Machine Learning has an almost cinematic quality. It evokes the work of Isaac Asimov and Arthur C. Clarke.
Science fiction has often been a precursor to real scientific progress. This is the case with artificial intelligence, although not how the writers and filmmakers predicted. At least for now.
Machine learning is authentic and not as impenetrable as you might think. If you’ve used a search engine, tagged a friend in a photo on Facebook, or noticed the lack of spam in your email inbox, then you’ve used machine learning technology.
This field is growing daily, and almost every industry can use it. If you’re interested in machine learning, you’re far from alone.
Trying to learn machine learning from scratch but not sure where to start? Or maybe you’ve taken an online course or two but hit a roadblock in your learning journey and don’t know how to proceed.
This blog will summarize all the resources you can use to learn machine learning.Before seeing the resources let’s first understand how to learn machine learning.
Steps To Learn Machine Learning in Python
These are the following steps that one should follow to learn machine learning in an effective way.
Step 1: Learn To Program For Machine Learning
Before diving into machine learning, you need to have a working knowledge of programming. Most data scientists use either Python or R to build ML models.
Most beginners start with Python because it is a universal programming language and is in higher demand than R.
Python skills are also transferable to different domains, so the transition would be easier if you were to branch out into areas like web development or data analysis in the future.
Step 2: Data Collection and Pre-Processing in Python
Now that you know how to code in Python, you can start learning how to collect and pre-process data.
Most data science beginners jump right into trying to master machine learning. They don’t emphasize data collection or analysis, which is a different skill set.
Because of this, they often struggle in the workplace when asked to perform tasks such as acquiring third-party data or preparing data for machine learning modeling.
Step 3: Analyze The Data in Python
Next, starting to learn data analysis with Python is a good idea. Data analysis identifies many data patterns and discovers insights that add value.
Before building any machine learning model, you must understand your working data. Look at the relationships between different variables in your data set. What information does one variable tell you about the other? Can you make recommendations based on the insights you discover in the dataset?
Step 4: Machine Learning With Python
You can finally start learning machine learning! I always recommend using a top-down approach when it comes to learning ML.
Instead of learning theory and working in-depth with machine learning models, start with implementation first.
First, learn how to use Python packages to build predictive models. Run the models on real datasets and observe the output. Once you know ??what machine learning looks like in practice, you can dive deeper into how each algorithm works.
Step 5: Deep Machine Learning Algorithms
Once you are familiar with the different models and how they are implemented, you can start learning the fundamental algorithms behind those models.
Step 6: Deep Learning
Deep learning algorithms can identify data representations with little or no feature engineering. Deep learning algorithms can identify words in data and derive features directly from them. Because of this, deep learning is often used to process data that does not have explicit functionssuch as image, voice, and text data.
A lot of material is provided above. You’ll forget what you’ve learned if you don’t apply it to real-life projects. You can memorize concepts, collect certifications and pass as many exams as possible. But you only really know when you start building.
Resources For Learning Machine Learning With Python
1. Google Machine Learning Crash Course (Online Training)
When it was introduced earlier in 2018, Google’s Machine Learning Crash Course quickly went from being one of the earliest to one of the most recent resources. Google offers 25 lessons, 40 activities, and video lectures and other interactive features make up the roughly 15-hour free course. When you’re finished, visit Google’s AI page for even better discounts.
2. Andrew Ng’s Machine Learning Course (Online Training)
It’s difficult to see a reliable list of resources for AI or machine learning where this course isn’t included near or at the top. It is referred regarded as the “gold standard” of machine learning instruction by even practicing AI specialists. It is an 11-week course taught by Stanford Assistant Professor Andrew Ng and is by no means simple; your talents will be put to the test. However, it’s free: You have the option of forgoing the certificate and leaving with just the knowledge you learned during the course by paying the $79 fee. If you’re serious about a career in machine learning, develop your basic knowledge with the other tools on this list then enrol in this course. (Bonus: Make sure to check out Ng’s newest book, Machine Learning Desire).
3. IBM Watson Starter Kit (Labs)
For developers wishing to get started with Watson, IBM’s pervasive cognitive computing tool/AI platform for organizations, it provides a variety of information (it even includes a free IBM cloud account). Once everything is set up, the company provides a number of starter kits that guide you through various scenarios, albeit they are straightforward, to demonstrate some of what Watson can do for businesses and how it gets there. You can dive deeper if you like what you see, but these starter kits are a great introduction to Watson and the power of AI in general (and they’re fun, too). This is an excellent start if you want to get your hands dirty.
4. Kaggle.com (data files)
To really get to know machine learning, you need data. Kaggle is probably the most popular of the many data science websites offering free datasets. You’re guaranteed to discover something you’ll want to work with on Kaggle, which compares datasets from many other websites, including Spotify’s top music, UK traffic accidents, warehouse data, and India’s air quality. Once you’ve gotten your feet wet, head over to the site’s contests page, which lists a variety of machine learning contests with prizes ranging from $100,000 to $100,000. And don’t miss the site’s opening challenge.
5. MIT Linear Algebra Class (online training)
While math is essential to all areas of programming (and vice versa), you may want to brush up on your linear algebra before diving into certain aspects of machine learning. Suppose you need more than a quick refresher (it’s excellent here). In that situation, the MIT OpenCourseWare online course that is listed above provides the whole curriculumincluding lectures, tests, and study materialsonline for free.
Best Books For Machine Learning in Python
Here you can strengthen your theoretical knowledge of the concepts used in your ML projects.
1. The Hundred Page Machine Learning Book by Andriy Burkov
A short book but with perfect knowledge. Andriy condensed all the essential points in AI/ML.
2. Practical Machine Learning with Scikit-Learn, Keras, and Tensorflow 2.0 Book by Aurelien Geron O’Reilly
this book is an alternative to deeplearning.ai’s Machine Learning and Deep Learning specializations. This book has excellent explanations, and each concept has a perfect code you can try.
3. Deep Learning Book by Ian Goodfellow
If you want to get deeper into the mathematical side of deep learning, this book has everything you need. It was published in 2015, which is quite old, but its content is excellent.
Conclusion
We have finally reached the end of this entire list of resources. There are many, and there are more! But don’t be fooled. Once you’ve gone through most of these resources, you’ll find it interesting to explore and discover new valuable resources on your own. Most importantly, you should not get stuck on completing courses and books. Keep doing projects here and there. Get into the habit of building projects after each new skill you learn.
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