How to Learn AI in 8 Months? Your Path from Beginner to Advanced | AI Roadmap

So, you are here to get a road map to Learning AI from scratch to advance level. Congratulations you are at a right place. Here we will discuss about why you should learn Ai? What are the Future job placement opportunities for AI Engineers? And the most correct and accurate path way to make you an Artificial Intelligence (AI) engineer in 8 months (4 Hours of daily study & Practice). If you are a student, machine learning engineer, AI researcher, or simply an AI enthusiast, this AI roadmap guide is for you.

What is Artificial Intelligence?

To learn what is AI you should first learn what it is? AI is the ability of a computer or machine to perform tasks that usually require human intelligence. This includes things like learning from experience, recognizing patterns, understanding natural language, and making decisions. Essentially, AI allows machines to think and act in a human-like way, automating complex processes and solving problems without needing explicit instructions for every step.

Different type of Artificial Intelligence?

There are three levels of AI based on its capabilities.

1. Artificial Narrow Intelligence (ANI), or Weak AI, refers to AI systems designed to perform a specific task or a narrow range of tasks for examples include voice assistants, recommendation algorithms, and self-driving cars.
2. Artificial General Intelligence (AGI), or Strong AI, refers to AI systems with the ability to understand, learn, and apply intelligence across a wide range of tasks and domains, similar to human beings. AGI remains theoretical, as current AI technologies have not yet achieved this level of general intelligence.
3. Artificial Super Intelligence (ASI) refers to AI systems that surpass human intelligence across all domains. ASI would outperform humans in every conceivable way, including problem-solving, decision-making, creativity, and emotional intelligence. While ASI could potentially solve complex global issues, it also raises significant ethical and existential risks, as machines might develop goals misaligned with human values or act in harmful ways.

Different Fields Related to AI

The fields given below are all interconnected, but they have distinct purposes:

Artificial intelligence (AI) is the field of computer science focused on creating machines that can reason, learn, and act like humans. These machines can perform tasks that typically require human intelligence, like solving problems, understanding language, and recognizing objects.

Different Careers within AI

Doing AI doesn’t mean you just have to stick with it you can have different options once you have done learning AI. You can dive into the data scientists, machine learning engineers, and research scientists. 

Machine Learning (ML)

It is a subfield of AI that focuses on developing algorithms that can learns from data without explicit programming. These models are trained on large datasets and can improve their performance over time as they encounter more data.

Machine learning engineers design and deploy machine learning systems to make predictions from organizational data, solving problems like predicting customer churn and lifetime value. They deploy models for organizational use, working primarily with coding tools. Key skills include deep knowledge of Python, Java, and Scala; familiarity with machine learning frameworks like Scikit-learn, Keras, or PyTorch; understanding of data structures, data modeling, and software architecture; advanced mathematical skills; and exceptional teamwork and problem-solving abilities. Essential tools include machine learning libraries (e.g., Scikit-learn, TensorFlow), data science libraries (e.g., Pandas, NumPy), cloud platforms (e.g., AWS, Google Cloud Platform), and version control systems (e.g., Git).

Deep learning (DL)

It is a subfield of machine learning inspired by the structure and function of the human brain. DL algorithms use artificial neural networks with multiple layers to process complex data like images, text, and speech. Deep learning excels at tasks requiring pattern recognition and can achieve high accuracy with massive datasets.

Data Science

It is a broader field that utilizes various tools and techniques to extract knowledge and insights from data. Data scientists deal with collecting, cleaning, analyzing, and interpreting data. They may use machine learning algorithms as part of their toolkit, but their primary focus is on understanding the data and using it to solve problems.

Data Scientists investigate, extract, and report meaningful insights from an organization’s data, communicating these insights to non-technical stakeholders and connecting machine learning workflows to business applications. They utilize coding tools and big data technologies to analyze and interpret large datasets, develop data-driven solutions, and create machine learning algorithms for tasks such as customer segmentation, credit score prediction, and recommender systems. Key skills include proficiency in Python, R, SQL, machine learning, AI concepts, statistical analysis, data visualization, and effective communication. Essential tools include Pandas, NumPy, Scikit-learn, Matplotlib, Tableau, Airflow, Spark, Git, and Bash.

Research Scientist

Research scientists in AI conduct cutting-edge research to advance the field, inventing new algorithms or improving existing ones, and presenting their findings at conferences and in scholarly articles. Key skills include a solid understanding of machine learning and deep learning, proficiency in Python and other programming languages, extensive knowledge of AI-related mathematical theory, the ability to conceptualize and validate novel AI models, and strong writing and public speaking abilities. Essential tools include deep learning frameworks (e.g., TensorFlow, PyTorch), scientific computation tools (e.g., MatLab, Mathematica), software for writing and presenting (e.g., LaTeX, Google Slides), and cloud computation resources (e.g., AWS, Google Cloud Platform).

