Best Courses to Learn Machine Learning from Scratch

Do you want to learn Machine Learning from scratch? Here we listed Machine Learning tutorials which will help you learn Machine Learning from scratch, and are suitable for beginners, intermediate learners as well as experts.

A to Z (NLP) Machine Learning Model building and Deployment

What you’ll learn

• Developing the NLP Model for Sentiment analysis and Machine learning deployment on local server using flask and docker.
• Select the most efficient Machine Learning Model,Tune the hyper-parameters and selecting the best model using cross-validation technique
• A quick discussion from the basic in nutshell about DevOps tools like docker, Git and GitLab, Jenkins etc.
• A better understanding about software development and automation in real scenario and concept of end-to-end Integration.

Duration: 3hr 19min
Rating: 3.9 (74 ratings) out of 5
Trainer: Mohammed Rijwan
URL: https://www.udemy.com/course/a-to-z-nlp-machine-learning-model-building-and-deployment/

Mastering Data Science and Machine Learning Fundamentals

What you’ll learn

• Mastering Data Science fundamentals
• Mastering Machine Learning Fundamentals
• How and when to use each Machine Learning model
• Make regression using Linear Regression, SVM, Decsision Trees and Ensemble Modeling
• Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA

Duration: 3hr 19min
Rating: 4.3 (762 ratings) out of 5
Trainer: AI Sciences
URL: https://www.udemy.com/course/mastering-data-science-and-machine-learning-fundamentals/

50 Must Know Concepts, Algorithms in Machine Learning

What you’ll learn

• Combined 50 Modules,Techniques,Algorithms of Machine Learning.
• Introduction to must know concepts in Machine Learning which will help you to prepare for interview
• You will get an idea of complete syllabus in Machine Learning

Duration: 54min
Rating: 4.0 (115 ratings) out of 5
Trainer: TheMachineLearning.Org
URL: https://www.udemy.com/course/must-know-in-machine-learning/

Introduction to Machine Learning for Coders

What you’ll learn

• Introduction to Random Forests
• Random Forest Deep Dive
• Performance, Validation, and Model Interpretation
• Feature Importance. Tree Interpreter
• Extrapolation and RF from Scratch
• Data Products and Live Coding
• RF From Scratch and Gradient Descent
• Gradient Descent and Logistic Regression
• Regularization, Learning Rates, and NLP
• More NLP and Columnar Data
• Embeddings
• Complete Rossmann. Ethical Issues

Duration: around 24 hours 
Trainer: Fast.ai
URL: http://course18.fast.ai/ml

Machine Learning

What you’ll learn

• Supervised learning techniques for regression and classification
• Unsupervised learning techniques for data modeling and analysis
• Probabilistic versus non-probabilistic viewpoints
• Optimization and inference algorithms for model learning

Trainer: John W. Paisley
URL: https://www.edx.org/course/machine-learning

Advanced Machine Learning Specialization

What you’ll learn

• Introduction to Deep Learning
• How to Win Data Science Competitions: Learn from Top Kagglers
• Bayesian Methods for Machine Learning
• Practical Reinforcement Learning
• Deep Learning in Computer Vision
• Natural Language Processing
• Addressing the Large Hadron Collider Challenges by Machine Learning

URL: https://www.coursera.org/specializations/aml

Machine Learning

What you’ll learn

• Understand parametric and non-parametric algorithms, clustering, dimensionality reduction, among other important topics.
• Gain best practices and advice from the instructor.
• Interact with your peers in a community of like-minded learners from all levels of experience.
• Real-world based case studies give you the opportunity to understand how problems are solved on a daily basis.
• The flexible deadline allows you to learn at your convenience.
• Learn to apply learning algorithms to build smart robots, understand text, audio, database mining.

Trainer: Andrew Ng
Rating: 4.9 (130,294 ratings)
URL: https://www.coursera.org/learn/machine-learning

Machine Learning with Python

What you’ll learn

• Regression
• Classification
• Clustering
• Recommender Systems
• Final Project

Trainer: Joseph Santarcangelo, SAEED AGHABOZORGI
Rating: 4.7 (6,920 ratings)
URL: https://www.coursera.org/learn/machine-learning-with-python

Deep Learning Specialization

What you’ll learn

• Learn about convolutional networks, RNNs, BatchNorm, Dropout and more.
• Different techniques using which you can build models to solve real-life problems.
• Real-world case studies in fields such as healthcare, autonomous driving, sign language reading, music generation, and natural language processing are covered.

Trainer: deeplearning.ai
Rating: 4.8 (203,169 ratings)
URL: https://www.coursera.org/specializations/deep-learning

Machine Learning A-Z™: Hands-On Python & R In Data Science

What you’ll learn

• Part 1 – Data Preprocessing
• Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
• 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

Duration: 44 hrs 
Rating: 4.5 (154535 ratings) out of 5 
Trainer: Kirill Eremenko, Hadelin de Ponteves
URL: https://www.udemy.com/course/machinelearning/

Machine Learning Practical: 6 Real-World Applications

What you’ll learn

• How real data science project looks like
• Case Studies in your resume
• How to chain multiple ML algorithms together to achieve the goal
• Most advanced Data Visualization techniques with Seaborn and Matplotlib
• Learn Logistic Regression
• L1 Regularization (Lasso)
• Random Forest Classifier

Duration: 8.5 hrs 
Rating: 4.4 (2,332 ratings) out of 5 
Trainer: Ryan Ahmed, Rony Sulca
URL: https://www.udemy.com/course/machine-learning-practical/

Machine Learning with Javascript

What you’ll learn

• 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
• Pick up the basics of Linear Algebra so you can dramatically speed up your code with matrix-based operations. (Don’t worry, I’ll make the math easy!)
• 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

Duration: 17.5 hrs 
Rating: 4.6 (2,657 ratings) out of 5 
Trainer: Stephen Grider
URL: https://www.udemy.com/course/machine-learning-with-javascript/

A Beginner’s Guide To Machine Learning with Unity

What you’ll learn

• genetic algorithms
• neural networks
• human player captured training sets
• reinforcement learning
• Unity’s ML-Agent plugin
• Tensorflow

Duration: 12.5 hrs
Rating: 4.6 (1,711 ratings) out of 5
Trainer: Penny de Byl
URL: https://www.udemy.com/course/machine-learning-with-unity/