artificial intelligence and machine learning. Where can to start?

Roadmap for the study of artificial intelligence

One of the main reasons people don’t jump directly into the field of artificial intelligence is that they don’t know where to start. There is a lot of technical jargon that comes their way when they are looking for resources to learn about artificial intelligence.

In this thread, we’ll walk you through the process in detail by which you can easily start your journey with AI.

 

  1. Basics

The best way to start with AI is to brush up on your basics. You can start with basic math and then, preferably, get your hands dirty with programming language. It is always recommended to use Python due to its large and supportive community and many packages and libraries to help you throughout your journey.

Here’s something you might want to pay attention to.

  • Matrices and fundamentals of linear algebra.
  • Mathematical calculus.
  • Graph theory.
  • Vector.
  • statistics and probability.

Other than that, there are a few tools you might want to learn to help you process data better.

  • Database basics.
    • SQL and joins in SQL.
    • Relational and non-relational databases.
    • NoSQL databases.
  • Tabular data (Excel).
  • Data frames and data series.
  • Data formats (JSON, CSV, XML).
  • Regular expressions.
  • Extract, transform and download data.

After getting familiar with these topics, it’s time to delve into the basics of a programming language that can handle data with ease. We usually have two options – Python and R. Most companies prefer Python over R because of the huge support it provides. Here is a roadmap for learning Python for artificial intelligence.

  • Python basics – expressions, variables, data structures, functions, packages like pip, etc.
  • After learning the basics, you need to learn some important data processing libraries such as pandas, NumPy, and matplotlib.
  • Next, you should get your hands dirty in virtual environments and how to use Jupyter notebooks/labs efficiently and effectively.

You are now well prepared to take the next big step in your AI journey.

read also : How to Learn Machine Learning in Practice

  1. Data preprocessing

Now that you can manipulate the data, it’s time to explore various techniques that will help you transform unstructured data into structured data so that you can extract information from it using machine learning algorithms. These methods include –

  • Principal component analysis.
  • Dimension reduction.
  • Normalization.
  • Cleaning data, handling missing values, etc.
  • unbiased ratings.
  • Extract features.
  • Noise reduction and sampling.

These methods will help you organize your data for further analysis. From here you have 3 directions –machine learning, processing specialist data, and data engineering.

1. Machine learning engineer

Machine learning is the application of algorithms that learn from data to find certain patterns and features that would help us make predictions and make decisions based on new data. The better and more accurate the algorithm, the better the results. Therefore, it is extremely important that you choose the most suitable algorithm for any problem statement. The general roadmap for learning machine learning is this –

  • Learn concepts such as types of input parameters and variables (categorical, ordinal, and numeric).
  • Concepts such as cost functions and gradient descents.
  • Overfitting, undertraining, training, testing, and validation datasets.
  • Accuracy, recall, bias, and variance.

After that, you can move on and explore different categories of machine learning algorithms that will help you solve specific problems.

The main categories are –

  • supervised learning– algorithms that are used to classify objects, as well as to solve regression problems.
  • Unsupervised learning– clustering algorithms.
  • Ensemble training– raising, packing, and stacking.
  • Reinforcement learning– learning algorithms based on rewards.

It is not difficult to learn these algorithms and the intuition behind them, it is difficult to figure out which algorithms are best for solving certain types of problems. You can brush up on your skills with comprehensive practice.

2. Data Processor

This is another career path you can take in the AI ​​field. This includes playing with data and mathematical models to find the right information hidden in the data. The two main components of a data science course are statistics and data visualization.

Topics you need to know well in statistics include –

  • Probability Theory.
  • Continuous and discrete distributions.
  • Hypothesis testing.
  • Summary statistics and some important laws like (LLN, and CLT).
  • Grades such as MLE, KDE, etc.
  • Confidence intervals.

For visualization, you can use tools like –

  • Python – Matplotlib, seaborn, plotline, etc.
  • Web – Vega-lite, D3.js, etc.
  • Dashboards – Tableau, Dash, etc.

3. Data Engineer

Data engineering is the study of different dimensions of data. Relevant components include-

  • f summary of data formats.
  • Data Discovery.
  • Source and collection of data.
  • Data integration.
  • Merging data.
  • Transformation and enrichment.
  • Polling and OpenRefine.
  • Data lake and data warehouse.

Apart from these career paths, you can also choose advanced subjects such as Software Engineer. deep learning and engineering by big data. However, these paths require deep experience and knowledge in areas such as machine learning and data processing. This is the best and easiest path you can take to get started on your AI journey.

Beyond that, there are other areas of AI that you can explore. These include –

  • Object recognition.
  • Robotics.
  • Speech processing.
  • Expert systems.
  • Natural language processing.

Here is a detailed description advanced

No matter what field or sub-field they work in, they have all considered AI engineers provided they work in the aforementioned fields. Their ultimate goal is to develop better systems with artificial intelligence.

Artificial intelligence courses

There are many online courses and tutorials available in the market today, most of them from established industries, to help you understand AI and its applications. You can expect that in these courses you will learn the following –

  • Understanding the basics and theory.
  • ML algorithms.
  • Mathematical concepts.
  • AI applications such as games, real-time challenges, self-driving cars, etc.
  • AI agents love search engines, constraint satisfaction issues, etc.
  • Deep learning.
  • Data processing.

The benefits you can get by learning AI from tutorials are as follows –

  • You will gain ace-level programming skills in AI.
  • You will receive downloadable templates for reusable codes.
  • This will help you develop your intuition.
  • You will apply theoretical knowledge to real-time solutions.

Here is a list of the best certification courses you can enroll in to get started on your AI journey.

1. Artificial Intelligence A to Z™: Learn How to Build AI

It paid course that provided a demo.

Main characteristics –

  • Complete AI skills from beginner to expert.
  • Code templates that you can copy and use for your projects.
  • Intuitive tutorials with practical examples.
  • Solutions to real problems.
  • Course support.

This course includes –

  • 16.5 hours of video on demand.
  • 21 articles with detailed explanations.
  • 1 resource that can be downloaded.
  • Full lifetime access to all videos.
  • Completion certificate.

2. Stanford University Artificial Intelligence Certification Program

This is a beginner’s course in AI that is suitable for candidates with CS experience and professional software engineers who want to choose a career in AI. You can earn a certificate in artificial intelligence from Stanford University and teach under Professor Andrew.

Main characteristics –

  • Fundamentals of machine learning and knowledge representation.
  • Useful logical and probabilistic models.
  • Robotics, NLP, and visual learning.

3. Introduction to artificial intelligence

It free course provided by Udacity.

Main characteristics –

  • Fundamentals of AI like statistics, Bayesian network, uncertainty principle, etc.
  • Machine learning, logic, and planning.
  • Applications such as image processing, computer vision, NLP, information retrieval, robotics, etc.

4. Artificial intelligence

This course is provided by MIT Open Courseware and is free MIT Open Courseware.

Main characteristics –

  • AI algorithms such as DFS, BFS, hill climbing, etc.
  • Limits.
  • Machine learning and deep learning.
  • Models of logical inference.

5. Artificial Intelligence (Course Pack) – LinkedIn

It paid course that comes with LinkedIn Premium.

Main characteristics –

  • Basic courses
  • Machine learning
  • Deep Learning
  • Practical projects
  • Business Basics Training
  • Predictive Analysis
  • AI for cybersecurity

 

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