Complete Data Science BootCamp

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  • Reading time:7 mins read


Learn about Data Science, Machine Learning and Deep Learning and build 5 different projects.

What you will learn

Learn about Libraries like Pandas and Numpy which are heavily used in Data Science.

Build Impactful visualizations and charts using Matplotlib and Seaborn.

Learn about Machine Learning LifeCycle and different ML algorithms and their implementation in sklearn.

Learn about Deep Learning and Neural Networks with TensorFlow and Keras

Build 5 complete projects based on the concepts covered in the course.

Description

Data science is the field that encompasses the various techniques and methods used to extract insights and knowledge from data. Machine learning (ML) and deep learning (DL) are both subsets of data science, and they are often used together to analyze and understand data.

In data science, ML algorithms are often used to build predictive models that can make predictions based on historical data. These models can be used for tasks such as classification, regression, and clustering. ML algorithms include linear regression, decision trees, and k-means.

DL, on the other hand, is a subset of ML that is based on artificial neural networks with multiple layers, which allows the system to learn and improve through experience. DL is particularly well-suited for tasks such as image recognition, speech recognition, and natural language processing. DL algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

In a data science project, DL models are often used in combination with other techniques such as feature engineering, data cleaning, and visualization, to extract insights and knowledge from data. For instance, DL models can be used to automatically extract features from images, and then these features can be used in a traditional ML model.


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In summary, Data science is the field that encompasses various techniques and methods to extract insights and knowledge from data, ML and DL are subsets of data science that are used to analyze and understand data, ML is used to build predictive models and DL is used to model complex patterns and relationships in data. Both ML and DL are often used together in data science projects to extract insights and knowledge from data.

IN THIS COURSE YOU WILL LEARN ABOUT :

  • Life Cycle of a Data Science Project.
  • Python libraries like Pandas and Numpy used extensively in Data Science.
  • Matplotlib and Seaborn for Data Visualization.
  • Data Preprocessing steps like Feature Encoding, Feature Scaling etc…
  • Machine Learning Fundamentals and different algorithms
  • Cloud Computing for Machine Learning
  • Deep Learning
  • 5 projects like Diabetes Prediction, Stock Price Prediction etc…

ALL THEΒ BEST !!!

English
language

Content

Introduction

Introduction

Numpy

Introduction to Numpy
Numpy Arrays
Shape and Reshape
Numpy Array Indexing
Iterating Numpy Arrays
Slicing
Numpy Array Searching and Sorting

Pandas

Pandas Introduction
Series in Pandas
Pandas DataFrame
Read CSV
Analyzing Data Frames in Pandas

Data Visualization

Introduction to Matplotlib
Different type of plots in Matplotlib
Seaborn

Data Preprocessing

Handling Missing Values
Feature Encoding
Feature Scaling

Machine Learning

Introduction to Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Machine Learning Life Cycle
Train Test Split
Regression Analysis
Linear Regression
Logistic Regression
KNN
SVM
Decision Tree
Random Forest
K Means Clustering
Hyper Parameter Optimization with GridSearchCV
Machine Learning Pipeline
Machine Learning Model Evaluation Metrics

Cloud Computing for Machine Learning

Cloud Computing Introduction
Introduction to AWS
Different AWS Services
Introduction to AWS SageMaker
First Machine Learning Practical on AWS SageMaker
Built in ML Algorithms in AWS SageMaker
Linear Learner Algorithm Practical Implementation
No Code ML using AWS SageMaker Canvas
AWS SageMaker MarketPlace

Deep Learning

Artificial Neural Network (ANN)
Activation Functions in Neural Networks
Optimizers in Neural Networks
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)

Projects

Diabetes Prediction
Medical Insurance Cost Prediction
Gold Price Prediction using ANN
Implementation of CNN using keras and tensor flow
Stock Price Prediction using LSTM
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