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I used Weka a lot when I was first starting out, and I can confidently recommend it. Data Mining: Practical Machine Learning Tools and Techniques is essentially a companion volume to Weka and its documentation, and it provides a great introduction to machine learning methodology in general; I recommend it, too. For user friendliness and visualization, I think it's a very good place to start.

Over time, I moved to R, which has the advantage of being more likely to incorporate new, cutting-edge methods that people have coded and released in packages. (There are also other R-based ML suites, such as Rattle.) If you like Weka, the transition into R can be pretty smooth, since R and Weka can talk to each other through R's Java interface. R is also good for applying command-line options (which can also be done in Weka's console), which you will eventually want to do as you get more familiar with your techniques of choice, whether they're found in Weka or not.

Python is a popular option for a lot of users (and with it you can use, among other things, Google's open-source TensorFlow suite), and it has the advantage of generally having pretty easy-to-read code, good visualization options, and a huge and very dedicated user base.

• Limit yourself to 10 words per slide. If you break that, your slide is getting too wordy.
  
• Along with that, bring the presentation back to you. What is displayed up on the screen should just be a focal point that your audience can refer back to WHILE you are talking, to continually reinforce whatever point you're trying to make/convey at the moment.
  
• Do not add fluff. Fluff is for high school, where your job is to meet some presentation requirement. The only requirement of your presentation now is to convince your audience of something. The faster you can do that, the more memorable it will be.
  
• Use graphics, but don't go overboard, and keep them tasteful. I love using the Flat Icon site to just have some representative image that supports whatever 10 words or less I have on the screen. Occasionally, I'll use only the image and some title.
  
• Use backgrounds. The stock crap with PowerPoint is mostly garbage. You can go do a Google search and just find some really nice stuff. Just make sure it's not too busy.
  
• Be mindful of corporate branding. If your company is big enough to have a marketing department, they probably have some content that you can use (they might even have a standard slide deck template).
  
• Consider doing a recorded video version of your presentation as well, hosting it somewhere (be mindful of security/confidential information, obviously when choosing a host), and then putting a bit.ly link to it at the end of your presentation. Sometimes "the suits" have a lot of stuff they're juggling, and it's hard not to reply to emails and Slack messages during meetings. If they found something they liked but missed it, that could give them a chance to go back.
  
  Good luck!
  
  EDIT: I remembered a good book, "Beyond Bullet Points" by Cliff Atkinson that might have some good tips or ideas for you.

A good textbook will do you wonders. Get one that is fairly general and includes exercises. Do the exercises. This will be hard, but it'll make you learn an enormous amount faster.

My personal favourite book is Christopher Bishop's Pattern Recognition and Machine Learning. It's very comprehensive, has a decent amount of maths as well as good examples and illustrations. The exercises are difficult and numerous.

That being said, it is entirely Machine Learning. You mention wanting to learn about 'AI' so potentially you may want to look at a different book for some grounding in the wider more classical field of AI than just Machine Learning. For this I'd recommend Russel and Norvig's [AI: A Modern Approach](https://smile.amazon.co.uk/Artificial- Intelligence-Modern-Approach-Global/dp/1292153962). It has a good intro which you can use to understand the structure and history of the field more generally, and following on from that has a load of content in various areas such as search, logic, planning, probabilistic reasoning, Machine Learning, natural language processing, etc. It also has exercises, but I've never done them so I can't comment much on them.

These two books, if you were to study them deeply would give you at least close to a graduate level of understanding. You may have to step back and drill down into mathematical foundations if you're serious about doing exercises in Bishop's book.

On top of this, there are many really good video series on youtube for times when you want to do more passive learning. I must say though, that this should not be where most of your attention rests.

Here are some of my favourite relevant playlists on YouTube, ordered in roughly difficulty / relevance. Loosely start at the top, but don't be afraid to jump around. Some are only very tenuously related, but in my opinion they all have some value.

