Complete Data Science BootCamp

Complete Data Science BootCamp, available at $79.99, has an average rating of 4.17, with 54 lectures, based on 397 reviews, and has 39604 subscribers.

You will learn about 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. This course is ideal for individuals who are People who want to start their Data Science Journey in Python. or Someone who is looking for a complete course that covers all important topics of Data Science, Machine Learning and Deep Learning. It is particularly useful for People who want to start their Data Science Journey in Python. or Someone who is looking for a complete course that covers all important topics of Data Science, Machine Learning and Deep Learning.

Enroll now: Complete Data Science BootCamp

Summary

Title: Complete Data Science BootCamp

Price: $79.99

Average Rating: 4.17

Number of Lectures: 54

Number of Published Lectures: 54

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 54

Original Price: ?3,099

Quality Status: approved

Status: Live

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.

Who Should Attend

People who want to start their Data Science Journey in Python.

Someone who is looking for a complete course that covers all important topics of Data Science, Machine Learning and Deep Learning.

Target Audiences

People who want to start their Data Science Journey in Python.

Someone who is looking for a complete course that covers all important topics of Data Science, Machine Learning and Deep Learning.

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.

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 THE BEST !!!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Numpy

Lecture 1: Introduction to Numpy

Lecture 2: Numpy Arrays

Lecture 3: Shape and Reshape

Lecture 4: Numpy Array Indexing

Lecture 5: Iterating Numpy Arrays

Lecture 6: Slicing

Lecture 7: Numpy Array Searching and Sorting

Chapter 3: Pandas

Lecture 1: Pandas Introduction

Lecture 2: Series in Pandas

Lecture 3: Pandas DataFrame

Lecture 4: Read CSV

Lecture 5: Analyzing Data Frames in Pandas

Chapter 4: Data Visualization

Lecture 1: Introduction to Matplotlib

Lecture 2: Different type of plots in Matplotlib

Lecture 3: Seaborn

Chapter 5: Data Preprocessing

Lecture 1: Handling Missing Values

Lecture 2: Feature Encoding

Lecture 3: Feature Scaling

Chapter 6: Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: Supervised Machine Learning

Lecture 3: Unsupervised Machine Learning

Lecture 4: Machine Learning Life Cycle

Lecture 5: Train Test Split

Lecture 6: Regression Analysis

Lecture 7: Linear Regression

Lecture 8: Logistic Regression

Lecture 9: KNN

Lecture 10: SVM

Lecture 11: Decision Tree

Lecture 12: Random Forest

Lecture 13: K Means Clustering

Lecture 14: Hyper Parameter Optimization with GridSearchCV

Lecture 15: Machine Learning Pipeline

Lecture 16: Machine Learning Model Evaluation Metrics

Chapter 7: Cloud Computing for Machine Learning

Lecture 1: Cloud Computing Introduction

Lecture 2: Introduction to AWS

Lecture 3: Different AWS Services

Lecture 4: Introduction to AWS SageMaker

Lecture 5: First Machine Learning Practical on AWS SageMaker

Lecture 6: Built in ML Algorithms in AWS SageMaker

Lecture 7: Linear Learner Algorithm Practical Implementation

Lecture 8: No Code ML using AWS SageMaker Canvas

Lecture 9: AWS SageMaker MarketPlace

Chapter 8: Deep Learning

Lecture 1: Artificial Neural Network (ANN)

Lecture 2: Activation Functions in Neural Networks

Lecture 3: Optimizers in Neural Networks

Lecture 4: Convolutional Neural Network (CNN)

Lecture 5: Recurrent Neural Network (RNN)

Chapter 9: Projects

Lecture 1: Diabetes Prediction

Lecture 2: Medical Insurance Cost Prediction

Lecture 3: Gold Price Prediction using ANN

Lecture 4: Implementation of CNN using keras and tensor flow

Lecture 5: Stock Price Prediction using LSTM

Instructors

Raj Chhabria
Computer Science Engineer with Specialization in DataScience

Rating Distribution

1 stars: 9 votes

2 stars: 10 votes

3 stars: 48 votes

4 stars: 158 votes

5 stars: 172 votes

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