Top 30 + Best Machine Learning Course Online
(Learn in-demand Machine learning skills and kick start your Machine learning career)
Are you looking for the best machine learning courses online to become a machine learning engineer?
Then you came to the right place.
Machine learning jobs are in extremely high demand. Do you know according to Indeed, Machine learning engineer is in the first place on the best jobs list of 2019.
What is machine learning?
“Using data to answer questions” is the simple answer to this question.
The world is filled with data. Lots and lots of data. Everything from pictures, music, words, spreadsheets, videos, and more.
Machine learning is an application of artificial intelligence (AI).
Machine learning is a process of tools and algorithms to create models to make better and more useful predictions, using our datasets.
The primary aim of ML is to allow computers to think and learn on their own.
Machine learning is an advanced technology and so to learn it, you have basic knowledge of Statistics, Linear Algebra, Calculus, Probability, and Programming Languages.
Here we curated the best online machine learning courses from the top universities and institutions around the world with more than 4.5 ratings out of 5.
Learn and master the required skills and kick start your career in machine learning.
Top 30 best Machine Learning Courses Online
Do you have basic programming skills and want to kickstart your career in the AI & ML?
Looking for the best Machine Learning course online?
Then Post Graduate Program in Artificial Intelligence and Machine Learning from NIT, Warangal is for you.
About the Course:
- This course helps you become an expert in the exciting new world of AI & Machine Learning
- 9 Months online program, 450+ hours of online learning
- 30+ Assignments & 20 projects
- 9 Months online program, 450+ hours of online learning
- Get placement & interview preparation assistance
- Industry networking
What you will learn:
- Python Fundamentals
- Exploratory Data Analysis
- Inferential Statistics
- Supervised Learning
- Regression Algorithm
- Classification Algorithm
- Unsupervised Learning
- Time Series Analysis
- Recommendation Engine
- Deep Learning- TensorFlow, Keras
- Image Classification- CNN, RCNN, GAN
- Text Classification- RNN, LSTM, GRU
- Natural Language Processing- NLTK, Spacy, RASA
- Machine Translation
- Reinforcement Learning
- Python for AI-ML
- Machine Learning
- Mid-Program Project 1
- Deep Learning
- Natural Language Processing
- Sequence Learning
- 3 industry projects (Building a conversational Chatbot, Human Pose Estimation Using Deep Learning, Predictive Model for Auto Insurance)
Udacity Become a Machine Learning Engineer Nanodegree Program
Do you have knowledge of Python and basic machine learning algorithms?
Want to learn advanced machine learning techniques and algorithms and create an end-to-end machine learning product?
Then Udacity Become a Machine Learning Engineer Nanodegree program is for you.
About the Course:
- Content co-created with Kaggle
- Learn the advanced skills you need to become a machine learning engineer.
- Able to deploy machine learning models to a production environment
- Evaluate and update that model according to performance metrics
- Real-world projects
- Technical mentor support and Project feedback from experienced reviewers
- Get personal career coach and career services
- Flexible learning program
- The course takes 3 months (10 hrs/week) to complete
Syllabus:
- Software Engineering Fundamentals
- Machine Learning in Production
- Machine Learning Case Studies
- Machine Learning Capstone
Do you have Python programming knowledge and basic knowledge of probability and statistics?
Want to become a Data Scientists and Machine Learning engineer?
Then Intro to Machine Learning with PyTorch Nano Degree Program from Udemy is for you.
About the Course:
- Foundational machine learning skills that data scientists and machine learning engineers use day-to-day.
- Learn about supervised learning, a common class of methods for model construction.
- Foundations of neural network design and training in PyTorch.
- Learn to implement unsupervised learning methods for different kinds of problem domains.
- Build a strong foundation in Supervised, Unsupervised, and Deep Learning.
- Real-world projects from industry experts
- Technical mentor support
- Personal career coach & career services
- On average, successful students take 3 months to complete this program.
Do you have a solid basis in R and statistics?
Looking for the best machine learning course online?
Then Introduction to Machine Learning from Datacamp is the best course for you.
About the Course:
- This course includes 15 Videos of 6 hours, 81 Exercises
- Learn the true fundamentals of machine learning
- Learn and build a decision tree and to classify unseen observations with k-Nearest Neighbors.
