Data Science, Machine Learning & AI Training Course Details

In this topic we will learn primitive types.

• Variables
• Variable Names
• Strings
• Escape Sequences
• Formatted Strings
• String Methods
• Numbers
• Working with Numbers
• Type Conversion

In this module, we will try to understand about the various data structures in python.

• Lists
• Accessing Items
• List Unpacking
• Looping over Lists
• Adding or Removing Items
• Finding Items
• Sorting Lists
• List Comprehensions
• Stacks
• Queues
• Arrays
• Zip Function
• Stacks
• Queues
• Tuples

In this module, we will try to understand how to implement conditions & looping.

• Arithmetic Operators
• Comparison Operators
• Conditional Statements, If-else If-elif-else
• Ternary Operator
• Logical Operators
• Short-circuit Evaluation
• Chaining Comparison Operators
• For Loops, For..Else
• Nested Loops
• Iterators
• While Loops
• Infinite Loops
• Filter Function

In this module, we will understand the functions in Python Data Science!

• What is Function?
• Arguments
• Default Arguments
• *args and **kwargs
• Parameter scope.
• Optional Arguments
• Lambda Functions
• Map Functions
• ZIP Functions

Learning Objective: In this module, we will learn how to handle Exceptions in python.

• Errors in Python
• Compile-Time Errors
• Runtime Errors
• Logical Errors
• What is Exception?
• Handling an exception
• Try …except…else
• try-finally clause
• The argument of an Exception
• Python Standard Exceptions
• Raising an exceptions
• User-Defined Exceptions

Learning Objective: understand OOP's programming concepts.

• Overview of OOP
• The self-variable
• Constructor
• Types Of Variables
• Namespaces
• Creating Classes and Objects
• Inheritance
• Types of Methods
• Instance Methods Static Methods Class Methods
• Accessing attributes
• Built-In Class Attributes
• Destroying Objects
• Abstract classes and Interfaces
• Abstract Methods and Abstract class
• Interface in Python
• Abstract classes and Interfaces

Learning Objective: Understand how to read/write with excel, pdf files.

• File Handling in Python.
• Read/Write Files
• Read/Write Excel Using xlrd
• Read/Write CSV using Pandas
• Excel Files using Pandas
• Read PDF using PyPDF2

Learning Objective: arrange code in modules & packages.

• Import statement
• From import statements
• Reload Module
• Dir function
• What is Packages?
• Intra-package References
• Python Package Index
• The dir Function
• Diff between Package & Module
• Pypi, Pip, Anaconda, Pipenv

Learning Objective: In this module, you will learn various built in modules.

• Dates Module
• Math Module
• Statistics module
• NumPy
• Json Module
• RegEx Module
• Random Module

In this module, understand num py library.

• What is NumPy?
• How to install NumPy?
• Arrays Revisited
• NumPy Arrays
• NumPy Operations
• Broadcasting NumPy Array
• NumPy Mathematics/Statics

In this module, we will learn to utilize Pandas library to Analyse data.

• Introduction
• Read/Write CSV File
• CSV Analysis
• Data Sets , Data Cleaning
• What is Series? Create Series, Series Operation
• What is Data Frames?
• Diff between Series & Data Frames
• Create Data Frames, Data Frames Operations
• Group, Joins, Concat, Shifting, Melt, Stacking, Un Stacking.

In this module, under stand Pivot Tables using pandas in python.

• What is Pivot Tables?
• Why Pivot Tables?
• Create Pivot Table
• Cross Tabs
• Time Series Analysis

In this module, we will try to understand data visualization.

• What is Visualization?
• Why Visualization?
• Introduction To Matplotlib
• Bar Graphs, Histogram, Scatter Plot, Area Plot, Pie Plot.
• Introduction To Pandas Visualization
• Bar Graphs, Histogram, Scatter Plot, Area Plot, Pie Plot using Pandas
• Introduction to Seaborn
• Scatter, Hue, Pie using Seaborn.

In this module, we will try to understand Data Science Process in detail.

• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis
• Univariate Analysis
• Bivariate Analysis
• Visualization of Data

In this module, we will implement a real world case study

In this module, understand how machine learning works.

• Collect Data for Machine Learning
• Apply Cleaning, Data Wangling, Reduce Nulls
• Load Data For ML Projects
• Understand Data with Statistics
• Understand Data with Visualization
• Select Feature Selection
• Implement a Algorithm
• Create & Test Model
• Verify the Accuracy of Model
• Refine Model
• Create a solution for IT.

In this module, we will learn various types of supervised learning.

