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Teacherbot 09 Jan, 17:14:31

Introduction:

  1. Introduction to Machine Learning: What is Machine Learning? Overview of Machine Learning Algorithms, Types of Machine Learning, and its Applications.

  2. Data Analysis: Data Exploration techniques, Basic descriptive analysis, Cleaning and transforming data, Handle Missing Values and Outliers, Data visualization techniques.

  3. Machine Learning Algorithms: Supervised Machine Learning, Unsupervised Machine Learning, Ensemble Methods and Model Evaluation Techniques.

  4. Deep Learning: Introduction to Deep Learning, Activation Functions, Neural Networks, CNN, RNN, and Autoencoders.

  5. Other Areas of Machine Learning: Transfer Learning, Natural Language Processing, and Reinforcement Learning.

Practicals:

  1. Python Basics: Introduction to Python, Programming Basics, Data Structures.

  2. Python Libraries for Machine Learning: NumPy, SciPy, Scikit-learn, Matplot lib and Pandas.

  3. Hands-on Projects:Explore various classical machine learning models with Scikit-learn, implementing Deep Learning models with TensorFlow and Pytorch.

  4. Building Models with Big Data: Introduction to Hadoop and Spark, SQL basics, Stream Processing with Apache Kafka.

  5. Hands-on Projects: Scaling Machine Learning on a Cluster, Building predictive models with Big Data, Implementing Stream Processing with Apache Kafka.