Roadmap for Becoming an Artificial Intelligence Engineer in 2024

In yesterday’s newsletter, we saw what Artificial Intelligence and its important concepts. Artificial Intelligence is one of the hottest topic in today’s world that has virtually made its mark in almost all realms of technology. Embarking on the journey to become an Artificial Intelligence (AI) engineer is an exciting and rewarding endeavor. In a world increasingly shaped by technological advancements, AI stands at the forefront, driving innovation and transforming industries. As we delve into the intricacies of this fascinating field, the roadmap ahead unfolds with a series of key milestones and skill acquisitions.

1. Programming Fundamentals

• Importance: Lay a strong foundation in a programming language. Python is highly recommended for its readability, extensive libraries, and community support.

  • Skills to Learn:

  • Variables, data types, and operators

  • Control structures (if, else, loops)

  • Functions and OOP concepts

2. Data Handling and Manipulation

• Importance: AI heavily relies on data. Proficiency in handling and manipulating data is crucial for preprocessing and analysis.

  • Skills to Learn:

  • Numpy and Pandas for data manipulation

  • Understanding and working with datasets

3. Mathematics and Statistics

• Importance: AI algorithms are often based on mathematical and statistical principles. A strong mathematical foundation is essential.

  • Skills to Learn:

  • Linear algebra

  • Calculus

  • Probability and statistics

4. Machine Learning Basics

• Importance: Understanding the fundamentals of machine learning is a cornerstone for AI development.

  • Skills to Learn:

  • Supervised and unsupervised learning

  • Regression, classification, clustering

  • Evaluation metrics

5. Deep Learning

• Importance: Deep Learning is a subset of machine learning crucial for advanced AI applications, such as image and speech recognition.

  • Skills to Learn:

  • Neural networks

  • TensorFlow or PyTorch for deep learning

  • Convolutional and recurrent networks

6. Natural Language Processing (NLP)

• Importance: NLP is essential for AI applications dealing with human language, such as chatbots and language translation.

  • Skills to Learn:

  • Tokenization, stemming, and lemmatization

  • Named Entity Recognition (NER)

  • Word embeddings

7. Computer Vision

• Importance: For AI applications involving image and video processing, such as facial recognition and object detection.

  • Skills to Learn:

  • Image processing

  • Feature extraction

  • Object detection algorithms

8. Reinforcement Learning

• Importance: Learn how machines can learn from experience and improve decision-making.

  • Skills to Learn:

  • Markov Decision Processes

  • Q-learning and policy gradients

  • Applications in game playing and robotics

9. Model Deployment and Optimization

• Importance: Understanding how to deploy models into production is critical for real-world applications.

  • Skills to Learn:

  • Model deployment platforms (e.g., Flask, Docker)

  • Model optimization techniques

Why Probability and Statistics important in the field of Data Science ?

Statistics plays a vital role in collecting, presenting, analyzing, and leveraging data for decision-making, problem-solving, and product/process design. Its application extends to drawing inferences about various aspects, such as product quality, performance, or system durability. For instance, it finds utility in assessing weather forecasts or system utilization. Probability, a key companion to statistics, empowers us to make informed statements and predictions regarding future events. This tandem serves as the foundational framework for Data Science, offering essential tools for robust design, simulation, experiments, decision analysis, forecasting, time-series analysis, and operations research. In essence, statistics and probability form the backbone of data-driven endeavors, providing the means to navigate uncertainties, quantify risks, and extract meaningful insights from diverse datasets.

• Statistical Analysis:

  • Uncovering trends and patterns in large datasets.

  • Forming hypotheses, determining sample sizes, and sampling procedures.

  • Using Descriptive and Inferential Statistics for analysis.

  • Visualization, interpretation, and generalization of findings.

  • Utilization of Python and R for statistical analysis.

• Quantitative Analysis:

  • Involves numerical data.

  • Suitable for answering questions like 'How much?' and 'How many?'