- Different approaches of Clustering, k-means clustering, and hierarchical clustering
- Understand different machine learning models
- Assess the performance of both supervised and unsupervised learning algorithms
- Learn simple linear regression, multi-linear regression, and k-Nearest Neighbors regression.
Are you a working professional and want to change your career into machine learning?
Looking for the best online machine learning course with a job guarantee?
Then the Machine Learning Career Track course is for you.
About the Course:
- The AI/Machine Learning Career Track is a 500-600 hour course.
- Learn job-ready skills like advanced machine learning, natural language processing, or deep learning.
- AI and Machine Learning Engineering Stack
- Data Wrangling at Scale and Statistics for AI
- Foundations of Machine Learning
- A Deep Dive into Deep Learning
- AI Case Study 1: Natural Language Processing
- AI Case Study 2: Computer Vision
- Building and Deploying Large-Scale AI Systems
- 14 real-life projects, including two industry-worthy capstone projects to showcase your skills to employers.
- Personalized career coaching and 1:1 mentorship from industry experts.
- Dedicated help when you’re stuck, and detailed feedback on each project.
- Course completion certificate
- You can complete the course in 6 months, working 15-20 hours per week
Do you have basic knowledge of Python and machine learning algorithms and want to master your skills?
Learn Machine Learning By Building Projects helps you to test your machine learning skills by building real-world machine learning projects across different verticals.
About the Course:
- Learn important concepts, algorithms, and functions of Machine Learning
- Understand the building blocks of Machine Learning
- Learn to implement neural networks
- Project 1: Breast Cancer Detection
- Project 2: Board Game Review
- Project 3: Credit Card Fraud Detection
- Project 4: Stock Market Clustering Project
- Project 5: Diabetes Onset Detection
- Project 6: Markov Models and K-Nearest Neighbor Approaches to Classifying DNA Sequences
- Project 7: Getting Started with Natural Language Processing In Python
- Project 8: Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning
- Project 9: Image Super-Resolution with the SRCNN
- Project 10: Natural Language Processing: Text Classification
- Project 11: K-Means Clustering For Image Analysis
- Project 12: Data Compression & Visualization Using Principal Component Analysis
Udemy Machine Learning Courses
Are you interested in Machine Learning and looking for the best machine learning courses online?
Machine Learning A-Z™: Hands-On Python & R In Data Science helps you learn to create Machine Learning Algorithms in Python and R from two Data Science experts.
About the Course:
- This course includes 44 hours of on-demand video, 75 articles, and 38 downloadable resources with code templates.
- Part 1 - Data Preprocessing
- Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 - Clustering: K-Means, Hierarchical Clustering
- Part 5 - Association Rule Learning: Apriori, Eclat
- Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
- 664,734 students joined and gave 4.5 ratings out of 5.
Are you a developer and want to start a career in Data Science and Machine Learning?
Then A-Z Machine Learning using Azure Machine Learning (AzureML) helps you master the Data Science and Machine Learning Models using Azure ML.
About the Course:
- This course includes 11 hours on-demand video of more than 80 lectures, 4 articles, and 40 downloadable resources
- Learn advanced Data Processing methods
- Statistical Analysis of the data using Azure Machine Learning Modules
- MICE or Multiple Imputation By Chained Equation
- SMOTE or Synthetic Minority Oversampling Technique
- PCA; Principal Component Analysis
- Two class and multiclass classifications
- Logistic Regression, Decision Trees, Linear Regression
- Support Vector Machine (SVM)
- Understanding how to evaluate and score models
- Detailed Explanation of input parameters to the models
- How to choose the best model using Hyperparameter Tuning
- Deploy your models as a web service using Azure Machine Learning Studio
- Cluster Analysis
- K-Means Clustering
- Feature selection using Filter-based as well as Fisher LDA of AzureML Studio
- Recommendation system using one of the most powerful recommenders of Azure Machine Learning
- This course also helps you in preparation for Azure DP-100: Azure Data Scientist Associate exam.
- 14,514 students joined and gave 4.5 ratings out of 5.
Are you interested to get into the most in-demand field of Data Science and Machine Learning?
Then Introduction to Machine Learning for Data Science helps you to understand the basic concepts of how machine learning is used for Data Science.