Gilbert Strang - Linear Algebra

Gilbert Strang - Calculus Overview

Andrew Ng - Machine Learning (Gentle coursera version)

Mathematical Monk - Machine Learning

Mathematical Monk - Probability

Mathematical Monk - Information Theory

Andrew Ng - Machine Learning (Full Stanford Course)

Ali Ghodsi - Data Visualisation (Unsupervised Learning)

Nando de Freitas - Deep Learning

The late great David MacKay - Information Theory

Berkeley Deep Unsupervised Learning

Geoff Hinton - Neural Networks for ML

Stephen Boyd - Convex Optimisation

Frederic Schuller - Winter School on Gravity and Light

Frederic Schuller - Geometrical Anatomy of Theoretical Physics

Yaser Abu-Mostafa - Machine Learning (statistical learning)

Daniel Cremers - Multiple View Geometry

/r/Nader_Nazemi please do your homework. This question has been asked a dozen at least in the past years.
Likewise, a quick Google search will find you that he wrote on of the popular ML books

Stochastic control theory, though that's not necessarily AI/ML related. You may still find that a good research subject and include methods or components you could mix into your overarching model.

ex.
https://www.amazon.com/Introduction-Stochastic-Control-Electrical-Engineering/dp/0486445313

I can't give a personal recommendation for a specific, current model. There is no way around doing some research and looking at the models in person. We have a mix of gaming laptops where I work, for QA testing. Our QA lead ranks them from best to worst brand as Razer->Asus->MSI->Alienware, in terms of which he has to get serviced the most, with Asus and MSI being roughly comparable. The Razer laptops with discrete Nvidia chips are great, but big bucks though compared to the MSI and Asus. Something in your budget would be a unit like this: https://www.amazon.com/MSI-GE62VR-Apache-Pro-026-i7-6700HQ/dp/B01IS33QWY/ref=pd_lpo_147_lp_t_3?_encoding=UTF8&psc=1&refRID=YXMX7D915FEQB0KMJ7GQ

Worth noting, gaming laptops with discrete graphics tend to be much bulkier than those with intel graphics, and with relatively poor battery life. The Alienware M17x R5 I have next to me for testing is more of a portable computer than a laptop; I wouldn't want to bring it on a plane or to Starbucks like I could with a Macbook or Razer Ultrabook. There are paving stones lighter than this thing, and it's noisy too.

You have servers with 8+ GPUs. ML is not the only field that uses GPU in big server farms. The movie industry used them first, but for your budget you will only be able to buy the server rack anyway. Also your budget is very low for ML specifically so you wont be able to buy GPU specialized for ML (no Tensor Cores). Your only choice is a consumer PC really pre-built.

For reference you also have things like [this][2] and [this][3]

[3]: https://www.dell.com/en-us/work/shop/povw/poweredge-r740
[2]: https://www.dell.com/en-us/work/shop/productdetailstxn/poweredge-t640

https://www.amazon.com/Machine-Learning-Security-Protecting-Algorithms-ebook/dp/B079C7LKKY

Foundations for Architecting Data Solutions by Ted Malaska and Jonathan Seidman

This book is your new bible: Guerrilla Analytics: A Practical Approach to Working with Data

Deep Learning with Python https://www.amazon.com/dp/1617294438/ref=cm_sw_r_cp_apa_i_9YmTCbSDTWXB7

The price jumped up a ton from when you bought it.

https://www.amazon.com/Nvidia-GEFORCE-GTX-1080-Ti/dp/B06XH5ZCLP

https://www.amazon.com/EVGA-GeForce-Founders-Support-11G-P4-6390-KR/dp/B06XH2P8DD

​

https://www.ebay.com/p/16026507463?iid=123919351872&rt=nc&thm=1000

The used ones are 500, but the pages literally say that they are almost out of stock. Even the new ones are 800.

Nvidia is a freaking monopoly that needs to get split up to bring back competition, lower prices and innovation. But that's an entirely different thread on its own.