Machine Learning for Quant Finance and Algorithmic Trading

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Free Download Machine Learning for Quant Finance and Algorithmic Trading
Published: 12/2024
Created by: Raj Chhabria
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 98 Lectures ( 11h 14m ) | Size: 4.37 GB

Master Machine Learning and Python for Quantitative Finance and Learn to Build and Backtest Algo Trading Strategies.
What you'll learn
Learn about complete life cycle of a Machine Learning Project from Data Processing to Building ML models to Deployment on WebApps built using Streamlit.
You will learn about Complex Financial Market concepts like Derivatives, Asset pricing models, Technical Analysis, etc... in simple terms without any jargons.
This course covers essentials of Machine Learning and Deep Learning that will help to get an edge in your Quant Analysis of Financial Data.
Learn to build your own Trading Strategies using Machine Learning and Backtest them using Python.
You will learn how to quickly build your own Web Apps and Dashboards for your Quant Analysis using Streamlit.
This course also has lots of Hands on Coding Projects in Python, Machine Learning, Deep Learning and Streamlit.
Requirements
Basic understanding of Python Programming Language. Prior knowledge Financial Markets is not necessary.
Description
--- WELCOME TO THE COURSE ---This comprehensive course is designed for anyone who wants to leverage machine learning techniques in finance. Covering essential topics such as Pandas, NumPy, Matplotlib, and Seaborn, participants will gain a solid foundation in data manipulation and visualization, crucial for analyzing financial datasets.The curriculum delves into key financial concepts, including derivatives, technical analysis, and asset pricing models, providing learners with the necessary context to apply machine learning effectively. Participants will explore various machine learning methodologies, including supervised and unsupervised learning, deep learning techniques, and their applications in developing trading strategies.A significant focus of the course is on hands-on coding projects that allow learners to implement machine learning algorithms for trading strategies and backtesting. By the end of the course, students will have practical experience in building predictive models using Python.Additionally, the course introduces Streamlit, enabling participants to create interactive web applications and dashboards to showcase their quantitative models effectively. This integration of machine learning with web development equips learners with the skills to present their findings dynamically.Whether you are a finance professional or a data enthusiast, this course empowers you to harness the power of machine learning in quantitative finance and algorithmic trading, preparing you for real-world challenges in the financial markets. Join us to transform your understanding of finance through advanced analytics and innovative technology!
Who this course is for
Someone with very basic understanding of Python and who wants to get into Quant Finance and learn about Algorithmic trading by leveraging the power of Machine Learning.
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4.37 GB | 14min 27s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

