Machine Learning Course With Placement And Industry Trends

With strong roots in statistics, Machine Learning is becoming one of the most exciting and fast-paced computer science fields. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent.

Attempt free GK Mock Test! Download Entri App!

Chatbots, spam filtering, ad serving, search engines, and fraud detection are among just a few examples of how machine learning models underpin everyday life. Machine learning lets us find patterns and create mathematical models for things that would sometimes be impossible for humans to do.

Important Machine Learning courses 

Machine Learning

This is the course for which all other machine learning courses are judged.

The course uses the open-source programming language Octave instead of Python or R for the assignments. This might be a deal-breaker for some, but Octave is a simple way to learn the fundamentals of ML if you’re a complete beginner.

Grab Latest Study Materials! Register Here!

Topics to understand in this course

• Linear Regression with One Variable
• Linear Algebra Review
• Linear Regression with Multiple Variables
• Octave/Matlab Tutorial
• Logistic Regression
• Regularization
• Neural Networks: Representation
• Neural Networks: Learning
• Advice for Applying Machine Learning
• Machine Learning System Design
• Support Vector Machines
• Dimensionality Reduction
• Anomaly Detection
• Recommender Systems
• Large Scale Machine Learning
• Application Example: Photo OCR

This is undoubtedly the best course to start with a newcomer.

Deep Learning Specialization — Coursera

This specialization is a more advanced course series for anyone interested in learning about neural networks and Deep Learning, and how they solve many problems.

Topics to understand:

1. Neural Networks and Deep Learning
   • Introduction to Deep Learning
   • Neural Network Basics
   • Shallow Neural Networks
   • Deep Neural Networks

1. Improving Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
2. Structuring Machine Learning Projects
3. Convolutional Neural Networks
4. Sequence Models

To understand the algorithms presented in this course, you should already be familiar with Linear Algebra and machine learning in general.

Machine Learning Crash Course — Google AI

Topics to understand:

1. Linear and Logistic Regression
2. Classification
3. Training and loss
4. Reducing Loss – gradient descent, learning rates
5. TensorFlow
6. Overfitting
7. Training sets, splitting, and validation
8. Feature Engineering and cleaning data
9. Feature Crosses
10. Regularization – L1 and L2, Lambda
11. Model performance metrics
12. Neural Networks – single and multi-class
13. Embeddings
14. ML Engineering

This is the best option in this list if you have tinkered with ML but are looking to cover all your bases. The course discusses many nuances of machine learning that may otherwise take hundreds of hours to learn serendipitously.

Attempt free GK Mock Test! Download Entri App!

Machine Learning with Python – Entri

Another beginner course, but this one focuses solely on the most fundamental machine learning algorithms. This course uses Python and is somewhat lighter on the mathematics behind the algorithms.

Topics to understand:

• Intro to Machine Learning
• Regression
• Classification
• Clustering
• Recommender Systems
• Final Project

Advanced Machine Learning Specialization – Entri

This is another advanced series of courses that casts a very wide net. If you are interested in covering as many machine learning techniques as possible, this Specialization is the key to a balanced and extensive online curriculum.

Topics to understand:

1. Introduction to Deep Learning
   • Intro to Optimization
   • Intro to Neural Networks
   • Deep Learning for Images
   • Unsupervised Representation Learning
   • Deep Learning for Sequences
   • Final Project

1. How to Win Data Science Competitions: Learn from Top Kagglers
2. Bayesian Methods for Machine Learning
3. Practical Reinforcement Learning
4. Deep Learning in Computer Vision
5. Natural Language Processing
6. Addressing the Large Hadron Collider Challenges by Machine Learning

Machine Learning — Edx

This is an advanced course with the highest math prerequisite out of any other course on this list. The course has interesting programming assignments in either Python or Octave, but the course doesn’t teach either language.

One of the biggest differences with this course is the coverage of the probabilistic approach to machine learning. If you’ve been interested in reading a textbook, like Machine Learning: A Probabilistic Perspective — which is one of the most recommended data science books in Master’s programs — then this course would be a fantastic complement.

Grab Latest Study Materials! Register Here!

Topic to understand:

• Maximum Likelihood Estimation, Linear Regression, Least Squares
• Ridge Regression, Bias-Variance, Bayes Rule, Maximum a Posteriori Inference
• Nearest Neighbor Classification, Bayes Classifiers, Linear Classifiers, Perceptron
• Logistic Regression, Laplace Approximation, Kernel Methods, Gaussian Processes
• Maximum Margin, Support Vector Machines (SVM), Trees, Random Forests, Boosting
• Clustering, K-Means, EM Algorithm, Missing Data
• Mixtures of Gaussians, Matrix Factorization
• Non-Negative Matrix Factorization, Latent Factor Models, PCA and Variations
• Markov Models, Hidden Markov Models
• Continuous State-space Models, Association Analysis
• Model Selection, Next Steps

Introduction to Machine Learning for Coders — Fast.ai

Topics to understand:

• 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

This course is excellent if you’re a programmer who wants to learn and apply ML techniques.

Fundamental Algorithms

There’s a base set of algorithms in machine learning that everyone should be familiar with and have experience using. These are:

• Linear Regression
• Logistic Regression
• k-Means Clustering
• k-Nearest Neighbors
• Support Vector Machines (SVM)
• Decision Trees
• Random Forests
• Naive Bayes

These are the essentials, but there are many, many more.

After the basics, some more advanced techniques to learn would be:

• Ensembles
• Boosting
• Dimensionality Reduction
• Reinforcement Learning
• Neural Networks and Deep Learning

This is just a start, but these algorithms are what you see in some of the most interesting machine learning solutions, and they’re practical additions to your toolbox.

And just like the basic techniques, with each new tool, you learn you should make it a habit to apply it to a project immediately to solidify your understanding and have something to go back to when in need of a refresher.

Tackle a Project

Learning machine learning online is challenging and extremely rewarding. It’s important to remember that just watching videos and taking quizzes doesn’t mean you’re really learning the material.

As soon as you start learning the basics, you should look for interesting data that you can use while experimenting with your new skills. The courses above will give you some intuition on when to apply certain algorithms, and so it’s a good practice to use them in a project of your own immediately.

Through trial and error, exploration, and feedback, you’ll discover how to experiment with different techniques, how to measure results, and how to classify or make predictions. For some inspiration on what kind of ML project to take on, see this list of examples.

Tackling projects gives you a better high-level understanding of the machine learning landscape. As you get into more advanced concepts, like Deep Learning, there’s virtually an unlimited number of techniques and methods to understand.

Machine learning is incredibly enjoyable and exciting to learn and experiment with, and I hope you found a course above that fits your own journey into this exciting field.

Machine learning makes up one component of Data Science. If you’re also interested in learning about statistics, visualization, data analysis, and more be sure to check out the top data science courses, which is a guide that follows a similar format to this one.

Attempt free GK Mock Test! Download Entri App!

Why is it important to choose Entri?

• Excellent online platform for all the Competitive Exams.
• Provides updated materials created by the Entri Experts.
• Entri provides a best platform with full- length mock tests including previous year question papers.
• You can download the app for free and join the required classes.
• Entri wishes you all the best for your examinations and future endeavours.

“YOU DON’T HAVE TO BE GREAT TO START, BUT YOU HAVE TO START TO BE GREAT.”