Machine Learning with Python

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Machine Learning with Python

The First Course in a Series for Mastering Python for Machine Learning Engineers.

4.5 out of 5 based on 875 ratings. 5 user reviews.  Last updated 2/2019

Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The key part of that definition is “without being explicitly programmed.”


  • Some programming experience.
  • Admin permissions to download files.
  • A basic understanding of programming.
  • Desire to learn Python.

Target Audience

Our target audience is especially those people who have great enthusiasm about Machine Learning with Python. We generally accept Software, system and web developers, Machine Learning with Python, College graduates, and Machine Learning enthusiasts. Other than that anyone who has genuine int3erest in Machine Learning with Python development we encourage them too.


Anybody who are beginners without programming knowledge interested in Machine Learning with Python Development can take this Training. A basic knowledge of Machine Learning with Python programming can help.

We have designed several courses for proper knowledge and development of our students in this field. Our courses are listed below:

What you'll learn

  • A way to determine and measure problem complexity
  • Python Programming
  • Statistical Maths for the Algorithms
  • Learning to solve statistics and mathematical concepts
  • Supervised and unsupervised learning
  • Classification and Regression
  • AI Algorithms
  • AI Programming & Use Cases
  • Python Overview: Introduction to Python Programming
    • What is Python
    • Understanding the IDLE
    • Python basics and string manipulation
    • lists, tuples, dictionaries, variables
    • Control Structure – If loop, For loop and while
    • Loop Single line loops
    • Writing user-defined
    • functions Classes
    • File Handling
    • OOPS concept with Classes
  • Data Structure & Data Manipulation in Python
    • Intro to Numpy Arrays
    • Creating Arrays
    • Creating Matrices
    • Creating Vectors
    • Indexing, Data Processing using Arrays
    • Mathematical computing basics
    • Basic statistics
    • File Input and Output
    • Getting Started with Pandas
    • Data Acquisition (Import & Export)
    • Selection and Filtering
    • Combining and Merging Data Frames
    • Removing Duplicates & String Manipulation
  • Understanding the Machine Learning Libraries
    • Numpy
    • Pandas
    • Theano
  • MACHINE LEARNING: Introduction
    • AI & Machine Learning vs. Data Science
    • Discussion on Various ML Learnings
    • Regression vs. Classification
    • Features , Labels , Class
    • Supervised Learning | Unsupervised Learning
    • Real implementation
    • ML Algorithms
    • Cost Function
    • Optimizers
  • Linear Regression
    • Regression Problem Analysis
    • Mathematical modelling of Regression
    • Model Gradient Descent Algorithm
    • Use cases
    • Regression Table
    • Model Specification
    • L1 & L2 Regularization
    • Data sources for Linear regression
  • Math of Linear Regression
    • Linear Regression
    • Math Cost Function
    • Cost Optimizer: Gradient Descent
    • Algorithm Regression R Squared
  • Modelling of Data
    • Variable Significance Identification
    • Model Significance Test
    • Bifurcate Data into Training / Testing Dataset
    • Build Model of Training Data Set
    • Reading and updating Contacts
    • Reading bookmarks
  • Hypothesis
    • Variable and Model Significance
    • Maximum Likelihood Concept
    • Log Odds and Interpretation
    • Null Vs Residual
    • Deviance Chi-Square Test
  • Optimization of threshold value
    • Estimating the Classification Model Hit
    • Ratio Isolating the Classifier for Optimum
    • Results Model Accuracy
    • Model Prediction
    • The Programming Model
    • Data Model, Tensor Board
    • Introducing Feed Forward Neural Nets
    • Softmax Classifier & ReLU Classifier
    • Deep Learning Applications
    • Working with Keras
    • Building Neural Network with keras
    • Examples and use cases
  • Cost Function Optimization
    • Applying Gradient Descent Algorithm
    • Stocha
    • Backpropagation Algorithm & Mathematical Modelling
    • Programming Flow for backpropagation algorithm
  • Use Cases of ANN
    • Programming SLNN using Python
    • Programming MLNN using Python
    • XOR Logic using MLNN & Backpropagation
    • Score Predictor
  • Handling K-Means Clustering
    • Maths behind KMeans Clustering
    • K Means from scratch
    • Mean shift Introduction
    • Dynamically weight
  • Project: Intruder Detection
    • Classification problems & best predictive out of all Case study for various examples

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