Math:

  1. Linear Algebra:

    • Understanding matrix multiplication, vectors, dot product.
    • Knowledge of matrix transformation, inverses, identity matrices, and diagonal matrices.

  2. Calculus:

    • Familiarity with differentiation, including chain rule and derivative rules.
    • Understanding of integration theory, such as basic integration concepts.

  3. Discrete Mathematics:

    • Knowledge of graph theory and combinations.
    • Understanding complexity and Big O notation for analyzing algorithm efficiency.

  4. Basic Mathematical Operations:

    • Proficiency in basic arithmetic operations like multiplication, division, and exponentiation.
    • Understanding of logarithms, exponents, and key mathematical symbols.

  5. Probability and Statistics:

    • Comprehension of descriptive statistics, including mean, median, standard deviation, and variance.
    • Foundation in inferential statistics, covering concepts like central limit theorem, hypothesis testing, and confidence intervals.
    • Understanding probability distributions like normal, binomial, uniform, and exponential distributions.

  6. Bayesian Thinking:

    • Knowledge of Bayes' theorem, conditional probability, hypothesis tests, and applications in machine learning.

These mathematical skills are crucial for a solid foundation in machine learning as they form the basis for understanding algorithms, analyzing data, and developing predictive models.

Statistics:

  1. Descriptive Statistics: Understanding concepts such as mean, median, standard deviation, variance, and data analysis using these measures.

  2. Inferential Statistics: Familiarity with central limit theorem, law of large numbers, hypothesis testing, and confidence intervals.

  3. Probability Distributions: Knowledge of various probability distributions, such as the normal, binomial, and exponential distributions, and their applications.

  4. Bayesian Thinking: Understanding Bayes' theorem, conditional probability, and its relevance to statistical analysis.

Natural Language Processing (NLP):

  1. Text Data Processing: Cleaning and preprocessing text data, including techniques like lowercasing, punctuation removal, tokenization, stemming, and lemmatization.

  2. Embeddings and Algorithms: Understanding word embeddings, sub-word embeddings, character embeddings, as well as algorithms like TF-IDF and word embeddings.

  3. NLP Applications: Awareness of applications such as chatbots, sentiment analysis, and document summarization using NLP techniques.

Other:

  1. Python Programming: Proficiency in Python, including libraries such as pandas, numpy, scikit-learn, TensorFlow, and PyTorch, and their application in machine learning.

  2. Data Science Fundamentals: Understanding of fundamental concepts in data science, including data visualization, feature engineering, and model training and testing.

  3. Deep Learning and Generative AI: Knowledge of deep learning concepts, neural network architectures, such as RNNs, LSTMs, CNNs, and generative AI models like GPTs, Transformers, and VAEs.

LECTURES:

Lecture 1_Index

Lecture 2_Index

Lecture 3_Index