• What is supervised Learning?
• Introduction to regression
• Simple linear regression
• Multiple linear regression and assumptions in linear regression
• Math behind linear regression

Hands-on Exercise:

• Implementing linear regression from scratch with Python
• Using Python library Scikit-Learn to perform simple linear regression and multiple linear regression
• Implementing train–test split and predicting the values on the test set

In this module, we will try to understand in detail about logistic regression and classification.

• Introduction to classification
• Linear regression vs logistic regression
• Math behind logistic regression
• Sigmoid Function
• Confusion Matrix & Accuracy.
• false positive and true positive.

Hands-on Exercise:

• Implementing logistic regression from scratch with Python
• Using Python library Scikit-Learn to perform simple logistic regression and multiple logistic regression
• Building a confusion matrix to find out accuracy, true positive rate, and false positive rate

In this module, module we want to understand in depth about the Decision Tree and random forest.

• Introduction to tree-based classification
• Understanding a Decision Tree in detail.
• Decision Tree Concepts like impurity function, entropy, information gain.
• Decision Tree Concepts like Gini index, over fitting, Pre-pruning, post-pruning, and cost-complexity Pruning.
• Introduction to Random Forest.
• Random Forest Concepts like -ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest

Hands-on Exercise:

• Implementing a decision tree from scratch in Python
• Using Python library Scikit-Learn to build a decision tree and a random forest
• Visualizing the tree and changing the hyper-parameters in the random forest

In this module, we want to understand Nai ve Bayes theorem and it's implementation using python.

• Introduction to probabilistic classifiers
• Understanding Naïve Bayes
• Math behind the Bayes theorem
• Understanding a support vector machine (SVM).
• Kernel functions in SVM and math behind SVM.

Hands-on Exercise:

• Using Python library Scikit-Learn to build a Naïve Bayes classifier and a support vector classifier

In this module, we will learn unsupervised learning with various methods and types.

• Types of unsupervised learning
• Clustering and dimensionality Reduction
• Introduction to k-means clustering
• Math behind k-means
• Dimensionality reduction with PCA

Hands-on Exercise:

• Using Python library Scikit-Learn to implement k-means clustering
• Implementing PCA (principal component analysis) on top of a dataset

In this module, we will learn in depth about Time Series Analysis.

• What is time series?
• Its techniques and applications
• Time series components
• Moving average, smoothing techniques, and exponential smoothing
• Univariate time series models
• Multivariate time series analysis
• ARIMA model and time series in Python
• Sentiment analysis in Python

Hands-on Exercise:

• Analysing time series data
• The sequence of measurements that follow a non-random order to recognize the nature of the phenomenon
• Forecasting the future values in the series

In this module, understand how AI is feature of industry, education and hospitals.

• How is this era of AI different?
• Transformative Changes
• Role of Machine learning & Deep Learning in AI
• Hardware for AI (CPU vs. GPU vs. TPU)
• Software Frameworks for AI

In this module, we will learn Tensor Flow.

• What is Tensor Flow?
• Tensor Flow code-basics
• Graph Visualization
• Constants, Placeholders, Variables
• Tensorflow Basic Operations
• Linear Regression with Tensor Flow
• Logistic Regression with Tensor Flow
• K Nearest Neighbor algorithm with Tensor Flow
• K-Means classifier with Tensor Flow
• Random Forest classifier with Tensor Flow

In this module, we will try to understand in detail about Tensor Flow.

• Quick recap of Neural Networks
• Activation Functions, hidden layers, hidden units
• Illustrate & Training a Perceptron
• Important Parameters of Perceptron
• Understand limitations of A Single Layer Perceptron
• Illustrate Multi-Layer Perceptron
• Back-propagation – Learning Algorithm
• Understand Back-propagation – Using Neural Network Example
• TensorBoard

In this module, module we want to understand in depth about the Deep Learning Networks.

• What is Deep Learning Networks?
• Why Deep Learning Networks?
• How Deep Learning Works?
• Feature Extraction
• Working of Deep Network
• Training using Backpropagation
• Variants of Gradient Descent
• Types of Deep Networks
• Feed forward neural networks (FNN)
• Convolutional neural networks (CNN)
• Recurrent Neural networks (RNN)
• Generative Adversal Neural Networks (GAN)
• Restrict Boltzman Machine (RBM)

In this module, we want to understand Deep Learning With Keras.

• Define Keras
• How to compose Models in Keras?
• Sequential Composition
• Functional Composition
• Predefined Neural Network Layers
• What is Batch Normalization?
• Saving and Loading a model with Keras
• Customizing the Training Process
• Using TensorBoard with Keras
• Use-Case Implementation with Keras
• Intuitively building networks with Keras

In this module, we will learn Key Applications of AI.

• Computer Vision
• Text Data Processing
• Image processing
• Audio & video Analytics
• Internet of things (IOT)