Complete Machine Learning
in 2022:
Zero to Mastery

4.7 (200 reviews)

Learn to create Machine Learning Algorithms in Python and R from two Data Science
experts. Code templates included.


2,983 enrolled on this course
Machine learning complete course

Duration

4 Months

Assignments

Daily

Courses

Both Online & Offline

Course Fee

See Prices

Overview

CodePlanet Technologies is one of the best Machine Learning Training/Internship institute in Jaipur. We have a team of experienced ML developers and Certified trainers from multinational companies to teach students. CodePlanet Technologies trainers provides highly customized and result oriented training in Machine learning.

We believe that learning technology makes you better, but implementing it makes you perfect. We provide computer labs to students so that they can implement regularly whatever they learn in the class.

In Machine Learning training, our training institute offers practical knowledge and full job assistance with Machine Learning training course.

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What you'll learn

  • Be able to program in Machine learning professionally
  • Create a portfolio of 100 Machine learning projects to apply for developer jobs
  • Be able to use Machine Learning for data science and Artificial intelligence.
  • Build GUIs and Desktop applications with Machine learning
  • Master Machine learning by building 100 projects over 100 days
  • Be able to build fully fledged websites and web apps with Machine Learning
  • Build games like Blackjack, Pong and Snake using Machine Learning
  • Learn to use modern & useful frameworks related to machine learning.

    • Our Instructor

    Chandrapal Singh Deora

    Co-Founder at Code Planet
    Technologies

    Parth Maheshwari

    Co-Founder at Code Planet
    Technologies

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    Course Content

    Introduction
    • Introduction to Machine Learning
    • Why Machine Learning
    • Why Python
    • Categories of ML Algorithms
      • Supervised Learning
      • Unsupervised Learning
      • Reinforcement Learning
      • Evolutionary Learning
    • Getting started with Anaconda Platform
    • Python terminology
    Python Terminology
    • Working with DataFrames in Python
      • Dataset description using DataFrame in Python
      • Loading Dataset into Pandas DataFrame
      • Displaying first few records of the DataFrame
      • Finding Summary of the DataFrame
      • Slicing and indexing of DataFrame
      • Value counts and cross tabulation
      • Sorting DataFrame by Column Values
      • Creating new Columns
      • Grouping and Aggregating
      • joining DataFrames
      • Re-naming columns
      • Applying operations to multiple columns
      • Filtering records based on conditions
      • Removing a column or a row from dataset
    • Exploration of Data using Visualization
      • Drawing Plots
      • Bar Chart
      • Histogram
      • Distribution or Density Plot
      • Box Plot
      • Comparing Distributions
      • Scatter Plot
      • Pair Plot
      • Correlation and Heatmap
    Data Processing
    • Get the dataset
    • Importing the Libraries
    • Importing the Dataset
    • Missing Data
    • Categorical Data
    • Splitting the Dataset into the Training and Test set
    • Feature Scaling
    Regression
    • Introduction
    • Simple Linear Regression
    • Building Simple Linear Regression Model
      • Creating Feature Set and Outcome Variable
      • Splitting the Dataset into Training and Validation Sets
      • Fitting the Model on realtime problem
    • Model Diagnostics
      • Co-efficient of Determination
      • Hypothesis Test for the Regression Co-efficient
      • Regression model summary using Python
      • Residual Analysis
      • Outlier Analysis
      • Making Prediction and Measuring Accuracy
    • Multiple Linear Regression
      • problem description
      • Loading the dataset
      • Categorical Encoding Features
      • splitting the Dataset into train and Validation sets
      • building the models on training dataset
      • Multi-Collinearity and Handling Multi-Collinearity
      • Residual Analysis
      • Making Predictions on the Validation Set
    • Polynomial Regression
    • Support Vector Regression
    • Decision Tree Regression
    • Random Forest Regression
    • Logistic Regression
    Classification
    • Introduction
    • Credit Classification
      • Encoding Categorical Features
      • Splitting Dataset
      • Building Logistic Regression model
      • Model Summary
      • Model Diagnostics
      • Predicting on Test Data
      • Creating a Confusion Matrix
      • Measuring Accuracies
      • Finding Optimal Classification Cut-off
    • Gain Chart and Lift Chart
    • Decision Tree Classification
    • Random Forest Classification
    Advanced Machine Learning
    • Overview
    • Gradient Descent Algorithm
    • K-Fold Cross-Validation
    • Naïve Bayes
    • K-Nearest Neighbors(KNN) Algorithm
    • Ensemble methods
    • Boosting
    Clustering
    • Introduction
    • How Clustering Works?
    • Finding Similarities using Distances
      • Euclidean Distance
      • Other Distance Metrices
    • K-Means Clustering
    • Hierarchical Clustering
    Forecasting
    • Intoduction
    • Components of Time-Series Data
    • Moving Average\n4. Decomposing Time Series
    Deep Learning
    • Introduction
    • Aritificial Neural Networks
      • Intoduction
      • Plan of attack
      • The Neuron
      • Activation Function
      • How do Neural Network Works?
      • How do Neural Network Learn?
      • Gradient Descent
      • BackPropagation
      • getting Dataset
      • Real Time Problem Description
      • implementing model
    • Convolutional Neural Networks
      • Plan of attack
      • Overview
      • Convolution Operation
      • ReLU Layer
      • Pooling
      • Flattening
      • Full Connection
      • Softmax & cross entropy
      • getting Dataset
      • Real Time Problem Description
      • implementing model
    Recommender Systems
    • Introduction
    • Association Rules
    • Collabrative Filtering
    • Matrix Factorization
    Text Analytics
    • Overview
    • Sentiment Classification
      • Loading the Dataset
      • Exploring the Dataset
      • Text Pre-processing
    • Naïve-Bayes Model for Sentiment Classification
      • Split the Dataset
      • Build model
      • Make Prediction on Test case
      • Finding Model Accuracy
    • Natural Language Processing
    • Challenges of Text Analytics