Python with Machine Learning is designed to provide in-depth knowledge of building Machine Learning algorithms from bottom up with Python
In recent years, Python has become the most preferred language for Data Science and considered as the powerful and flexible platform for building Machine Learning systems. Python with Machine Learning is designed to provide in-depth knowledge of building Machine Learning algorithms from bottom up with Python. This course provides a solid foundation in Machine Learning with Python as the syllabus is aligned with international market requirements.
This course is an intermediate level course, so most of the aspiring Machine Learning candidates can opt for this course
Learning Objectives: You will get a brief idea of what Python is and touch on the basics.
Topics:
* Overview of Python
* Different Applications where Python is used
* Values, Types, Variables
* The Companies using Python
* Operands and Expressions
* Discuss Python Scripts on UNIX/Windows
* Loops
* Command Line Arguments
* Conditional Statements
* Writing to the screen
* Overview of Python
* Writing to the screen
* practical exposure
* Creating “Hello World” code
* Variables
* Demonstrating Conditional Statements
* Demonstrating Loops
Learning Objectives: Learn different types of sequence structures, related operations, and their usage. Also learn diverse ways of opening, reading, and writing to files.
Topics:
*Python files I/O Functions
* Strings and related operations
* Lists and related operations
* Numbers
* Tuples and related operations
* Sets and related operations
* Dictionaries and related operations
Practical Exposure:
* Tuple - properties, related operations, compared with a list
* List - properties, related operations
* Dictionary - properties, related operations
* Set - properties, related operations
Learning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.
Topics:
* Functions
* Variable Scope and Returning Values
* Function Parameters
* Global Variables
* Object-Oriented Concepts
* Lambda Functions
* Modules Used in Python
* Module Search Path
* Handling Multiple Exceptions
* Package Installation Ways
* Standard Libraries
* Errors and Exception Handling
* The Import Statements
Practical exposure:
* Functions - Syntax, Arguments, Keyword Arguments, Return Values
* Lambda - Features, Syntax, Options, Compared with the Functions
* Sorting - Sequences, Dictionaries, Limitations of Sorting
* Errors and Exceptions - Types of Issues, Remediation
* Packages and Module - Modules, Import Options, sys Path
Learning Objectives: This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.
Topics:
* NumPy - arrays
* Operations on arrays
* Indexing slicing and iterating
* Reading and writing arrays on files
* Pandas - data structures & index operations
* Reading and Writing data from Excel/CSV formats into Pandas
* matplotlib library
* Grids, axes, plots
* Markers, colours, fonts, and styling
* Types of plots - bar graphs, pie charts, histograms
* Contour plots
Practical Exposure:
* NumPy library- Creating NumPy array, operations performed on NumPy array
* Pandas library- Creating series and data frames, Importing and exporting data
* Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot
Learning Objective: Through this Module, you will understand in detail about Data Manipulation
Topics:
* Basic Functionalities of a data object
* Merging of Data objects
* Concatenation of data objects
* Types of Joins on data objects
* Exploring a Dataset
* Analysing a dataset
Practical Explosure:
* Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
* GroupBy operations
* Aggregation
* Concatenation
* Merging
* Joining
Learning Objectives: In this module, you will learn the concept of Machine Learning and its types.
Topics:
* Machine Learning Categories
* Linear regression
* Gradient descent
* What is Machine Learning?
* Machine Learning Process Flow
* Python Revision (numpy, Pandas, scikit learn, matplotlib)
* Machine Learning Use-Cases
Practical Exposure:
* Linear Regression – Boston Dataset
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Topics:
* What are Classification and its use cases?
* Confusion Matrix
* What is Decision Tree?
* Creating a Perfect Decision Tree
* Algorithm for Decision Tree Induction
* What is Random Forest?
Practical Exposure:
* Implementation of Logistic regression
* Decision tree
* Random forest
Learning Objectives: In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.
Topics:
* Why Dimensionality Reduction
* Scaling dimensional model
* Introduction to Dimensionality
* LDA
* PCA
* Factor Analysis
Practical Exposure:
* PCA
* Scaling
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Topics:
* How Naïve Bayes works?
* Illustrate how Support Vector Machine works?
* Hyperparameter Optimization
* What is Naïve Bayes?
* Implementing Naïve Bayes Classifier
* What is Support Vector Machine?
* Grid Search vs Random Search
* Implementation of Support Vector Machine for Classification
Practical Exposure:
* Implementation of Naïve Bayes, SVM
Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
Topics:
* What is Clustering & its Use Cases?
* How to do optimal clustering
* What is K-means Clustering?
* How does K-means algorithm work?
* What is Hierarchical Clustering?
* How Hierarchical Clustering works?
* What is C-means Clustering?
Practical Exposure:
* Unsupervised Learning
* Implementation of Clustering – various types
Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.
Topics:
* Calculating Association Rule Parameters
* Recommendation Engines
* Content-Based Filtering
* How does Recommendation Engines work?
* What are Association Rules?
* Collaborative Filtering
* Content-Based Filtering
Practical Exposure:
* Apriori Algorithm
* Market Basket Analysis
Learning Objectives: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent-environment interaction.
Topics:
* What is Reinforcement Learning
* Why Reinforcement Learning
* Elements of Reinforcement Learning
* Exploration vs Exploitation dilemma
* Epsilon Greedy Algorithm
* Markov Decision Process (MDP)
* Q values and V values
* Q – Learning
* a values
Practical Exposure:
* Calculating Reward
* Discounted Reward
* Calculating Optimal quantities
* Implementing Q Learning
* Setting up an Optimal Action
Learning Objectives: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time.
You will be taught different models for time series modeling such that you analyze a real time-dependent data for forecasting.
Topics:
* What is Time Series Analysis?
* ARMA model
* ARIMA model
* Components of TSA
* White Noise
* Importance of TSA
* Stationarity
* ACF & PACF
* AR model
* MA model
Practical Exposure:
* Checking Stationarity
* Converting a non-stationary data to stationary
* Implementing Dickey-Fuller Test
* Plot ACF and PACF
* Generating the ARIMA plot
* TSA Forecasting
Learning Objectives: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.
Topics:
* The need for Model Selection
* Adaptive Boosting
* What is Boosting?
* What is Model Selection?
* Types of Boosting Algorithms
* Cross-Validation
* How Boosting Algorithms work?
Practical Explosure
* Cross-Validation
* AdaBoost