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Description
Transformational advancements in technology in today’s world are making it possible for data scientists to develop machines that think for themselves. Based on complex algorithms that can glean information from data, today’s computers can use neural networks to mimic human brains, and make informed decisions based on the most likely scenarios. The immense possibilities that machine learning can unlock are fascinating, and with data exploding across all fields, it appears that in the near future Machine Learning will be the only viable alternative simply because there is nothing quite like it!
Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world? KnowledgeHut’s comprehensive course will help you go from basic to advanced concepts in Machine Learning using Python, the most popular language in the Data Science space. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each using Python libraries. You will work on real life case studies to get hands-on experience and learn Machine Learning techniques to build predictive models.
A career in Machine Learning is much sought after because it defines and shapes the future. Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey.
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
- Familiarity with statistics
- Elementary programming knowledge
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.
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
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
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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!
- 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 : http://www.knowledgehut.com/refund
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?
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