About the Course:
- Learn Core concepts of Python
- Hands-on running Python! (Interactively, with scripts, and with Jupyter)
- Learn how to use Jupyter Notebooks
- The core concepts of Machine Learning
- Essential Machine Learning and Data Science modules (NumPy, Pandas, Matplotlib, SciPy and Scikit-Learn)
- Understand the basic steps of the Machine Learning workflow throughout
- This course includes 5.5 hours on-demand video, 1 article and 8 downloadable resources
- 36,554 students joined and gave 4.4 ratings out of 5.
Machine Learning, Data Science and Deep Learning with Python
Are you a software developer or programmer and want to enter into the most in-demand data science and machine learning career path?
Then Machine Learning, Data Science and Deep Learning with Python course which is complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks is best for you
About the Course:
- 100 lectures spanning 14 hours of video, and most topics include hands-on Python code examples
- Learn techniques used by real data scientists and machine learning practitioners in the tech industry
- Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Data Visualization in Python with MatPlotLib and Seaborn
- Transfer Learning
- Sentiment analysis
- Image recognition and classification
- K-Means Clustering
- Principal Component Analysis
- Bayesian Methods
- Decision Trees and Random Forests
- Support Vector Machines
- Reinforcement Learning
- Collaborative Filtering
- K-Nearest Neighbor
- Term Frequency / Inverse Document Frequency
- Experimental Design and A/B Tests
- Hyperparameter Tuning
- Machine learning with Apache Spark
Do you have basic knowledge of Machine learning algorithms?
Want to learn the concepts of Support Vector Machines(SVM) and how to use it for practical problems.
Then Machine Learning and AI: Support Vector Machines in Python course is for you.
About the Course:
- Linear SVM derivation
- Hinge loss (and its relation to the Cross-Entropy loss)
- Quadratic programming (and Linear programming review)
- Slack variables
- Lagrangian Duality
- Kernel SVM (nonlinear SVM)
- Polynomial Kernels, Gaussian Kernels, Sigmoid Kernels, and String Kernels
- Learn how to achieve an infinite-dimensional feature expansion
- Projected Gradient Descent
- SMO (Sequential Minimal Optimization)
- RBF Networks (Radial Basis Function Neural Networks)
- Support Vector Regression (SVR)
- Multiclass Classification
- You can build practical machine learning applications for Image recognition, Spam detection, Medical diagnosis, and Regression analysis
- 4,956 students joined and gave 4.6 ratings out of 5.
You are new to Python programming and want to learn Machine Learning, Data Science, and Python in a single course?
Complete Machine Learning and Data Science: Zero to Mastery is the best online machine learning course for beginners.
In this course, you learn Data Science and Machine Learning skills from scratch.
About the Course:
- This course includes 42.5 hours of on-demand video, 50 articles, and 13 downloadable resources
- Learn all the modern skills of a Data Scientist
- Data Exploration and Visualizations
- Neural Networks and Deep Learning
- Model Evaluation and Analysis
- Python 3, Tensorflow 2.0, Numpy, Scikit-Learn
- Data Science and Machine Learning Projects and Workflows
- Data Visualization in Python with MatPlotLib and Seaborn
- Transfer Learning
- Image recognition and classification
- Train/Test and cross-validation
- Supervised Learning: Classification, Regression and Time Series
- Decision Trees and Random Forests
- Using Pandas Data Frames to solve complex tasks
- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras
- Using Kaggle and entering Machine Learning competitions
- How to clean and prepare your data for analysis
- K Nearest Neighbours
- Support Vector Machines
- Regression analysis (Linear Regression / Polynomial Regression)
- How Hadoop, Apache Spark, Kafka, and Apache Flink are used
- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks
- Using GPUs with Google Colab
- Able to Build professional real-world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more.
Working as Data scientists and want to learn how to deploy the machine learning models?
Then this course is for you.
About the Course:
- This course includes 9 hours of on-demand video, 30 articles, and 67 downloadable resources
- Covers every aspect of model deployment.
- The Research Environment
- Understanding Machine Learning Systems
- From Research to Production Code
- Deployment Tooling
- Deployments
- 10,732 students joined and gave 4.5 ratings out of 5.
Are you working as a Javascript developer and want to enter into the most in-demand Machine Learning field?