  • Examples include measuring attendance or profit revenue.

• Qualitative Analysis:

  • Involves non-numeric, categorical data.

  • Answers questions like 'How?' and 'Why?'

  • Useful for understanding consumer mindset.

• Data Understanding:

  • Classification into structured, semi-structured, and unstructured data.

  • Descriptive and Numerical data types.

  • Binary, Nominal, Ordinal, Interval, and Ratio data.

• Measures of Central Tendency:

  • Mean, Median, and Mode.

  • Mean for average, Median for middle value, Mode for most occurring.

• Measures of Dispersion:

  • Variance and Standard Deviation.

  • Indicate data spread around the mean.

• Linear Regression:

  • Maps relationships between two variables.

  • Equation:
    Y=βo+β1x.

  • Used in forecasting and cause-and-effect analysis.

• Resampling Methods:

  • Bootstrapping, Monte-Carlo, Cross Validation.

  • Generate unique sample distributions for analysis.

• Probability:

  • Likelihood of an event happening.

  • Conditional Probability, Random Variables, Probability Distribution.

  • Bayes' Theorem for probability based on prior events.

• P Value and Hypothesis Testing:

  • Validate hypotheses against observed data.

  • P value < acceptable value rejects the null hypothesis.

  • Used in various confidence levels for robust statistical tests.

• Application in Data Science:

  • Statistics and Probability essential for collecting, organizing, and analyzing data.

  • Probability crucial for predictive modeling in various applications.

  • Used by data-driven businesses for predictions and insights.

Financial Term of the day : What is GDP and its importance ?

Gross Domestic Product (GDP) stands as a crucial indicator for assessing the economic well-being of a nation. In simple terms, it reflects the total value of all goods and services within a country.

How to Calculate the GDP of a country ?

The GDP can be computed using the formula:

GDP = C + I + G + (Exports−Imports)

Where:

• C represents Consumption, encompassing individual spending on physical goods (such as cars and food) and services (like haircuts). In developed nations like the US, the UK, and India, consumer spending constitutes a significant portion of GDP.

• I signifies Investments, representing the money that companies invest in tangible products like land, buildings, and business infrastructure. Personal expenditures on assets like homes also fall under this category.

• G denotes Government spending, reflecting the funds allocated by the government for infrastructure development, including schools, hospitals, roads, and defense. The amount varies from country to country.

• Net Exports (Exports−Imports) quantify the value of total exported goods and services minus the value of total imported goods and services. Positive net exports indicate a trade surplus, while negative net exports signify a trade deficit.

Usually the GDP of a country expands when an economy is healthy and contracts when the economy is bad. When the GDP of a country shrinks for two consecutive quarters (Negative GDP growth) it is called as Recession, which is another important financial term which we will look in brief in our upcoming newsletters.

Key Financial Terms

Import : The process of bringing in products or services from another country.

Export : The act of sending products or services from one's country to another.

How hospitals are leveraging AI to detect Cancer

• Tata Memorial Hospital (TMH), Mumbai, pioneers AI in cancer detection via a 'Bio-Imaging Bank' housing data from 60,000 patients.

• The initiative aims to craft AI algorithms for early-stage cancer detection using radiology and pathology images linked to clinical data.

• The project focuses on head neck and lung cancers, aiming for robust AI algorithms for tasks like screening, segmentation, and therapy prediction.

• Supported by multiple institutions and funded by the Department of Biotechnology, the project signifies a collaborative AI-powered approach in cancer research.

• AI's role in cancer detection replicates human brain processing, aiding in early identification by analysing radiological and pathological images.

• Dr Suyash Kulkarni from TMC elaborates on AI's use in radiology, citing reduced radiation exposure for paediatric patients undergoing CT scans.

• TMH implements AI algorithms to reduce radiation exposure and streamline diagnoses, envisioning AI's transformative potential in cancer treatment.