**Machine Learning – Key to Data Science**

Best and most cost effective courses to understand the concept of Machine Learning, Python and Deep Learning recommended by industry experts.

Let us quickly understand the basics of Machine Learning. So that you have complete clarity on what do you need to learn, I would request you to just go through this.

“Machine Learning is the science of making computers learn and act like humans by feeding data and information without being explicitly programmed”

Machine Learning started as a medium of model building and predicting behaviour, now it is getting integrated with AI(Artificial Intelligence).

But I will keep it simple for you and it is not so technical that you can’t learn. Basically, there are three branches of Machine Learning.

**Supervised Learning: **

In supervised learning, model predicts outcome (dependent) variable from a given set of predictors (independent variables). Using these set of independent variables and training data, we generate a model/function that maps inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning algorithms are Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

**Un-Supervised Learning:**

In this, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention.

Examples could be market segmentation, Text mining etc. Some of the algorithms used are Apriori algorithm, K-means.

**Reinforced Learning:**

Using reinforced learning, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from experience and tries to capture the best possible knowledge to make accurate business decisions.

Examples are gaming, stock trading or robot movements etc. Example of algorithm is Markov Decision Process.

**What should I learn? – Concepts or coding **

This is the question that many of you might be having. Many online learning platforms are promoting coding with R or Python and become machine learning expert.

So, what happens, if we take up these courses and learn coding but we don’t understand the background. So, in the real world scenario you will be only able to do, what your supervisor tells you to do.

For example, you may learn how to use a clustering technique in Python, but you will not know when to use clustering and which technique will work best in a given scenario.

To solve a real world problem, we need to know all the different techniques and which one can be applied. In our models, we look for better accuracy in predicting. To do this, we may end up using multiple techniques and concepts. All this needs to be logical based on statistical or mathematical concepts.

Initially, you may be doing only coding work but if you want to grow then concepts are very important and my suggestion is to start with building sound base with concepts.

2nd step should be learning Python for coding work. Once you master these then Deep Learning comes into picture.

If you are new to this field then I would try to explain you which courses you can take up and how to move forward, I am keeping it simple with few top rated courses only.

Also keeping cost in mind, I will recommend the most cost effective way to gain knowledge. I am not recommending you to start searching YouTube and get lost in plethora of videos, some are good but others are just average and you will not understand where to start and how do they link to each other.

Overall we can divide the complete learning into three broad areas as shown below in the graphic.

**Best courses for Machine Learning and Deep Learning :**

1. Machine Learning (Stanford University-Coursera) by Andrew Ng

2. Machine Learning Specialization(Coursera) by University of Washington

3. Micro Masters in Machine Learning(Edx) by Columbia University

4. Machine Learning A-Z™: Hands-On Python & R In Data Science(Udemy) by Kirill Eremenko

5. Complete Python Bootcamp: Go from zero to hero in Python 3(Udemy) by Jose Portilla

6. Applied Data Science with Python Specialization(Coursera) by University of Michigan

7. Deep Learning Specialization by Deeplearning.ai(Coursera) Course by Andrew Ng

8. Deep Learning A-Z™: Hands-On Artificial Neural Networks(Udemy) by Kirill Eremenko

**Machine Learning Concepts**

This is arguably the best course on Machine Learning concepts. This course is not for Python coding but to understand the underlying concepts and you will really enjoy learning from Andrew Ng, who is the instructor of this course.

Andrew Ng from Coursera is a dynamic instructor, he is the co founder of Coursera and founding lead of Google Brain. He inspires confidence and self belief, especially when sharing practical implementation tips.

Machine Learning courses by Andrew Ng are highly recommended by most of the industry experts.

**You can simultaneously do this course and learn Python, check below options for Python courses.**

**Machine Learning (Stanford University-Coursera) by Andrew Ng**

Duration : Approx 55 Hours

Price : Approx $ 48/month (It will take 2-3 months to complete, you can apply for financial aid option for free course)

Rating : 4.9 out of 5

You can Sign up Here

**Note: You can enrol for a 7 day free trial**

The next course is actually a specialization from Coursera and is offered from University of Washington. It is a combination of four courses:

a) Machine Learning concepts with case studies

b) Regression

c) Classification concepts

d) Clustering and Retrieval

You can also take these courses one by one as well and work at your own pace. Upon completion you will get the certificate from the University. You will also get to work on a hands on projects as part of specialization.