Read More: How AI is Driving Growth and Innovation for Online/Offline Businesses

How AI Works? A Step-by-Step Breakdown

AI is now really becoming essential part of our life day by day. And it’s better that you should know how it actually works. Even if you don’t want to learn it. Let’s break down the process into five key steps:

Input: Feeding the Machine

The first step involves collecting data from various sources. This data can be text, audio, videos, or anything else that can be digitized. The AI system is then trained on this data, which is sorted into categories so that it can only process the data that is relevant to the task at hand. For instance, an AI designed to recognize objects in images would only be trained on image data.

Processing: Making Sense of the Data

Once the data is inputted, the AI processes it by looking for patterns. The AI has been programmed to learn patterns from data, so it can use this knowledge to identify similar patterns in the new data. Imagine an AI trained on millions of cat pictures. When presented with a new image, it can analyze the data and recognize patterns that match those of cats, allowing it to identify the new image as a cat as well.

Outcomes: Using Patterns for Predictions

After processing the data, the AI can use the patterns it found to predict outcomes. For example, an AI that is trained on customer data might be able to predict which customers are likely to churn (cancel their service). This allows businesses to take proactive steps to retain these customers.

Adjustments: Learning from Mistakes

If the AI’s predictions are not accurate, it can learn from its mistakes and adjust its algorithms. This is an important step in the AI learning process, as it allows the AI to improve its accuracy over time. Continuing with the customer churn example, if the AI incorrectly predicts a customer will churn but they don’t, the AI can adjust its algorithms to account for this new information.

Assessments: Refining the AI

Finally, the AI assesses its performance and makes adjustments to its algorithms as needed. This process of assessment and adjustment is what allows AI to continuously learn and improve. Through ongoing assessments, AI systems can become more sophisticated and capable of handling increasingly complex tasks.

Why chose AI as Career?

AI a fast-growing field

You can see the hype of AI around the world now a days which means you can understand it’s really growing field. Cause it’s a new field people are now getting into it rapidly. And according to a research ​​AI and machine learning specialists top the list of fast-growing jobs over the next five years. And now businesses and companies are adopting AI into their operations the job market for AI specialist will likely only increase in coming years.

AI as a High Paying Job

So, the jobs and the packages you get upon them completely depends on your skill and the country you are living in. If you are living in a developing country like in Asia you will probably get less salary then those as compared to US, Canada, And in Australia and so on. However, if we specifically discuss about average salaries of AI Engineers in USA, then it is $153,719 per annum, with the potential for bonuses and profit sharing. Machine learning engineers and data scientists are similarly well-paid, with average salaries of $151,158 and $178,515 per annum, respectively. Speaking of salary it is good career.

Why AI as Career?

Now the question arises why AI? Why not all the other high paying jobs. First is Ai is not an easy field its challenging it require a lot of problem-solving skills and creativity. If you are someone who enjoy challenges then AI is for you. Secondly, AI has the potential to solve some of the world’s most pressing problems, from climate change to disease. If you’re interested in using your skills to make a difference, then AI could be a great way to do that. And lastly if you chose AI which is a broad field, so there are many different career paths you can take. You could become a machine learning engineer, a data scientist, an AI researcher, or something else entirely. Well, it completely depends on your skills and interests in general.

AI Engineer Roadmap for Beginners

If you want to set foot in the industry and want to learn and create AI systems. First you should consider learning the four core skills you can see above these skills can help you thrive in any field not only just in AI. For example, good communication skills can help you make connections and secure opportunities in different places. And yes, You have remember that you must have to be consistent and determined if you want to be a good engineer. Here is the Pathway .pdf file you can view it that how they have arranged all of the courses inline from scratch to advance level you can also solve the exercises and question related to the programming. Here is the shorter explanation for what’s in the .pdf file. Duration: 8 Months (4 hours/day).

AI Roadmap .pdf File

RoadMap in Short

Week 0: Research and Scam Prevention

– Research market and mentors to avoid scams.

Week 1-2: Computer Science Fundamentals

– Data representation, computer networks, internet protocols, and programming basics.

Week 3-4: Beginner’s Python

– Variables, data structures, loops, functions, and exception handling.

Week 5-6: Data Structures and Algorithms in Python

– Basic data structures, algorithms, and recursion.

Week 7-8: Advanced Python

– Inheritance, generators, iterators, multithreading, and multiprocessing.