Then Machine Learning with Javascript course helps you learn machine learning from scratch using Javascript and TensorflowJS with hands-on projects.
About the Course:
- This course includes 17.5 hours of on-demand video, 2 articles and 1 downloadable resource
- Understand the common math and programming techniques used in ML algorithms
- Advanced memory profiling to enhance the performance of your algorithms
- Build apps powered by the powerful Tensorflow JS library
- Develop programs that work either in the browser or with Node JS
- Write clean, easy to understand ML code, no one-name variables or confusing functions
- Learn the basics of Linear Algebra
- Comprehend how to twist common algorithms to fit your unique use cases
- Plot the results of your analysis using a custom-build graphing library
- Learn performance-enhancing strategies that can be applied to any type of Javascript code
- Data loading techniques, both in the browser and Node JS environments
- 19,231 students joined and gave 4.6 ratings out of 5.
Are you a Data science specialist and want to pass AWS Certified Machine Learning – Specialty exam (MLS-C01)?
Then this course is for you.
About the Course:
- This course includes 9.5 hours of on-demand video, a 30-minute quick assessment practice exam, 2 articles, and 1 practice test
- Learn SageMaker, feature engineering, model tuning, and the AWS machine learning ecosystem
- S3 data lakes
- AWS Glue and Glue ETL
- Kinesis data streams, firehose, and video streams
- DynamoDB
- Data Pipelines, AWS Batch, and Step Functions
- Using scikit learn
- Data science basics
- Athena and Quicksight
- Elastic MapReduce (EMR)
- Apache Spark and MLLib
- Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization)
- Deep Learning basics
- Tuning neural networks and avoiding overfitting
- Amazon SageMaker, in-depth
- Regularization techniques
- Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)
- High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
- Security best practices with machine learning on AWS
- For better understanding, you must have Associate-level knowledge of AWS services such as EC2 and machine learning concepts.
- 12,158 students joined and gave 4.5 ratings out of 5.
Are you working as a Data Scientists and want to learn more techniques for feature engineering for machine learning?
Feature Engineering for Machine Learning course helps you transform the variables in your data and build better performing machine learning models
About the Course:
- This course includes 9.5 hours of on-demand video, 19 articles, and 5 downloadable resources
- Impute your missing data
- Encoding your categorical variables
- How to transform your numerical variables so they meet ML model assumptions
- Converting your numerical variables into discrete intervals
- Removing outliers
- Date and time variables
- Learn to work with different time zones
- How to handle mixed variables which contain strings and numbers
- 8,725 students joined and got 4.6 ratings out of 5.
Coursera machine learning courses Online
Are you a software developer and want to learn the skills of the high demand field of data science and machine learning?
Then this online Machine Learning Specialization from the University of Washington helps you to gain experience in major areas of Machine Learning.
About the Course:
- You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
- Build Intelligent Applications. Master machine learning fundamentals in four hands-on courses.
- This specialization course contains 4 Courses 1. Machine Learning Foundations: A Case Study Approach, 2. Machine Learning: Regression, 3. Machine Learning: Classification, 4. Machine Learning: Clustering & Retrieval
- This is 100% online courses
- Most learners can complete the Specialization in about 8 months.
- You will get shareable Certificate
- 111,754 already enrolled and gave 4.7 ratings out of 5
Coursera Machine Learning for All, University of London is one of the best machine learning courses online for beginners.
Are you a non-technical person or a beginner and want to learn the basic concepts of machine learning then this course is for you.
About the Course:
- Artificial intelligence and machine learning techniques
- You will understand the basic of how modern machine learning technologies work
- Data Features
- Learn how data representation affects machine learning
- Machine Learning in Practice
- You will be able to use a non-programming based platform to train a machine learning module using a dataset
- Learn how to test a machine learning project You will be able to explain and predict how data affects the results of machine learning
- Machine Learning Project
- 35,383 already enrolled and gave 4.7 ratings out of 5.
Coursera Introduction to Machine Learning, Duke University helps you learn the concepts of machine learning models.
About the Course:
- Introduction to Machine Learning
- Logistic regression, Multilayered perceptron (MLP)
- Basics of Model Learning
- Learn how to solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.
