialize the Python shell, open your terminal and type in Python or Python 3 into the command line and press Enter. 
  4. Assume the role of a Bug Bounty Hunter: With programming, it's inevitable that you’ll encounter bugs. Sitting down and solving these bugs on your own will help you improve your Python programming skills. Don’t be frustrated by the bugs and instead take up the challenge and become a Bug Bounty Hunter!
  5. Surround yourself with other people who are learning: Coding actually brings out the best results when it is done in a collaborative manner. Surround yourself with other people who are learning Python too because it motivates you to keep going and you can also get helpful tips as you work. 
  6. Opt for Pair programming: Pair programming in a technique in which two developers work on a particular piece of work and code together. One programmer acts as the Driver, while the other acts as a Navigator. The driver is the one who actually writes the code and the Navigator guides the entire process and is the developer. The developer also gives 

feedback and checks if the code is accurate while its written. Both the programmers learn mutually and also get introduced to different ways of thinking and fresh perspectives.  

Python is a vast and open-source community and thus, has tons of libraries that you can explore. We have compiled a list of the best Python libraries for you depending upon their ease of implementation, performance, etc.: 

  • Scikit-learn: Used majorly for data mining, data analysis and in data science as well.
  • SciPy: Contains packages for Mathematics, engineering, and science (manipulation).
  • Numpy: Provides much more than just efficiency - free and fast vector and matrix operations.
  • Keras: When one thinks Neural network, one goes to get the help of Keras.
  • TensorFlow: It uses multi-layered nodes. Basically allows faster training, set-up, and deployment of artificial neural networks. 
  • Pandas: Is very useful when you want to extract or prepare data and provides high-level data structures. 
  • Matplotlib: It helps in 2D graphical plotting for data visualization. 
  • Pytorch: The ideal library for NLP.

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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.

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 Austin, TX

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