A Level: Machine Learning Using Python (A10.5-R5, NIELIT / DOEACC)



    The course would cover the spectrum of data analytics, machine learning, deep learning, natural language processing, and computer vision. The student would dive straight into data analytics and applied machine learning and deep learning algorithms.

    At the end of the course, the students will be able to

    • Solve real-world problems through machine learning implementation leading to predictions.
    • Understand various learning models, methods, and applications under supervised and unsupervised learning.
    • Use NLTK Library which helps in text analytics.

    120 Hours – (Theory: 48 hrs + Practical: 72 hrs)

    Detailed Syllabus

    (i) Advanced Python
    Overview of Python language, Programming Constructs, Data Structures like lists, dictionaries, tuples, sequences, and their manipulations. Python Functions. Modules and Packages, Exception Handling, NumPy Library, Broadcasting, and NumPy functions. Pandas Library, working with data frames, loading CSV, manipulating data frames, Aggregation functions, Analysis. Visualization using matplotlib and Seaborn.

    (ii)Machine Learning
    Categories of ML, Supervised, Unsupervised, Reinforcement, Semi-Supervised. Supervised Learning Models, Regression, Classification, Naive Bayes, Support Vector Machines, Decision Trees, K-nearest Neighbours, Ensemble Methods of Classification, Machine Learning Evaluation Metrics, Cross-Validation.

    (iii) Computer Vision
    Introduction to Computer Vision, Face Recognition, and Detection with OpenCV, Face Recognisers, Training data, Prediction.

    (iv) Deep Learning
    Artificial Neural Networks and Model, ANN structure, Feed Forward Neural network, Back Propagation, Deep Learning Concepts, Convolution Neural Network (CNN), Neural Network using TensorFlow. Learning Algorithms, Error correction, and Gradient Descent Rules, Perceptron Learning Algorithm.

    (v) Natural Language Processing
    Basics of text processing, Lexical processing, NLP tasks in syntax, semantics, and pragmatics. Applications like Automatic Summarization, Sentiment Analysis, and Text Classification.