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Days Course Fee=2200 USD</SPAN><SPAN
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our website </SPAN><A href="http://tracking.datastat.co.ke/tracking/click?d=aie0V4cxK3fInwEh1Hlko2Nh2JoJNovHzQpd_ywcbShL_aziUWCqyDRYrE9TN2XsZweUbjP9qBK0tna2UfCp655zQN5Te_I1ZdFxcQD1AY-AxT2ZplV-g4aS1duuQnUIw9WK0_RGoAjahydF-V-shQQ1"><I><SPAN
style='FONT-SIZE: 18pt; FONT-FAMILY: "Times New Roman",serif; COLOR: blue; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'>www.datastatresearch.org</SPAN></I></A><I><U><SPAN
style='FONT-SIZE: 18pt; FONT-FAMILY: "Times New Roman",serif; COLOR: blue; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></U></I></P><I><U><SPAN
style='FONT-SIZE: 18pt; FONT-FAMILY: "Times New Roman",serif; COLOR: blue; LINE-HEIGHT: 107%; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA'><BR
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<P class=MsoNormal style="MARGIN: 0in 0in 8pt"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'><U>ARTIFICIAL
INTELLIGENCE AND MACHINE LEARNING TRAINING</U></SPAN></B><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></B></P>
<P class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>This
training course aims to provide a comprehensive understanding of the core
concepts, principles, and applications of AI and ML. This training is designed
for participants who have a basic understanding of programming and statistics
but want to dive deeper into AI and machine learning concepts and
applications.</SPAN><SPAN
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lang=EN-GB
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext; mso-ansi-language: EN-GB'>Duration</SPAN></B><SPAN
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lang=EN-GB
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext; mso-ansi-language: EN-GB'>10
days</SPAN><SPAN lang=EN-GB
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<A
href="http://tracking.datastat.co.ke/tracking/click?d=l4EcuOU_mmcO1ouxd742-wlSTdXyDWRT_ZWeOIfj2JzX1Gr6RVcCo5cijUsDlQwpfpOu-5XquUy0unVImgJc2AQMrhrlfE8FEUUsjMGdTbLaARWO1QWsUHn76gN8Ixh6ubhpNf96U0hZOniVASH0yOpJRHmeVUql2rYx62aFzC_6M_Q7DtIU7J0_K2fP3Qr-QjiC9gu8DxEE9hLEaz9MBKG9o5HPTCPhxBGmxV3WgDWtGbR6E42a6LdwdgzlBBYoizvRQz4L8ZAl3K3mDtqJxRdZB7AuXXOHGEYSB_noJSBigpAtRm4k3dPJhwCtIMZwm1A2j_bbURgq_9zJZdcTTiI1"><FONT
size=7 face="Microsoft Sans Serif">HERE</FONT></A></SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Objectives</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l9 level1 lfo1; tab-stops: list .5in"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Understanding
the Fundamentals of AI and ML </SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l9 level1 lfo1; tab-stops: list .5in"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Exploring
Core Concepts and Algorithms</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l9 level1 lfo1; tab-stops: list .5in"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Mastering
Data Preprocessing and Feature Engineering Techniques</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l9 level1 lfo1; tab-stops: list .5in"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Evaluating
and Optimizing Models</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l9 level1 lfo1; tab-stops: list .5in"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Applying
AI and ML in Real-World Scenarios</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l9 level1 lfo1; tab-stops: list .5in"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Addressing
Ethical Concerns in AI</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l9 level1 lfo1; tab-stops: list .5in"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Exploring
Future Trends and Innovations</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l9 level1 lfo1; tab-stops: list .5in"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Identifying
Challenges and Opportunities</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI></UL>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><B><U><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Day
1: Introduction to AI and ML</SPAN></U></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l4 level1 lfo2; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Overview
of AI and ML</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Definitions, history, and applications</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l4 level1 lfo2; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Types
of AI</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Narrow AI, General AI, and Superintelligent AI</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l4 level1 lfo2; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Types
of ML</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Supervised, Unsupervised, and Reinforcement Learning</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l4 level1 lfo2; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Mathematics
for ML</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Linear algebra, calculus, probability, and statistics basics</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l4 level1 lfo2; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Tools
and Environments</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Introduction to Python, Jupyter Notebooks, and libraries like NumPy and
Pandas</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI></UL>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><B><U><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Day
2: Data Preprocessing and Exploration</SPAN></U></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l6 level1 lfo3; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Data
Collection</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Sources and methods</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l6 level1 lfo3; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Data
Cleaning</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Handling missing data, outliers, and normalization</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l6 level1 lfo3; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Data
Visualization</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Using Matplotlib and Seaborn for exploratory data analysis</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l6 level1 lfo3; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Feature
Engineering</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Creating meaningful features from raw data</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l6 level1 lfo3; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Dimensionality
