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Python with Machine Learning

 

Please Note: All below course content will be covered in practical scenarios and regular assignments will be shared. All sessions will be recorded and shared with student for future reference (free of cost). Along with below course.

Python Fundamental

  1. IDE, Basic Operations and Data Structures
  2. Standard Libraries
  3. Loop structure
  4. Input / Output
  5. Creating User Defined Functions
  6. Object Oriented Programing
  7. File Management

Numpy Basics / Advance

  1. Functions and
  2. oop in Numpy,
  3. arrays
  4. ndarray data structure
  5. Chart Plotting,
  6. matplotlib

Pandas

  1. Introduction to Pandas
  2. Dataframe
  3. Reading and writing data to csv,database
  4. merging and joining of data sets
  5. Data Manupulation

Introduction to Machine Learning

  1. What is Machine Learning
  2. What Machine Learning is NOT
  3. AI vs ML vs Deep learning
  4. Types of Machine Learning
  5. Supervised Learning
  6. Classification and Regression Example
  7. Unsupervised Learning
  8. Semi-Supervised Learning
  9. Common Machine Learning Problems
  10. Regression
  11. Clustering
  12. Supervised vs Unsupervised- with example

Linear Regression:

  1. Understand the intuition behind Linear Regression
  2. Understand the Linear Regression Cost Function
  3. Understand the Linear Regression using Gradient Descent Algorithm
  4. Introduction to Linear Regression in sklearn
  5. Learn about the assumptions in Linear Regression Algorithm
  6. Evaluating Metrics for Regression

Feature Selection

  1. Apply Univariate feature selection techniques
  2. Apply Bivariate feature selection techniques
  3. Understand correlation and causation
  4. Learn how to use sklearn for feature selection
  5. Sklearn for Feature Selection
  6. Filter Methods
  7. Wrapper Methods

Logistic Regression:

  1. Understand and apply the Logistic Regression algorithm
  2. Understand Gradient Descent in Logistic Regression
  3. Enter Logistic Regression
  4. What is the Sigmoid function

Decision Trees:

  1. Understand why to use a Decision Tree
  2. Learn how to make a Decision Tree
  3. Learn about the challenges with Decision Tree
  4. Learn how to deal with Over-fitting

Random Forest

  1. Understand the short-comings of decision trees
  2. Understand Random Forest
  3. Understand the Random Forest hyperparameters
  4. shortcomings of decision trees

Clustering/ k-means

  1. Understand Clustering
  2. Understand the reasons for doing Clustering
  3. Understand k-means clustering and its shortcoming
  4. Understand Hierarchical clustering and its shortcoming
  5. Learn to implement clustering using sklearn

Time series:

  1. What is Time-Series ?
  2. What is Time-Series Forecasting and Time-Series Analysis?
  3. Predictive vs Descriptive Analytics
  4. Factors to consider while forecasting.
  5. Different kinds of Forecasting
  6. Forecast Horizons and Data Collection Methods
  7. Types of Forecasting
  8. Visualization of Time-Series Data

Un-supervised ML

  1. Concepts of Unsupervised learnings
  2. Implementation of Unsupervised learnings.
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Students Testimonials

  • Seeing the demand of Python in programming, I decided to enroll for weekend classes of Python then after joining, Ankit sir took our class and from the beginning we attended all the classes..

    gaurav saini

  • Ankit sir is one of the best mentors I have ever had and he's supportive also . He's is the professional in every branch he know and....

    Anshul

  • Ankit sir is the best trainer and have best knowledge in coding he is the best teacher and also supports the idea help to develop them

    rishabh jain
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