- Image Analysis with Convolutional Neural Networks
- Recurrent Neural Networks for Natural Language Processing
- The Transformer Network for Natural Language Processing
- Introduction to Reinforcement Learning
- Get hands-on experience in implementing data science models on data sets
- Learn how to implement machine learning algorithms with PyTorch, open-source libraries used by leading tech companies in the machine learning field
- 30,258 already enrolled and given 4.6 ratings out of 5.
Are you interested in machine learning but you don’t have the prerequisite mathematical knowledge to learn machine learning?
For a lot of higher-level courses in Machine Learning and Data Science, You need mathematics.
Coursera Mathematics for Machine Learning Specialization, Imperial College London helps you learn about the prerequisite mathematics for applications in data science and machine learning.
About the Course:
- Learn the basics of mathematics
- Implement mathematical concepts using real-world data
- Understand how orthogonal projections work
- Mathematics for Machine Learning - Linear Algebra: What linear algebra is and how it relates to data, working with vectors and matrices
- Mathematics for Machine Learning - Multivariate Calculus: Learn how to optimize fitting functions to get good fits to data.
- Mathematics for Machine Learning - PCA: Dimensionality Reduction with Principal Component Analysis with Python and NumPy
- Derive PCA from a projection perspective
- Master the mathematical skills to continue your journey and take more advanced courses in machine learning.
- 71,084 already enrolled and given 4.5 ratings out of 5.
Do you want to learn the skills for implementing a machine learning project?
Then this Coursera Machine Learning: Algorithms in the Real World Specialization course helps you to define, train, and maintain a successful machine learning application.
About the Course:
- You should have at least beginner-level background in Python programming
- Introduction to Applied Machine Learning
- Machine Learning Algorithms: Supervised Learning Tip to Tail
- Data for Machine Learning
- Clearly define an ML problem
- Prepare data for effective ML applications
- Survey available data resources and identify potential ML applications
- Optimizing Machine Learning Performance
- Learn the entire process of building a machine learning project.
- Take a business need and turn it into a machine learning application
- 5,047 already enrolled and given 4.6 ratings out of 5.
Building a machine learning project is very difficult if you can’t understand the basic principles.
Coursera Structuring Machine Learning Projects, Andrew Ng helps you build a successful machine learning project. This course is part of Deep Learning Specialization
About the Course:
- Understand how to diagnose errors in a machine learning system
- Be able to prioritize the most promising directions for reducing error
- Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
- Know how to apply end-to-end learning, transfer learning, and multi-task learning
- ML Strategy (1)
- ML Strategy (2)
- 242,763 already enrolled and given 4.8 ratings out of 5.
Are you a business person and want to learn basic of machine learning?
Coursera Machine Learning for Business Professionals, By Google Cloud, helps you build an ML model from scratch for your business.
About the Course:
- What is Machine Learning?
- Employing ML
- Formulate machine learning solutions to real-world problems
- Discovering ML Use Cases
- Identify whether the data you have is sufficient for ML
- Carry a project through various ML phases including training, evaluation, and deployment
- Perform AI responsibly and avoid reinforcing existing bias
- Be successful at ML
- 76,925 already enrolled and given 4.6 ratings out of 5.
Do you have basic knowledge of machine learning and want to learn advanced techniques of machine learning?
Coursera Advanced Machine Learning Specialization helps you gain the hands-on experience of applying advanced machine learning techniques that provide the foundation to the current state-of-the-art in AI.
About the Course:
- You will be able to apply modern machine learning methods in the enterprise.
- Understand the caveats of real-world data and settings.
- Solving a wide variety of real-world problems like image captioning and automatic game playing throughout the course projects
- Introduction to Deep Learning
- Modern neural networks and their applications in computer vision and natural language understanding.
- Fully connected layers, convolutional and recurrent layers
- Learn how to win a Data Science Competition: Learn from Top Kagglers
- Solving predictive modeling competitions efficiently
- Learn how to preprocess the data and generate new features from various sources such as text and images
- Bayesian Methods for Machine Learning
- Understand how to define a probabilistic model and how to make predictions from it
- Practical Reinforcement Learning
- Foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc.