Reduction</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
PCA, t-SNE, and other techniques</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI></UL>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><B><U><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Day
3: Supervised Learning - Regression</SPAN></U></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l0 level1 lfo4; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Introduction
to Supervised Learning</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Concepts and algorithms</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l0 level1 lfo4; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Linear
Regression</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Simple and multiple linear regression</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l0 level1 lfo4; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Evaluation
Metrics</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
MSE, RMSE, and R²</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l0 level1 lfo4; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Polynomial
Regression</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
When and how to use it</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l0 level1 lfo4; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Hands-on
Exercise</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Building and evaluating regression models</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI></UL>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><B><U><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Day
4: Supervised Learning - Classification</SPAN></U></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l1 level1 lfo5; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Introduction
to Classification</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Binary and multiclass classification</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l1 level1 lfo5; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Logistic
Regression</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Understanding and applying it</SPAN><SPAN
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<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Decision
Trees</SPAN></B><SPAN
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Concepts and implementation</SPAN><SPAN
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<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Evaluation
Metrics</SPAN></B><SPAN
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Accuracy, precision, recall, F1-score, ROC-AUC</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Hands-on
Exercise</SPAN></B><SPAN
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Building and evaluating classification models</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI></UL>
<P class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Day
5: Unsupervised Learning - Clustering</SPAN></U></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l8 level1 lfo6; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Introduction
to Unsupervised Learning</SPAN></B><SPAN
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Concepts and importance</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l8 level1 lfo6; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>K-Means
Clustering</SPAN></B><SPAN
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Algorithm, distance metrics, and optimization</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l8 level1 lfo6; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Hierarchical
Clustering</SPAN></B><SPAN
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Concepts and dendrograms</SPAN><SPAN
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<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l8 level1 lfo6; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>DBSCAN</SPAN></B><SPAN
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Density-based clustering</SPAN><SPAN
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<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Hands-on
Exercise</SPAN></B><SPAN
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Applying clustering techniques to datasets</SPAN><SPAN
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<P class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Day
6: Unsupervised Learning - Association and Anomaly Detection</SPAN></U></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l3 level1 lfo7; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Association
Rule Mining</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Apriori algorithm and market basket analysis</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l3 level1 lfo7; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Anomaly
Detection</SPAN></B><SPAN
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Techniques and applications in fraud detection</SPAN><SPAN
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<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l3 level1 lfo7; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Principal
Component Analysis (PCA)</SPAN></B><SPAN
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Dimensionality reduction for unsupervised learning</SPAN><SPAN
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<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Hands-on
Exercise</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Implementing association rules and anomaly detection</SPAN><SPAN
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<P class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Day
7: Neural Networks and Deep Learning - Basics</SPAN></U></B><SPAN
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<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l7 level1 lfo8; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Introduction
to Neural Networks</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Perceptron and multilayer perceptron</SPAN><SPAN
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<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Activation
Functions</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Sigmoid, ReLU, Tanh, etc.</SPAN><SPAN
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<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l7 level1 lfo8; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Backpropagation</SPAN></B><SPAN
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Understanding and implementing</SPAN><SPAN