Week 9: Version Control (Git, GitHub)

– Basic commands, branches, merging, and pull requests.

Week 10-11: SQL

– Basics of relational databases, basic and advanced queries, joins, and database creation.

Week 12: Numpy, Pandas, Data Visualization

– Data manipulation with Numpy and Pandas, data visualization with Matplotlib and Seaborn.

Week 13-16: Math & Statistics for AI

– Descriptive and inferential statistics, linear algebra, calculus, probability, distributions, and hypothesis testing.

Week 17: Exploratory Data Analysis (EDA)

– Practice EDA using multiple datasets.

Week 18-21: Machine Learning

– Preprocessing, model building, evaluation, hyperparameter tuning, and unsupervised learning.

Week 22: ML Ops

– API development, CI/CD pipelines, containerization, and cloud platform familiarity.

Week 23-24: Machine Learning Projects

– Complete two end-to-end ML projects (regression and classification) and deploy them.

Week 25-27: Deep Learning

– Neural networks, CNNs, RNNs, and LSTMs.

Week 28-30: NLP or Computer Vision

– Choose a specialization and complete related projects.

Week 31-32: LLM & Langchain

– Learn about LLM, vector databases, embeddings, and the Langchain framework.

Week 33 onwards

– Focus on more projects, online brand building, and job applications. You can view all this Road Map in detail in the .pdf file below

Learning Tips

Spend more time digesting, implementing, and sharing information. Utilize group study and accountability partners to enhance learning and maintain progress. Here are some points you can consider.

• Choose Your Focus:

Decide on your career goals and focus accordingly. For applied roles like data scientist or machine learning engineer, concentrate on programming and machine learning algorithms. For research roles, delve into the theory behind AI, including mathematics, statistics, and theoretical computer science.

• Start Learning:

Begin your learning journey using the suggested resources. Take your time to thoroughly understand each concept before moving on.

• Apply Your Skills:

Apply what you learn through real-world projects to gain practical experience and enrich your portfolio.

• Join a Community:

Engage with AI communities online and offline to stay updated, get help, and network with other AI enthusiasts.

• Keep Iterating:

Continue learning and improving your skills by following AI blogs, reading research papers, and taking advanced courses to stay current and grow from novice to expert.

AI Job Market & How to Find Job?

Finding a job is not as hard as you think if you have the skills and have confidence in yourself you can possibly find job in no time. The thig people lack is the preparation. They do study hard develop there skills but, they don’t prepare for the job interviews.

Here are some of the steps you can do to find jobs in AI

1. Create a Strong Portfolio

• Projects: Build and showcase projects on GitHub or personal websites.
• Competitions: Participate in Kaggle competitions to demonstrate practical skills.
• Publications: Write blog posts or research papers on AI topics.

2. Update Your Resume and LinkedIn Profile

• Highlight relevant skills, projects, and experiences.
• Use keywords that match job descriptions to improve visibility.

3. Leverage Job Platforms

• General Platforms: LinkedIn, Indeed, Glassdoor.
• Specialized Platforms: AI Jobs Board, AngelList (for startups), GitHub Jobs.

4. Network

• Conferences and Meetups: Attend events like NeurIPS, ICML, local AI meetups.
• Online Communities: Engage in forums like Reddit’s r/MachineLearning, LinkedIn groups.
• Networking Sites: Use LinkedIn to connect with professionals in the field.

5. Apply for Internships and Entry-Level Positions

• Many companies offer internships or junior roles for recent graduates or those new to the field.

6. Target Companies

• Tech Giants: Google, Facebook, Amazon, Microsoft.
• Startups: Many innovative AI roles can be found in smaller, agile companies.
• Research Institutions: Universities, dedicated AI research labs.

7. Prepare for Interviews

• Technical Skills: Be ready to solve coding problems and explain algorithms.
• Practical Knowledge: Understand how to apply AI techniques to real-world problems.
• Soft Skills: Communication, teamwork, and problem-solving abilities.

8. Continuous Learning

• AI is a rapidly evolving field. Keep learning through online courses (Coursera, edX), certifications, and staying updated with the latest research and trends.

Conclusion

Diving into the world of Artificial Intelligence now offers immense opportunities and challenges. As you know AI is rapidly growing, learning it can lead to lucrative and impactful careers. By following the structured 8-month roadmap above, you can develop essential skills, work on real-world projects, and prepare for high-demand roles in AI. Remember to focus on continuous learning, building a strong portfolio, and networking within the AI community. With dedication and persistence, you can become a proficient AI engineer and contribute to the innovative solutions shaping our future. Start your AI journey today and embrace the exciting possibilities ahead! This could be the start of something great for you.