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1 -Introduction to the Course and Key Learning outcomes.mp4 (97.91 MB)
2 -Introduction to Machine Learning for Quant Finance.mp4 (86.49 MB)
1 -Introduction to Pandas.mp4 (26.11 MB)
10 -Array Iterating.mp4 (25.78 MB)
11 -Slicing Arrays.mp4 (43.16 MB)
12 -Searching and Sorting Numpy Arrays.mp4 (32.64 MB)
2 -Pandas Series.mp4 (17.96 MB)
3 -DataFrame in Pandas.mp4 (26.55 MB)
4 -How to work with CSV and other file types in pandas.mp4 (18.71 MB)
5 -Analysing DataFrames in Pandas.mp4 (48.42 MB)
6 -Introduction to Numpy.mp4 (35.95 MB)
7 -Numpy Arrays.mp4 (53.02 MB)
8 -Shape and Reshape Arrays in Numpy.mp4 (50.05 MB)
9 -Indexing Arrays.mp4 (35.79 MB)
1 -Introduction to Matplotlib.mp4 (29.5 MB)
2 -Different Plotting Examples in Matplotlib.mp4 (47.16 MB)
3 -Introduction to Seaborn.mp4 (63.92 MB)
4 -Handling Missing Values in Data.mp4 (64.52 MB)
5 -Feature Scaling.mp4 (56.89 MB)
6 -Feature Encoding.mp4 (67.71 MB)
1 -Supervised Machine Learning.mp4 (88.51 MB)
10 -KNN.mp4 (79.11 MB)
11 -SVM.mp4 (63.76 MB)
12 -Decision Tree.mp4 (73.82 MB)
13 -Random Forest.mp4 (48.38 MB)
14 -K-Means Clustering.mp4 (66.07 MB)
15 -GridSearch CV.mp4 (74.01 MB)
16 -Machine Learning Pipeline.mp4 (56.84 MB)
17 -Regression Practical Coding Example Mini Project.mp4 (84.18 MB)
18 -Classification Practical Coding Example Mini Project.mp4 (80.23 MB)
2 -Unsupervised Machine Learning.mp4 (81.93 MB)
3 -Machine Learning Lifecycle.mp4 (29.66 MB)
4 -Train Test Split.mp4 (14.58 MB)
5 -Machine Learning Model Evaluation Metrics.mp4 (45.23 MB)
6 -Dimensionality in Machine Learning.mp4 (33.72 MB)
7 -Regression Analysis.mp4 (55.45 MB)
8 -Linear Regression.mp4 (52.24 MB)
9 -Logistic Regression.mp4 (74.78 MB)
1 -Artificial Neural Networks (ANN).mp4 (44.41 MB)
2 -Activation Functions.mp4 (47.28 MB)
3 -Optimizers in Neural Networks.mp4 (49.61 MB)
4 -Convolutional Neural Networks (CNN).mp4 (61.07 MB)
5 -Recurrent Neural Networks (RNN).mp4 (40.5 MB)
1 -Introduction to Financial Markets.mp4 (54.3 MB)
10 -Sharpe Ratio.mp4 (51.95 MB)
11 -Pair Trading.mp4 (35.67 MB)
12 -Arbitrage Trading.mp4 (28.81 MB)
13 -Introduction to Financial Derivatives.mp4 (48.93 MB)
14 -Futures (Financial Derivatives).mp4 (63.83 MB)
15 -Options (Financial Derivatives).mp4 (86.85 MB)
16 -Black Scholes Option Pricing Model.mp4 (30.7 MB)
17 -Introduction to Technical Analysis.mp4 (62.29 MB)
18 -CandleStick Chart Python Code Implementation.mp4 (35.86 MB)
19 -Support and Resistance.mp4 (71.19 MB)
2 -Introduction to Financial Markets Part 2.mp4 (37.91 MB)
20 -Moving Average.mp4 (58.8 MB)
21 -Simple Moving Average(SMA) Python Code Implementation.mp4 (43.92 MB)
22 -Exponential Moving Average(EMA) Python Code Implementation.mp4 (26.92 MB)
23 -Chart Patterns.mp4 (130.73 MB)
24 -Dow Theory.mp4 (37.99 MB)
25 -RSI.mp4 (49.72 MB)
3 -Time Value of Money.mp4 (35.95 MB)
4 -Type of Analysis in Financial Markets.mp4 (37.25 MB)
5 -Capital Asset Pricing Model (CAPM).mp4 (41.21 MB)
6 -Modern Portfolio Theory(MPT).mp4 (60.68 MB)
7 -Correlation Theory.mp4 (37.9 MB)
8 -Correlation Python Code Practical.mp4 (37.52 MB)
9 -Kelly Criterion.mp4 (33.94 MB)
1 -Working with OHLC Data for Stocks in Python.mp4 (72.62 MB)
2 -Apple Stock Price Prediction using Linear Regression.mp4 (70.02 MB)
3 -Gold Price Prediction using Machine Learning.mp4 (63.07 MB)
4 -Tesla Stock Price Prediction with different ML models.mp4 (116.92 MB)
5 -Trading Strategy Development and Backtesting in Python on Amazon Stock.mp4 (133.3 MB)
6 -Stock Price Prediction using LSTM Neural Network.mp4 (57.55 MB)
1 -Streamlit Installation.mp4 (6.24 MB)
10 -Buttons Streamlit.mp4 (14.13 MB)
11 -Radio Button Streamlit.mp4 (15.49 MB)
12 -Text input in Streamlit.mp4 (13.34 MB)
13 -Number Input Streamlit.mp4 (8.39 MB)
14 -Download Button in Streamlit.mp4 (45.05 MB)
15 -Checkbox input in Streamlit.mp4 (9.13 MB)
16 -Selectbox input in Streamlit.mp4 (11.18 MB)
17 -Date Input Streamlit.mp4 (11.09 MB)
18 -Slider Input Widget in Streamlit.mp4 (41.62 MB)
19 -Working with Images in Streamlit.mp4 (10.65 MB)
2 -Text Elements in Streamlit.mp4 (18.03 MB)
20 -Working with Videos in Streamlit.mp4 (15.69 MB)
21 -Sidebar Layout in Streamlit.mp4 (17 MB)
22 -Column layout in Streamlit.mp4 (18.7 MB)
23 -Expander Layout in Streamlit.mp4 (15.23 MB)
3 -DataFrames in Streamlit.mp4 (19.72 MB)
4 -Tables in Streamlit.mp4 (11.86 MB)
5 -JSON in Streamlit.mp4 (9.15 MB)
6 -Line Chart.mp4 (13.29 MB)
7 -Area Chart.mp4 (13.25 MB)
8 -Bar Chart.mp4 (12.03 MB)
9 -Pyplot in Streamlit.mp4 (10.44 MB)
1 -Project - Stock Market Index Prediction Streamlit Webapp.mp4 (81 MB)

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