**Machine Learning Specialization(Coursera) by University of Washington **

Duration : Approx 200 Hours

Price : Approx $ 48/month (It will take 5-6 months to complete, you can apply for financial aid option for free course)

Rating : 4.7 out of 5

You can Sign up Here

**Note: You can enrol for a 7 day free trial**

The next one is a unique program offered by Edx, this is the MicroMasters program by Columbia university. Students may apply to the university offering credit for the MicroMasters program certificate and, if accepted, can pursue an accelerated and less expensive Master’s Degree. These MicroMaster certifications are well recognized by the industry.

Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modelling and model selection.

Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

**Micro Masters in Machine Learning(Edx) by Columbia University**

Duration : Approx 120 Hours

Price : Approx $ 300 for certificate (one can browse through the course for free)

Rating : 4.7 out of 5

You can Sign up Here

**Note: You can enrol for a 7 day free trial**

**Learn Python and R**

Most of the modern day coding for Machine Learning is happening in Python and R. Python has replaced most of the legacy tools/languages like SAS.

It is always better to start learning Python and R before we move into Machine Learning concepts. This is specially important for someone having very less or no programming experience.

Recommended Courses (Pick one of them):

**Machine Learning A-Z™: Hands-On Python & R In Data Science(Udemy) by Kirill Eremenko**

Duration : Approx 41 Hours

Price : Lowest price

Rating : 4.5 out of 5

You can Sign up Here

**Complete Python Bootcamp: Go from zero to hero in Python 3(Udemy) by Jose Portilla**

Duration : Approx 24 Hours

Price : Lowest price

Rating : 4.5 out of 5

You can Sign up Here

Above two courses from Udemy are beginner level good courses and the next one is an intermediate level specialization which I would recommend for more detailed study.

This one is from University of Michigan and offered by Coursera. This specialization is a combination of following courses:

Introduction of Python

Applied Plotting and data representation

Machine Learning with Python

Text Mining using Python

You can always pick one of the above mentioned course, complete and then opt for the next one.

**Applied Data Science with Python Specialization(Coursera) by University of Michigan**

Duration : Approx 99 Hours

Price : Approx $ 48/month (It will take 3-4 months to complete, you can apply for financial aid option for free course)

Rating : 4.5 out of 5

You can Sign up Here

**Deep Learning and AI**

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods. This is the next step within machine learning and AI.

The first recommended course is a specialization from Andrew Ng and it broadly covers following topics:

a) Tensor flow

b) Convolutional Neural Network

c) Artificial Neural Network

d) Deep Learning

You can pick one of the courses to start with and later complete other ones to earn your specialization, This covers all the important aspects of deep learning.

**Deep Learning Specialization by Deeplearning.ai(Coursera) Course by Andrew Ng**

Duration : Approx 120 Hours

Price : Approx $ 48/month (It will take 4-5 months to complete, you can apply for financial aid option for free course)

Rating : 4.9 out of 5

You can Sign up Here

This is a good course but you need to have prior Python experience. You will learn to write Deep Learning algorithms using Python.

This course is recommended because it’s a good one and very cost effective.

**Deep Learning A-Z™: Hands-On Artificial Neural Networks(Udemy) by Kirill Eremenko**

Duration : Approx 22 Hours

Price : Lowest price

Rating : 4.5 out of 5

You can Sign up Here

Machine Learning and Deep Learning are the most sought after skill in today’s time as well as in the foreseeable future. This is the right time to start your learning and jump start your career.

Since this is a fast evolving industry, be ready to learn new techniques and target to learn a new thing every quarter.

I have done exhaustive research and came up with the Best Machine Learning Courses, Best Deep Learning Courses and Best AI Courses which cover various aspects, technologies and programming languages.

I have only included the best and though there are other good courses also available but I have tried to keep it simple.

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