Machine Learning

Categories: AI & ML

Course Content

Unit 1: Fundamentals of Data Science and Artificial Intelligence for Humanities
1. Data Science, Data Analytics, Artificial Intelligence, ML & DL a. Fundamentals of Data Science for humanities b. Fundamentals of Data Analytics for humanities c. Fundamentals of Artificial Intelligence for humanities d. Fundamentals of Machine Learning and Deep Learning for humanities e. Common AI Terminologies 2. Defining Data Science for Humanities a. What is Data Science? b. There are many paths to data science c. What is the cloud? d. Data Science: The job of the 21st century 3. What do data science people do? a. A day in the life of a data science person b. Data Science Tools and Technology 4. Data Science and AI in Different Sectors a. Data Science and AI in Fine Arts b. Data Science in Law Health c. Data Science in Hotel Management d. Data Science in Life Sciences and Bio Technology 5. Fundamentals of Data Science Methodology a. From Problem to Approach b. From Requirements to collection c. From understanding to Preparation d. From modelling to Evaluation e. From deployment to feedback

Session-01: Data Science, Data Analytics, Artificial Intelligence, ML & DL

00:00
Session-02: Defining Data Science for Humanities

00:00
Session-03: What do data science people do?

00:00
Session-04: Data Science and AI in Different Sectors

00:00
Session-05: Fundamentals of Data Science Methodology

00:00
Unit-1 Assignments
Unit-1 Quiz

Unit 2: Fundamentals of Machine Learning and Python
6. Machine Learning Introduction a. Types of Machine learning b. Splitting the dataset into training and test dataset c. Error metrics and validation techniques d. Linear Regression e. Decision Tree Classification f. Visualizing High Dimensional Data using t-SNE over GUI 7. Machine Learning Fundamentals a. Introduction to Machine Learning b. Linear Regression c. Classification d. Regression e. Sampling and Bootstrap f. Model Selection g. Tree Based Models h. Unsupervised Learning i. Classification Metrics 8. Python Foundation for Data Science a. Introduction to Python b. Understanding Operators c. Variables and Data Types d. Conditional Statements e. Looping Constructs f. Functions g. Data Structure h. Lists i. Dictionaries j. Understanding Standard Libraries in Python (Pandas, Numpy, Etc) k. Algorithms (Searching, Sorting, Recursion) l. Object-Oriented Programming m. Exception Handling 9. Mathematical computing using NumPy a. Statistical Features (Box plot, variance, mean, mode, and etc) b. EDA and Data Processing c. Handling missing data d. Handling Categorical data e. Understand relational data and methods for joining data frames. f. Reshape data using the NumPy package. h. Feature transformation: Scaling, normalization, etc

Unit 3: Exploratory Data Analysis (EDA) using Python
10. Data Manipulation with Pandas a. Introduction b. Finding Datasets c. Pandas vs Numpy d. Creating Dataframe e. Saving and Serialising f. Inspecting Dataframe g. Visualizing 1D distributions h. Visualizing 2D distributions i. Higher Dimension Visualizations j. Slicing and Filtering k. Replacing and Thresholding l. Removing and Adding Data m. Grouping n. Merging o. Time Series Data – Reindexing, Resampling, Rolling Functions 11. Data Visualization with Python a. Deal with missing data. b. Introduction to Data Visualization c. Line Chart, Scatterplots, Box Plots, Violin Plots d. Histograms, Heat maps and Clustered Matrices e. Correlation f. Visualization using Excel and Tableau 12. Machine Learning Implementation Steps a. ML Strategy b. Single Number Evaluation Metric c. Train/Dev/Test Distributions d. Improving Model Performance e. Error Analysis f. Training and Testing on Different Distributions 13. Data Collection for Machine Learning

Unit 4: Supervised Learning vs Unsupervised Learning
14. Concept of Supervised and Unsupervised ML 15. Practical Implementation of Supervised ML Algorithm a. Model Evaluation and Data Splitting b. Introduction to Parametric Models and Linear Regression c. Implementing Linear Regression d. Implementing Logistic Regression e. The Bias Variance Trade Off f. Implementing Decision Trees g. Implementing K-Nearest Neighbor 16. Practical Implementation of Unsupervised ML Algorithm a. Market Basket Analysis b. Curse of Dimensionality c. Approaches to Dimensionality Reduction d. PCA e. Implementing Clustering 17. Data Preparation for ML using Data a. Ability to perform EDA using SQL b. Ability to perform EDA using python functions c. Ability to perform EDA using Excel d. Ability to draw hypothesis by analysing EDA outcomes 18. Feature Selection for ML a. Ability to create time-based features b. Ability to create relationship-based features c. Ability to create frequency-based features d. Ability to create features using algorithms e. Ability to identify important features

Unit 5: Fundamentals of Deep Learning and Neural Network
19. Introduction to Deep Learning a. What is Deep Learning and how it is different from Machine Learning b. Artificial Neural (Perceptron) c. Train a perceptron d. Backpropagation Algorithm e. Regression using Neural Networks 20. Introduction to Neural Networks a. What is a neural network b. Binary Classification c. Logistics Regression d. Logistics Regression Cost Function e. Gradient Descent 21. ANN with Tensorflow a. Forward Propagation b. Activation Functions c. Multiclass Classification d. ANN for Image Classification e. ANN for Regression

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