- Deep Learning in Computer Vision
- Image and video recognition, including image classification and annotation
- Natural Language Processing
- Addressing Large Hadron Collider Challenges by Machine Learning
Free Machine Learning Courses Online
Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
About the Course:
- A self-study guide for aspiring machine learning practitioners
- 25 lessons, 15 hours, Lectures from Google researchers, 30+ exercises
- Learn and apply fundamental machine learning concepts with the Crash Course
- Real-world case studies
- Interactive visualizations of algorithms in action
Whether you’re just learning to code or you’re a seasoned machine learning practitioner, then Learn from ML experts at Google helps you develop your skills and advance your projects.
This is a Free machine learning course online from Google.
About the Course:
- Introduction to AI and machine learning
- Examples of what AI and ML can do
- Introduction to machine learning problem framing
- Data sources
- Using AI responsibly
- Accessing the right skillset
- Getting started with machine learning
Data Science: Machine Learning, Harvard University course helps you to learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
About the Course:
- The basics of machine learning
- Learn about training data
- Understand how to use a set of data to discover potentially predictive relationships
- How to perform cross-validation to avoid overtraining
- Several popular machine learning algorithms
- How to build a recommendation system
- What is regularization and why it is useful?
Machine Learning Fundamentals UCSSan Diego helps you to understand machine learning’s role in data-driven modeling, prediction, and decision-making.
About the Course:
- Learn supervised and unsupervised learning algorithms
- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- How to classify images, identify salient topics in a corpus of documents
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets
Machine Learning with Python: A Practical Introduction course helps you learn the basics of Python and get started with supervised and unsupervised learning.
About the Course:
- Basics of machine learning using Python
- Supervised vs. Unsupervised learning
- Learn how statistical modeling relates to machine learning, and do a comparison of each.
- The difference between the two main types of machine learning methods: supervised and unsupervised
- Supervised learning algorithms, including classification and regression
- Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
- How statistical modeling relates to machine learning and how to compare them
- Real-life examples of the different ways machine learning affects society
Are you looking for a free machine learning course to learn the skills to become a machine learning engineer?
Introduction to Machine Learning Course teaches you the end-to-end process of investigating data through a machine learning lens.
About the Course:
- Get the foundation of Machine Learning
- Use Naive Bayes with scikit learn in python.
- Splitting data between training sets and testing sets with scikit learn.
- Calculate the posterior probability and the prior probability of simple distributions.
- Learn the simple intuition behind Support Vector Machines.
- Implement an SVM classifier in SKLearn/scikit-learn.
- Identify how to choose the right kernel for your SVM and learn about RBF and Linear Kernels.
- Code your decision tree in python.
- Learn the formulas for entropy and information gain and how to calculate them.
- Implement a mini-project where you identify the authors in a body of emails using a decision tree in Python.
- Decide how to pick the right Machine Learning Algorithm among K-Means, Adaboost, and Decision Trees.
- Apply your Machine Learning knowledge by looking for patterns in the Enron Email Dataset.
- Understand how continuous supervised learning is different from discrete learning.
- Code a Linear Regression in Python with scikit-learn.
- Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.
- You'll be investigating one of the biggest frauds in American history!
- Remove outliers to improve the quality of your linear regression predictions.
- Apply your learning in a mini-project where you remove the residuals on a real dataset and reimplement your regressor.
- Identify the difference between Unsupervised Learning and Supervised Learning.
- Implement K-Means in Python and Scikit Learn to find the center of clusters.
- Understand how to preprocess data with feature scaling to improve your algorithms.
- Use a min mx scaler in sklearn.
- Student Support Community, Interactive Quizzes
If you’re a tech professional and new to machine learning?
Want to learn how machine learning will change the way you work and how to work with this new technology?
Then Introduction to machine learning in Python course is for you.
About the Course:
- This a free online machine learning course with Python
- Get a clear idea of machine learning
- Learn Common techniques used in this field
- Common supervised learning and unsupervised learning algorithms
- Learn Pandas and NumPy?
- How to use scikit-learn?
- Learn computer science techniques?
- Building basic deep neural networks
Are you interested in Machine learning and looking for a Free machine learning course online? Stanford University offers a free course on machine learning
Machine learning course, Andrew Ng course helps you to understand the techniques and algorithms used in machine learning.
About the Course:
- Learn most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself
- Understand the main learning algorithms used in the software industry
- Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
- Learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
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