Machine Learning and Python Integrated Curriculum
Intro to python- (1,2)
Installing, versions, interpreter, jupyter notebook, pycharm, indentation, virtual env
Using interpreter, writing scripts, passing arguments
Data types- Number(int, float, complex), Strings - (1,2)
Control flows- if, if-else, for, range, while, break, pass, continue, else on loops.- (1,2)
Boolean - and, or, not -(1)
Function- Defining functions, arguments, keyword args, arg list, default args, unpacking args
- (2,2)
Lambda - defining and usage of lambda - (1,1)
• String- (1,1)
String manuplation
String matching, regex - (string, re)
• More Data types- (1,2)
datetime
collections
array
copy(shallow and deep)
enum
• Data Structures and types - (4,4)
• List, Tuple, Set, Dict, Stack , Queue
• List comprehension, list and lambda
• Sequence (List, tuple, range)
• Set type - (set, fozenset)
• Mapping type - (dict)
• Modules- defining and usage - (1)
• Packages- import, import *, intra-package reference - (1)
• Input-output - (2,3)
• String format
• Reading, writing files, os moudle intro
• File handleing- os.path,fileinput, glob
• Errors and exception- (1,1)
• syntax error
• Exception- raising, handling, user defined exception
• Exception heirarchy
• Classes- (2,3)
• Classes and Objects
• Scope and namespace
• Instance variable, method, self
• Class and instance variable
• Inheritance and composition
• Private variables
• Iterator and Generators - (1,1)
• itertools, functools, operator

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Machine Learning

• Introudctio to ml - (3)
• training phase
• testing and evaluation phase
• Data - features, Dimension, training data, testing data, validation data
• Data types- Categorical, ordinal, Numeric, Text
• Labled and Unlabled data
• Models-
• Logical Models (Decistion tree)
• Geometric Models (linear regression)
• Probablistic models(SVM)
• Data Inconsistencies-
• Under fitting
• Over fitting
• Missing values
• Outliers
• Types of learning-(3)
• Classification
• Regression
• Clustering
• Supervised leaning
• Unsupervised learning
• Reinforcement learning
• Deep learning

• Performance measures in ml- (3)
• Mean Square Error
• Mean absolute error
• Normalized MSE
• Bias and variance
• Data manipulation and analysis- (9,9)
• Numpy-
• Pandas-
• Matplotlib and Seaborn

• Machine learning algos (using scikit-learn) - (9,9)
• Parametric vs Non parametric algos
• Linear regeression,
• Optimizers- Gradient descent
• Regularization- L1, L2
• Hyper parameter tuning
• Decistion Tree (CART)
• Naive Bayes classifier
• Deep learning- (tensorflow, Keras)-(9,9)
• Neural Network
• Convolution 1D, 2D
• HandWritten digit recognition
• spam detection
Hardware Requirements- (DIT responsibility)
i3-i5 processor
more then 4 Gb RAM
OS- windows/ ubuntu
Software Requirements- (TPC responsibility)
Python3
jupyter notebook/pycharm
Mini Projects like :-
(1)House Price Pridiction
(2)Irise Datasets
(3)Credit Card Prediction
Major Projects like :-
(1)Autonomas Driving
(2)Cencer Pridiction
(3)Working on Kaggle Datasets.

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