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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:
- Learn to implement statistical operations in Excel.
- Get a taste of how to start work with data in Python.
- Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.
- Learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies.
- Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering.
- Learn about Decision Trees for regression & classification problems through a real-life case study.
- Get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID.
- 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
- It is used in multifold applications such as financial and banking sectors, healthcare, publishing, retail, social media, etc.
- Machine learning can handle multi-variety and multi-dimensional data in an uncertain or dynamic environment.
- Machine learning algorithms are used by Facebook and Google to push advertisements which are based on the past search behaviour of a user.
- In large and complex process environments, Machine Learning has made tools available which provide continuous improvement in quality.
- Machine learning has reduced the time cycle and has led to the efficient utilization of resources.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- With the increase in demand and usage for Machine Learning, the usage of Robots will increase as well.
- 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.
- 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
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
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
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
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
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
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
- 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.
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
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.
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.
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
- 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.
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
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.
Meet your instructors
Biswanath Banerjee
TrainerProjects
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
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.
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).
reviews on our popular courses
It’s my time to thank one of my colleagues for referring Knowledgehut for the training. Really it was worth investing in the course. The customer support was very interactive. The trainer took a practical session which is supporting me in my daily work. I learned many things in that session, to be honest, the overall experience was incredible!
Astrid Corduas
Senior Web Administrator
My special thanks to the trainer for his dedication, learned many things from him. I would also thank for the support team for their patience. It is well-organised, great work Knowledgehut team!
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Network Systems Administrator
I am glad to have attended KnowledgeHut’s training program. Really I should thank my friend for referring me here. I was impressed with the trainer, explained advanced concepts deeply with better examples. Everything was well organized. I would like to refer some of their courses to my peers as well.
Rubetta Pai
Front End Developer
I would like to thank KnowledgeHut team for the overall experience. I loved our trainer so much. Trainers at KnowledgeHut are well experienced and really helpful completed the syllabus on time, also helped me with live examples.
Elyssa Taber
IT Manager.
I feel Knowledgehut is one of the best training providers. Our trainer was a very knowledgeable person who cleared all our doubts with the best examples. He was kind and cooperative. The courseware was designed excellently covering all aspects. Initially, I just had a basic knowledge of the subject but now I know each and every aspect clearly and got a good job offer as well. Thanks to Knowledgehut.
Archibold Corduas
Senior Web Administrator
Knowledgehut is known for the best training. I came to know about Knowledgehut through one of my friends. I liked the way they have framed the entire course. During the course, I worked a lot on many projects and learned many things which will help me to enhance my career. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.
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Junior Software Engineer
The course material was designed very well. It was one of the best workshops I have ever seen in my career. Knowledgehut is a great place to learn and earn new skills. The certificate which I have received after my course helped me get a great job offer. Totally, the training session was worth investing.
Hillie Takata
Senior Systems Software Enginee
Trainer really was helpful and completed the syllabus covering each and every concept with examples on time. Knowledgehut also got good customer support to handle people like me.
Sherm Rimbach
Senior Network ArchitectFaqs
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!
- 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.
Have More Questions?
Machine Learning with Python Course in Munich
Machine Learning With Python Training in Munich
Munich is a city humming with high-tech, world-class technology institutions, and industries. It is the third-largest city in Germany and a major center for art, science, finance, education, culture, and more. To find growth prospects in Munich's IT sector, you should enhance your skills with KnowledgeHut's data analysis training using Python in Munich. This training is beneficial for programmers, web masters, software developers, and anyone who is interested in getting a thorough understanding of Python the role it plays in machine learning and data analysis. Python?s ease of use has made it one of the fastest-growing and most preferred programming language. It is one of the most used languages by top programmers and developers. Its simplicity and ease of learning make it popular around the world, which makes KnowledgeHut's data analysis using Python course in Munich popular course for developing professional skills.
A New Alternative
Python has many design features that highlight easy comprehension, easy readability, and clarity of syntax, which makes it a crucial part of data analysis and machine learning. The machine learning training using Python offered by KnowledgeHut is an online training that is structured to help you easily understand the significance of Python packages in machine learning and data analysis. The machine learning using Python course in Munich is a comprehensive course, at the end of which you will get a course completion certification. In this training, you will learn the different aspects of Python packages required for data analysis solutions. Some of these aspects include various types of plots in Matplotlib, image and signal processing in Numpy and Scipy, vectorized string operations in Pandas, and model selection in scikit-learn.
Keeping Ahead of the Curve
The data analysis using Python course in Munich includes hands-on exercises and comprehensive project work on data analysis scenarios to make learning simple and fun. By the end of the training, you will be aware of different techniques to use Python packages, including Pandas, Matplotlib, Numpy, and Scipy for data analysis and modeling. You can use your skills and knowledge of the analytics libraries with different Hadoop ecosystems. You will be aware of the right strategies to use when you are faced with scenarios involving selecting the right data models or reducing data dimensions. The industry experts and certified instructors delivering the training ensure they use interactive sessions to reinforce the concepts they teach in the online classes.
KnowledgeHut Empowers You
You will get access to e-learning material and a copy of the slide deck online. When you register for the course, you can be sure that the experiential training will benefit you in the long run.
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