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<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Introduction
to TensorFlow/Keras</SPAN></B><SPAN
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Setting up and basic operations</SPAN><SPAN
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<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Hands-on
Exercise</SPAN></B><SPAN
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Building a simple neural network</SPAN><SPAN
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<P class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Day
8: Deep Learning - Advanced Topics</SPAN></U></B><SPAN
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<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l10 level1 lfo9; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Convolutional
Neural Networks (CNNs)</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Architecture and applications in image processing</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l10 level1 lfo9; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Recurrent
Neural Networks (RNNs)</SPAN></B><SPAN
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Understanding sequences and time series data</SPAN><SPAN
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<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Transfer
Learning</SPAN></B><SPAN
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Using pre-trained models</SPAN><SPAN
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<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Hands-on
Exercise</SPAN></B><SPAN
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Building and training CNNs and RNNs</SPAN><SPAN
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<P class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Day
9: Reinforcement Learning</SPAN></U></B><SPAN
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<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Introduction
to Reinforcement Learning</SPAN></B><SPAN
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Key concepts and terminology</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Markov
Decision Processes (MDPs)</SPAN></B><SPAN
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Framework for RL</SPAN><SPAN
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<LI class=MsoNormal
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style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Q-Learning
and Deep Q-Networks (DQNs)</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Algorithms and applications</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l5 level1 lfo10; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Hands-on
Exercise</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Implementing a simple reinforcement learning algorithm</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI></UL>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><B><U><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Day
10: AI/ML Applications and Future Trends</SPAN></U></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<UL style="MARGIN-TOP: 0in" type=disc>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l2 level1 lfo11; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Natural
Language Processing (NLP)</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Basics and applications like sentiment analysis</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l2 level1 lfo11; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>AI
Ethics and Fairness</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Understanding bias and ethical considerations in AI</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l2 level1 lfo11; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>AI
in Industry</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Case studies from healthcare, finance, autonomous systems, etc.</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI>
<LI class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt; mso-list: l2 level1 lfo11; tab-stops: list .5in"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Capstone
Project</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>:
Apply the learned concepts to a real-world problem</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></LI></UL>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><B><U><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Training
Methodology</SPAN></U></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>The
training approach comprises many methods to facilitate deep understanding and
practical proficiency in artificial intelligence (AI) and machine learning (ML).
Participants will experience complete immersion in a dynamic learning
environment that includes interactive lectures, hands-on coding laboratories,
real-world case studies, and group discussions. During the training program,
participants have the opportunity to demonstrate their skills and receive
customized feedback through practical projects and assessments. This prepares
them to effectively apply the principles of Artificial Intelligence (AI) and
Machine Learning (ML) in real-world scenarios</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p> </o:p></SPAN></P>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>Key
Notes</SPAN></B><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>i. The
participant must be conversant with English.</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>ii. Upon
completion of training the participant will be issued with an Authorized
Training Certificate</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>iii. Course
duration is flexible and the contents can be modified to fit any number of
days.</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>iv. One-year
post-training support Consultation and Coaching provided after the
course.</SPAN><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt'><o:p></o:p></SPAN></P>
<P class=MsoNormal
style="BACKGROUND: white; TEXT-ALIGN: justify; MARGIN: 0in; LINE-HEIGHT: 18pt"><SPAN
style='FONT-SIZE: 12pt; FONT-FAMILY: "Times New Roman",serif; COLOR: black; mso-fareast-font-family: "Times New Roman"; mso-font-kerning: 0pt; mso-color-alt: windowtext'>v. Payment
should be done at least a week before commence of the training, to DATASTAT
CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare
better for you</SPAN></P>
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us Via:</SPAN></B><SPAN
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